{"id":1817,"date":"2023-06-20T16:38:32","date_gmt":"2023-06-20T16:38:32","guid":{"rendered":"https:\/\/blog.examboosts.com\/?p=1817"},"modified":"2023-06-20T16:38:32","modified_gmt":"2023-06-20T16:38:32","slug":"q25-q48-verified-professional-data-engineer-dumps-qas-pass-guarantee-exam-dumps-test-engine-2023","status":"publish","type":"post","link":"https:\/\/blog.examboosts.com\/ko\/2023\/06\/q25-q48-verified-professional-data-engineer-dumps-qas-pass-guarantee-exam-dumps-test-engine-2023\/","title":{"rendered":"[Q25-Q48] Verified Professional-Data-Engineer dumps Q&amp;As &#8211; Pass Guarantee Exam Dumps Test Engine [2023]"},"content":{"rendered":"\n\n<div class=\"kk-star-ratings kksr-auto kksr-align-left kksr-valign-top\"\n    data-payload='{&quot;align&quot;:&quot;left&quot;,&quot;id&quot;:&quot;1817&quot;,&quot;slug&quot;:&quot;default&quot;,&quot;valign&quot;:&quot;top&quot;,&quot;ignore&quot;:&quot;&quot;,&quot;reference&quot;:&quot;auto&quot;,&quot;class&quot;:&quot;&quot;,&quot;count&quot;:&quot;0&quot;,&quot;legendonly&quot;:&quot;&quot;,&quot;readonly&quot;:&quot;&quot;,&quot;score&quot;:&quot;0&quot;,&quot;starsonly&quot;:&quot;&quot;,&quot;best&quot;:&quot;5&quot;,&quot;gap&quot;:&quot;5&quot;,&quot;greet&quot;:&quot;Rate this post&quot;,&quot;legend&quot;:&quot;0\\\/5 - (0 votes)&quot;,&quot;size&quot;:&quot;24&quot;,&quot;title&quot;:&quot;[Q25-Q48] Verified Professional-Data-Engineer dumps Q\\u0026amp;As - Pass Guarantee Exam Dumps Test Engine [2023]&quot;,&quot;width&quot;:&quot;0&quot;,&quot;_legend&quot;:&quot;{score}\\\/{best} - ({count} {votes})&quot;,&quot;font_factor&quot;:&quot;1.25&quot;}'>\n            \n<div class=\"kksr-stars\">\n    \n<div class=\"kksr-stars-inactive\">\n            <div class=\"kksr-star\" data-star=\"1\" style=\"padding-right: 5px\">\n            \n\n<div class=\"kksr-icon\" style=\"width: 24px; height: 24px;\"><\/div>\n        <\/div>\n            <div class=\"kksr-star\" data-star=\"2\" style=\"padding-right: 5px\">\n            \n\n<div class=\"kksr-icon\" style=\"width: 24px; height: 24px;\"><\/div>\n        <\/div>\n            <div class=\"kksr-star\" data-star=\"3\" style=\"padding-right: 5px\">\n            \n\n<div class=\"kksr-icon\" style=\"width: 24px; height: 24px;\"><\/div>\n        <\/div>\n            <div class=\"kksr-star\" data-star=\"4\" style=\"padding-right: 5px\">\n            \n\n<div class=\"kksr-icon\" style=\"width: 24px; height: 24px;\"><\/div>\n        <\/div>\n            <div class=\"kksr-star\" data-star=\"5\" style=\"padding-right: 5px\">\n            \n\n<div class=\"kksr-icon\" style=\"width: 24px; height: 24px;\"><\/div>\n        <\/div>\n    <\/div>\n    \n<div class=\"kksr-stars-active\" style=\"width: 0px;\">\n            <div class=\"kksr-star\" style=\"padding-right: 5px\">\n            \n\n<div class=\"kksr-icon\" style=\"width: 24px; height: 24px;\"><\/div>\n        <\/div>\n            <div class=\"kksr-star\" style=\"padding-right: 5px\">\n            \n\n<div class=\"kksr-icon\" style=\"width: 24px; height: 24px;\"><\/div>\n        <\/div>\n            <div class=\"kksr-star\" style=\"padding-right: 5px\">\n            \n\n<div class=\"kksr-icon\" style=\"width: 24px; height: 24px;\"><\/div>\n        <\/div>\n            <div class=\"kksr-star\" style=\"padding-right: 5px\">\n            \n\n<div class=\"kksr-icon\" style=\"width: 24px; height: 24px;\"><\/div>\n        <\/div>\n            <div class=\"kksr-star\" style=\"padding-right: 5px\">\n            \n\n<div class=\"kksr-icon\" style=\"width: 24px; height: 24px;\"><\/div>\n        <\/div>\n    <\/div>\n<\/div>\n                \n\n<div class=\"kksr-legend\" style=\"font-size: 19.2px;\">\n            <span class=\"kksr-muted\">Rate this post<\/span>\n    <\/div>\n    <\/div>\n<p><span style=\"font-size: 18px\"><strong><span style=\"color: red\">Verified Professional-Data-Engineer dumps Q&amp;As &#8211; Pass Guarantee Exam Dumps Test Engine [2023]<\/span><\/strong><\/span><\/p>\n<p><strong><span style=\"color: red\">Professional-Data-Engineer dumps and 270 unique questions<\/span><\/strong><\/p>\n<p><\/p>\n<h3>Career Opportunities<\/h3>\n<p>The certified individuals can explore a variety of job opportunities. Some of the positions that they can take up include a Software Engineer, a Cloud Architect, a Data Engineer, a Sales Engineer, a Data Scientist, a Cloud Developer, and a Kubernetes Architect, among others. The salary outlook for these job roles is an average of $128,500 per annum. <\/p>\n<p>&nbsp;<\/p>\n<div id=\"watu_quiz\" class=\"quiz-area single-page-quiz\">\n<form action=\"\" method=\"post\" class=\"quiz-form \" id=\"quiz-696\" >\n<div class='watu-question' id='question-1'><div class='question-content'><p><strong>NEW QUESTION 25<\/strong><br \/>You are integrating one of your internal IT applications and Google BigQuery, so users can query BigQuery from the application&#8217;s interface. You do not want individual users to authenticate to BigQuery and you do not want to give them access to the dataset. You need to securely access BigQuery from your IT application.<br \/>What should you do?<\/p>\n<\/div><input type='hidden' name='question_id[]' value='13727' \/><div class='watu-questions-wrap '><input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='53883' \/><div class='watu-question-choice'><input type='radio' name='answer-13727[]' id='answer-id-53883' class='answer answer-1 js-answer-label answerof-13727' value='53883' \/>&nbsp;<label for='answer-id-53883' id='answer-label-53883' class='js-answer-label answer label-1'><span class='answer'>Create groups for your users and give those groups access to the dataset<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='53884' \/><div class='watu-question-choice'><input type='radio' name='answer-13727[]' id='answer-id-53884' class='answer answer-1 js-answer-label answerof-13727' value='53884' \/>&nbsp;<label for='answer-id-53884' id='answer-label-53884' class='js-answer-label answer label-1'><span class='answer'>Integrate with a single sign-on (SSO) platform, and pass each user&#8217;s credentials along with the query<br \/>request<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='53885' \/><div class='watu-question-choice'><input type='radio' name='answer-13727[]' id='answer-id-53885' class='answer answer-1 php-answer-label answerof-13727' value='53885' \/>&nbsp;<label for='answer-id-53885' id='answer-label-53885' class='php-answer-label answer label-1'><span class='answer'>Create a service account and grant dataset access to that account. Use the service account&#8217;s private key to access the dataset<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='53886' \/><div class='watu-question-choice'><input type='radio' name='answer-13727[]' id='answer-id-53886' class='answer answer-1 js-answer-label answerof-13727' value='53886' \/>&nbsp;<label for='answer-id-53886' id='answer-label-53886' class='js-answer-label answer label-1'><span class='answer'>Create a dummy user and grant dataset access to that user. Store the username and password for that user in a file on the files system, and use those credentials to access the BigQuery dataset<\/span><\/label><\/div>\n<\/div><div class='show-question-feedback' style='display:none;'><\/div><input type='button' class='showchecked' style='margin: 10px 0;' onclick='showanswer1(1,this)' id='btn-1' value='See Answer'  \/><input type='hidden' id='questionType1' value='radio' class=''><\/div><div class='watu-question' id='question-2'><div class='question-content'><p><strong>NEW QUESTION 26<\/strong><br \/>You set up a streaming data insert into a Redis cluster via a Kafka cluster. Both clusters are running on Compute Engine instances. You need to encrypt data at rest with encryption keys that you can create, rotate, and destroy as needed. What should you do?<\/p>\n<\/div><input type='hidden' name='question_id[]' value='13728' \/><div class='watu-questions-wrap '><input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='53887' \/><div class='watu-question-choice'><input type='radio' name='answer-13728[]' id='answer-id-53887' class='answer answer-2 js-answer-label answerof-13728' value='53887' \/>&nbsp;<label for='answer-id-53887' id='answer-label-53887' class='js-answer-label answer label-2'><span class='answer'>Create a dedicated service account, and use encryption at rest to reference your data stored in your Compute Engine cluster instances as part of your API service calls.<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='53888' \/><div class='watu-question-choice'><input type='radio' name='answer-13728[]' id='answer-id-53888' class='answer answer-2 js-answer-label answerof-13728' value='53888' \/>&nbsp;<label for='answer-id-53888' id='answer-label-53888' class='js-answer-label answer label-2'><span class='answer'>Create encryption keys in Cloud Key Management Service. Use those keys to encrypt your data in all of the Compute Engine cluster instances.<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='53889' \/><div class='watu-question-choice'><input type='radio' name='answer-13728[]' id='answer-id-53889' class='answer answer-2 php-answer-label answerof-13728' value='53889' \/>&nbsp;<label for='answer-id-53889' id='answer-label-53889' class='php-answer-label answer label-2'><span class='answer'>Create encryption keys locally. Upload your encryption keys to Cloud Key Management Service. Use those keys to encrypt your data in all of the Compute Engine cluster instances.<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='53890' \/><div class='watu-question-choice'><input type='radio' name='answer-13728[]' id='answer-id-53890' class='answer answer-2 js-answer-label answerof-13728' value='53890' \/>&nbsp;<label for='answer-id-53890' id='answer-label-53890' class='js-answer-label answer label-2'><span class='answer'>Create encryption keys in Cloud Key Management Service. Reference those keys in your API service calls when accessing the data in your Compute Engine cluster instances.<\/span><\/label><\/div>\n<\/div><div class='show-question-feedback' style='display:none;'><\/div><input type='button' class='showchecked' style='margin: 10px 0;' onclick='showanswer1(2,this)' id='btn-2' value='See Answer'  \/><input type='hidden' id='questionType2' value='radio' class=''><\/div><div class='watu-question' id='question-3'><div class='question-content'><p><strong>NEW QUESTION 27<\/strong><br \/>You are building a model to make clothing recommendations. You know a user&#8217;s fashion pis likely to change over time, so you build a data pipeline to stream new data back to the model as it becomes available. How should you use this data to train the model?<\/p>\n<\/div><input type='hidden' name='question_id[]' value='13729' \/><div class='watu-questions-wrap '><input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='53891' \/><div class='watu-question-choice'><input type='radio' name='answer-13729[]' id='answer-id-53891' class='answer answer-3 js-answer-label answerof-13729' value='53891' \/>&nbsp;<label for='answer-id-53891' id='answer-label-53891' class='js-answer-label answer label-3'><span class='answer'>Continuously retrain the model on just the new data.<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='53892' \/><div class='watu-question-choice'><input type='radio' name='answer-13729[]' id='answer-id-53892' class='answer answer-3 php-answer-label answerof-13729' value='53892' \/>&nbsp;<label for='answer-id-53892' id='answer-label-53892' class='php-answer-label answer label-3'><span class='answer'>Continuously retrain the model on a combination of existing data and the new data.<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='53893' \/><div class='watu-question-choice'><input type='radio' name='answer-13729[]' id='answer-id-53893' class='answer answer-3 js-answer-label answerof-13729' value='53893' \/>&nbsp;<label for='answer-id-53893' id='answer-label-53893' class='js-answer-label answer label-3'><span class='answer'>Train on the existing data while using the new data as your test set.<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='53894' \/><div class='watu-question-choice'><input type='radio' name='answer-13729[]' id='answer-id-53894' class='answer answer-3 js-answer-label answerof-13729' value='53894' \/>&nbsp;<label for='answer-id-53894' id='answer-label-53894' class='js-answer-label answer label-3'><span class='answer'>Train on the new data while using the existing data as your test set.<\/span><\/label><\/div>\n<\/div><div class='show-question-feedback' style='display:none;'>We have to use a combination of old and new test data as well as training data.<\/div><input type='button' class='showchecked' style='margin: 10px 0;' onclick='showanswer1(3,this)' id='btn-3' value='See Answer'  \/><input type='hidden' id='questionType3' value='radio' class=''><\/div><div class='watu-question' id='question-4'><div class='question-content'><p><strong>NEW QUESTION 28<\/strong><br \/>Case Study: 2 &#8211; MJTelco<br \/>Company Overview<br \/>MJTelco is a startup that plans to build networks in rapidly growing, underserved markets around the world. The company has patents for innovative optical communications hardware. Based on these patents, they can create many reliable, high-speed backbone links with inexpensive hardware.<br \/>Company Background<br \/>Founded by experienced telecom executives, MJTelco uses technologies originally developed to overcome communications challenges in space. Fundamental to their operation, they need to create a distributed data infrastructure that drives real-time analysis and incorporates machine learning to continuously optimize their topologies. Because their hardware is inexpensive, they plan to overdeploy the network allowing them to account for the impact of dynamic regional politics on location availability and cost. Their management and operations teams are situated all around the globe creating many-to- many relationship between data consumers and provides in their system. After careful consideration, they decided public cloud is the perfect environment to support their needs.<br \/>Solution Concept<br \/>MJTelco is running a successful proof-of-concept (PoC) project in its labs. They have two primary needs:<br \/>Scale and harden their PoC to support significantly more data flows generated when they ramp to more than 50,000 installations.<br \/>Refine their machine-learning cycles to verify and improve the dynamic models they use to control topology definition.<br \/>MJTelco will also use three separate operating environments ?development\/test, staging, and production ?<br \/>to meet the needs of running experiments, deploying new features, and serving production customers.<br \/>Business Requirements<br \/>Scale up their production environment with minimal cost, instantiating resources when and where needed in an unpredictable, distributed telecom user community. Ensure security of their proprietary data to protect their leading-edge machine learning and analysis.<br \/>Provide reliable and timely access to data for analysis from distributed research workers Maintain isolated environments that support rapid iteration of their machine-learning models without affecting their customers.<br \/>Technical Requirements<br \/>Ensure secure and efficient transport and storage of telemetry data Rapidly scale instances to support between 10,000 and 100,000 data providers with multiple flows each.<br \/>Allow analysis and presentation against data tables tracking up to 2 years of data storing approximately<br \/>100m records\/day<br \/>Support rapid iteration of monitoring infrastructure focused on awareness of data pipeline problems both in telemetry flows and in production learning cycles.<br \/>CEO Statement<br \/>Our business model relies on our patents, analytics and dynamic machine learning. Our inexpensive hardware is organized to be highly reliable, which gives us cost advantages. We need to quickly stabilize our large distributed data pipelines to meet our reliability and capacity commitments.<br \/>CTO Statement<br \/>Our public cloud services must operate as advertised. We need resources that scale and keep our data secure. We also need environments in which our data scientists can carefully study and quickly adapt our models. Because we rely on automation to process our data, we also need our development and test environments to work as we iterate.<br \/>CFO Statement<br \/>The project is too large for us to maintain the hardware and software required for the data and analysis.<br \/>Also, we cannot afford to staff an operations team to monitor so many data feeds, so we will rely on automation and infrastructure. Google Cloud&#8217;s machine learning will allow our quantitative researchers to work on our high-value problems instead of problems with our data pipelines.<br \/>Given the record streams MJTelco is interested in ingesting per day, they are concerned about the cost of Google BigQuery increasing. MJTelco asks you to provide a design solution. They require a single large data table called tracking_table. Additionally, they want to minimize the cost of daily queries while performing fine-grained analysis of each day&#8217;s events. They also want to use streaming ingestion. What should you do?<\/p>\n<\/div><input type='hidden' name='question_id[]' value='13730' \/><div class='watu-questions-wrap '><input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='53895' \/><div class='watu-question-choice'><input type='radio' name='answer-13730[]' id='answer-id-53895' class='answer answer-4 js-answer-label answerof-13730' value='53895' \/>&nbsp;<label for='answer-id-53895' id='answer-label-53895' class='js-answer-label answer label-4'><span class='answer'>Create a table called tracking_table and include a DATE column.<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='53896' \/><div class='watu-question-choice'><input type='radio' name='answer-13730[]' id='answer-id-53896' class='answer answer-4 php-answer-label answerof-13730' value='53896' \/>&nbsp;<label for='answer-id-53896' id='answer-label-53896' class='php-answer-label answer label-4'><span class='answer'>Create a partitioned table called tracking_table and include a TIMESTAMP column.<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='53897' \/><div class='watu-question-choice'><input type='radio' name='answer-13730[]' id='answer-id-53897' class='answer answer-4 js-answer-label answerof-13730' value='53897' \/>&nbsp;<label for='answer-id-53897' id='answer-label-53897' class='js-answer-label answer label-4'><span class='answer'>Create sharded tables for each day following the pattern tracking_table_YYYYMMDD.<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='53898' \/><div class='watu-question-choice'><input type='radio' name='answer-13730[]' id='answer-id-53898' class='answer answer-4 js-answer-label answerof-13730' value='53898' \/>&nbsp;<label for='answer-id-53898' id='answer-label-53898' class='js-answer-label answer label-4'><span class='answer'>Create a table called tracking_table with a TIMESTAMP column to represent the day.<\/span><\/label><\/div>\n<\/div><div class='show-question-feedback' style='display:none;'><\/div><input type='button' class='showchecked' style='margin: 10px 0;' onclick='showanswer1(4,this)' id='btn-4' value='See Answer'  \/><input type='hidden' id='questionType4' value='radio' class=''><\/div><div class='watu-question' id='question-5'><div class='question-content'><p><strong>NEW QUESTION 29<\/strong><br \/>Your analytics team wants to build a simple statistical model to determine which customers are most likely<br \/>to work with your company again, based on a few different metrics. They want to run the model on Apache<br \/>Spark, using data housed in Google Cloud Storage, and you have recommended using Google Cloud<br \/>Dataproc to execute this job. Testing has shown that this workload can run in approximately 30 minutes on<br \/>a 15-node cluster, outputting the results into Google BigQuery. The plan is to run this workload weekly.<br \/>How should you optimize the cluster for cost?<\/p>\n<\/div><input type='hidden' name='question_id[]' value='13731' \/><div class='watu-questions-wrap '><input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='53899' \/><div class='watu-question-choice'><input type='radio' name='answer-13731[]' id='answer-id-53899' class='answer answer-5 php-answer-label answerof-13731' value='53899' \/>&nbsp;<label for='answer-id-53899' id='answer-label-53899' class='php-answer-label answer label-5'><span class='answer'>Migrate the workload to Google Cloud Dataflow<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='53900' \/><div class='watu-question-choice'><input type='radio' name='answer-13731[]' id='answer-id-53900' class='answer answer-5 js-answer-label answerof-13731' value='53900' \/>&nbsp;<label for='answer-id-53900' id='answer-label-53900' class='js-answer-label answer label-5'><span class='answer'>Use pre-emptible virtual machines (VMs) for the cluster<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='53901' \/><div class='watu-question-choice'><input type='radio' name='answer-13731[]' id='answer-id-53901' class='answer answer-5 js-answer-label answerof-13731' value='53901' \/>&nbsp;<label for='answer-id-53901' id='answer-label-53901' class='js-answer-label answer label-5'><span class='answer'>Use a higher-memory node so that the job runs faster<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='53902' \/><div class='watu-question-choice'><input type='radio' name='answer-13731[]' id='answer-id-53902' class='answer answer-5 js-answer-label answerof-13731' value='53902' \/>&nbsp;<label for='answer-id-53902' id='answer-label-53902' class='js-answer-label answer label-5'><span class='answer'>Use SSDs on the worker nodes so that the job can run faster<\/span><\/label><\/div>\n<\/div><div class='show-question-feedback' style='display:none;'><\/div><input type='button' class='showchecked' style='margin: 10px 0;' onclick='showanswer1(5,this)' id='btn-5' value='See Answer'  \/><input type='hidden' id='questionType5' value='radio' class=''><\/div><div class='watu-question' id='question-6'><div class='question-content'><p><strong>NEW QUESTION 30<\/strong><br \/>Flowlogistic&#8217;s CEO wants to gain rapid insight into their customer base so his sales team can be better informed in the field. This team is not very technical, so they&#8217;ve purchased a visualization tool to simplify the creation of BigQuery reports. However, they&#8217;ve been overwhelmed by all the data in the table, and are spending a lot of money on queries trying to find the data they need. You want to solve their problem in the most cost-effective way. What should you do?<\/p>\n<\/div><input type='hidden' name='question_id[]' value='13732' \/><div class='watu-questions-wrap '><input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='53903' \/><div class='watu-question-choice'><input type='radio' name='answer-13732[]' id='answer-id-53903' class='answer answer-6 js-answer-label answerof-13732' value='53903' \/>&nbsp;<label for='answer-id-53903' id='answer-label-53903' class='js-answer-label answer label-6'><span class='answer'>Export the data into a Google Sheet for virtualization.<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='53904' \/><div class='watu-question-choice'><input type='radio' name='answer-13732[]' id='answer-id-53904' class='answer answer-6 js-answer-label answerof-13732' value='53904' \/>&nbsp;<label for='answer-id-53904' id='answer-label-53904' class='js-answer-label answer label-6'><span class='answer'>Create an additional table with only the necessary columns.<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='53905' \/><div class='watu-question-choice'><input type='radio' name='answer-13732[]' id='answer-id-53905' class='answer answer-6 php-answer-label answerof-13732' value='53905' \/>&nbsp;<label for='answer-id-53905' id='answer-label-53905' class='php-answer-label answer label-6'><span class='answer'>Create a view on the table to present to the virtualization tool.<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='53906' \/><div class='watu-question-choice'><input type='radio' name='answer-13732[]' id='answer-id-53906' class='answer answer-6 js-answer-label answerof-13732' value='53906' \/>&nbsp;<label for='answer-id-53906' id='answer-label-53906' class='js-answer-label answer label-6'><span class='answer'>Create identity and access management (IAM) roles on the appropriate columns, so only they appear in a query.<\/span><\/label><\/div>\n<\/div><div class='show-question-feedback' style='display:none;'>Topic 2, MJTelco Case Study<br\/>Company Overview<br\/>MJTelco is a startup that plans to build networks in rapidly growing, underserved markets around the world.<br\/>The company has patents for innovative optical communications hardware. Based on these patents, they can create many reliable, high-speed backbone links with inexpensive hardware.<br\/>Company Background<br\/>Founded by experienced telecom executives, MJTelco uses technologies originally developed to overcome communications challenges in space. Fundamental to their operation, they need to create a distributed data infrastructure that drives real-time analysis and incorporates machine learning to continuously optimize their topologies. Because their hardware is inexpensive, they plan to overdeploy the network allowing them to account for the impact of dynamic regional politics on location availability and cost.<br\/>Their management and operations teams are situated all around the globe creating many-to-many relationship between data consumers and provides in their system. After careful consideration, they decided public cloud is the perfect environment to support their needs.<br\/>Solution Concept<br\/>MJTelco is running a successful proof-of-concept (PoC) project in its labs. They have two primary needs:<br\/>* Scale and harden their PoC to support significantly more data flows generated when they ramp to more than 50,000 installations.<br\/>* Refine their machine-learning cycles to verify and improve the dynamic models they use to control topology definition.<br\/>MJTelco will also use three separate operating environments &#8211; development\/test, staging, and production &#8211; to meet the needs of running experiments, deploying new features, and serving production customers.<br\/>Business Requirements<br\/>* Scale up their production environment with minimal cost, instantiating resources when and where needed in an unpredictable, distributed telecom user community.<br\/>* Ensure security of their proprietary data to protect their leading-edge machine learning and analysis.<br\/>* Provide reliable and timely access to data for analysis from distributed research workers<br\/>* Maintain isolated environments that support rapid iteration of their machine-learning models without affecting their customers.<br\/>Technical Requirements<br\/>Ensure secure and efficient transport and storage of telemetry data<br\/>Rapidly scale instances to support between 10,000 and 100,000 data providers with multiple flows each.<br\/>Allow analysis and presentation against data tables tracking up to 2 years of data storing approximately 100m records\/day Support rapid iteration of monitoring infrastructure focused on awareness of data pipeline problems both in telemetry flows and in production learning cycles.<br\/>CEO Statement<br\/>Our business model relies on our patents, analytics and dynamic machine learning. Our inexpensive hardware is organized to be highly reliable, which gives us cost advantages. We need to quickly stabilize our large distributed data pipelines to meet our reliability and capacity commitments.<br\/>CTO Statement<br\/>Our public cloud services must operate as advertised. We need resources that scale and keep our data secure.<br\/>We also need environments in which our data scientists can carefully study and quickly adapt our models.<br\/>Because we rely on automation to process our data, we also need our development and test environments to work as we iterate.<br\/>CFO Statement<br\/>The project is too large for us to maintain the hardware and software required for the data and analysis. Also, we cannot afford to staff an operations team to monitor so many data feeds, so we will rely on automation and infrastructure. Google Cloud&#8217;s machine learning will allow our quantitative researchers to work on our high-value problems instead of problems with our data pipelines.<\/div><input type='button' class='showchecked' style='margin: 10px 0;' onclick='showanswer1(6,this)' id='btn-6' value='See Answer'  \/><input type='hidden' id='questionType6' value='radio' class=''><\/div><div class='watu-question' id='question-7'><div class='question-content'><p><strong>NEW QUESTION 31<\/strong><br \/>You are building an application to share financial market data with consumers, who will receive data feeds.<br \/>Data is collected from the markets in real time. Consumers will receive the data in the following ways:<br \/>* Real-time event stream<br \/>* ANSI SQL access to real-time stream and historical data<br \/>* Batch historical exports<br \/>Which solution should you use?<\/p>\n<\/div><input type='hidden' name='question_id[]' value='13733' \/><div class='watu-questions-wrap '><input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='53907' \/><div class='watu-question-choice'><input type='radio' name='answer-13733[]' id='answer-id-53907' class='answer answer-7 php-answer-label answerof-13733' value='53907' \/>&nbsp;<label for='answer-id-53907' id='answer-label-53907' class='php-answer-label answer label-7'><span class='answer'>Cloud Dataflow, Cloud SQL, Cloud Spanner<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='53908' \/><div class='watu-question-choice'><input type='radio' name='answer-13733[]' id='answer-id-53908' class='answer answer-7 js-answer-label answerof-13733' value='53908' \/>&nbsp;<label for='answer-id-53908' id='answer-label-53908' class='js-answer-label answer label-7'><span class='answer'>Cloud Pub\/Sub, Cloud Storage, BigQuery<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='53909' \/><div class='watu-question-choice'><input type='radio' name='answer-13733[]' id='answer-id-53909' class='answer answer-7 js-answer-label answerof-13733' value='53909' \/>&nbsp;<label for='answer-id-53909' id='answer-label-53909' class='js-answer-label answer label-7'><span class='answer'>Cloud Dataproc, Cloud Dataflow, BigQuery<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='53910' \/><div class='watu-question-choice'><input type='radio' name='answer-13733[]' id='answer-id-53910' class='answer answer-7 js-answer-label answerof-13733' value='53910' \/>&nbsp;<label for='answer-id-53910' id='answer-label-53910' class='js-answer-label answer label-7'><span class='answer'>Cloud Pub\/Sub, Cloud Dataproc, Cloud SQL<\/span><\/label><\/div>\n<\/div><div class='show-question-feedback' style='display:none;'><\/div><input type='button' class='showchecked' style='margin: 10px 0;' onclick='showanswer1(7,this)' id='btn-7' value='See Answer'  \/><input type='hidden' id='questionType7' value='radio' class=''><\/div><div class='watu-question' id='question-8'><div class='question-content'><p><strong>NEW QUESTION 32<\/strong><br \/>The CUSTOM tier for Cloud Machine Learning Engine allows you to specify the number of which types of cluster nodes?<\/p>\n<\/div><input type='hidden' name='question_id[]' value='13734' \/><div class='watu-questions-wrap '><input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='53911' \/><div class='watu-question-choice'><input type='radio' name='answer-13734[]' id='answer-id-53911' class='answer answer-8 js-answer-label answerof-13734' value='53911' \/>&nbsp;<label for='answer-id-53911' id='answer-label-53911' class='js-answer-label answer label-8'><span class='answer'>Workers<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='53912' \/><div class='watu-question-choice'><input type='radio' name='answer-13734[]' id='answer-id-53912' class='answer answer-8 js-answer-label answerof-13734' value='53912' \/>&nbsp;<label for='answer-id-53912' id='answer-label-53912' class='js-answer-label answer label-8'><span class='answer'>Masters, workers, and parameter servers<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='53913' \/><div class='watu-question-choice'><input type='radio' name='answer-13734[]' id='answer-id-53913' class='answer answer-8 php-answer-label answerof-13734' value='53913' \/>&nbsp;<label for='answer-id-53913' id='answer-label-53913' class='php-answer-label answer label-8'><span class='answer'>Workers and parameter servers<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='53914' \/><div class='watu-question-choice'><input type='radio' name='answer-13734[]' id='answer-id-53914' class='answer answer-8 js-answer-label answerof-13734' value='53914' \/>&nbsp;<label for='answer-id-53914' id='answer-label-53914' class='js-answer-label answer label-8'><span class='answer'>Parameter servers<\/span><\/label><\/div>\n<\/div><div class='show-question-feedback' style='display:none;'>The CUSTOM tier is not a set tier, but rather enables you to use your own cluster specification. When you use this tier, set values to configure your processing cluster according to these guidelines:<br\/>You must set TrainingInput.masterType to specify the type of machine to use for your master node.<br\/>You may set TrainingInput.workerCount to specify the number of workers to use.<br\/>You may set TrainingInput.parameterServerCount to specify the number of parameter servers to use.<br\/>You can specify the type of machine for the master node, but you can&#8217;t specify more than one master node.<br\/>Reference: https:\/\/cloud.google.com\/ml-engine\/docs\/training-overview#job_configuration_parameters<\/div><input type='button' class='showchecked' style='margin: 10px 0;' onclick='showanswer1(8,this)' id='btn-8' value='See Answer'  \/><input type='hidden' id='questionType8' value='radio' class=''><\/div><div class='watu-question' id='question-9'><div class='question-content'><p><strong>NEW QUESTION 33<\/strong><br \/>Your company is performing data preprocessing for a learning algorithm in Google Cloud Dataflow.<br \/>Numerous data logs are being are being generated during this step, and the team wants to analyze them.<br \/>Due to the dynamic nature of the campaign, the data is growing exponentially every hour. The data scientists have written the following code to read the data for a new key features in the logs.<br \/>BigQueryIO.Read<br \/>.named(&#8220;ReadLogData&#8221;)<br \/>.from(&#8220;clouddataflow-readonly:samples.log_data&#8221;)<br \/>You want to improve the performance of this data read. What should you do?<\/p>\n<\/div><input type='hidden' name='question_id[]' value='13735' \/><div class='watu-questions-wrap '><input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='53915' \/><div class='watu-question-choice'><input type='radio' name='answer-13735[]' id='answer-id-53915' class='answer answer-9 js-answer-label answerof-13735' value='53915' \/>&nbsp;<label for='answer-id-53915' id='answer-label-53915' class='js-answer-label answer label-9'><span class='answer'>Specify the Tableobject in the code.<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='53916' \/><div class='watu-question-choice'><input type='radio' name='answer-13735[]' id='answer-id-53916' class='answer answer-9 js-answer-label answerof-13735' value='53916' \/>&nbsp;<label for='answer-id-53916' id='answer-label-53916' class='js-answer-label answer label-9'><span class='answer'>Use .fromQuery operation to read specific fields from the table.<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='53917' \/><div class='watu-question-choice'><input type='radio' name='answer-13735[]' id='answer-id-53917' class='answer answer-9 js-answer-label answerof-13735' value='53917' \/>&nbsp;<label for='answer-id-53917' id='answer-label-53917' class='js-answer-label answer label-9'><span class='answer'>Use of both the Google BigQuery TableSchema and TableFieldSchema classes.<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='53918' \/><div class='watu-question-choice'><input type='radio' name='answer-13735[]' id='answer-id-53918' class='answer answer-9 php-answer-label answerof-13735' value='53918' \/>&nbsp;<label for='answer-id-53918' id='answer-label-53918' class='php-answer-label answer label-9'><span class='answer'>Call a transform that returns TableRow objects, where each element in the PCollexction represents a single row in the table.<\/span><\/label><\/div>\n<\/div><div class='show-question-feedback' style='display:none;'><\/div><input type='button' class='showchecked' style='margin: 10px 0;' onclick='showanswer1(9,this)' id='btn-9' value='See Answer'  \/><input type='hidden' id='questionType9' value='radio' class=''><\/div><div class='watu-question' id='question-10'><div class='question-content'><p><strong>NEW QUESTION 34<\/strong><br \/>Your software uses a simple JSON format for all messages. These messages are published to Google Cloud Pub\/Sub, then processed with Google Cloud Dataflow to create a real-time dashboard for the CFO. During testing, you notice that some messages are missing in the dashboard. You check the logs, and all messages are being published to Cloud Pub\/Sub successfully. What should you do next?<\/p>\n<\/div><input type='hidden' name='question_id[]' value='13736' \/><div class='watu-questions-wrap '><input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='53919' \/><div class='watu-question-choice'><input type='radio' name='answer-13736[]' id='answer-id-53919' class='answer answer-10 js-answer-label answerof-13736' value='53919' \/>&nbsp;<label for='answer-id-53919' id='answer-label-53919' class='js-answer-label answer label-10'><span class='answer'>Check the dashboard application to see if it is not displaying correctly.<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='53920' \/><div class='watu-question-choice'><input type='radio' name='answer-13736[]' id='answer-id-53920' class='answer answer-10 php-answer-label answerof-13736' value='53920' \/>&nbsp;<label for='answer-id-53920' id='answer-label-53920' class='php-answer-label answer label-10'><span class='answer'>Run a fixed dataset through the Cloud Dataflow pipeline and analyze the output.<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='53921' \/><div class='watu-question-choice'><input type='radio' name='answer-13736[]' id='answer-id-53921' class='answer answer-10 js-answer-label answerof-13736' value='53921' \/>&nbsp;<label for='answer-id-53921' id='answer-label-53921' class='js-answer-label answer label-10'><span class='answer'>Use Google Stackdriver Monitoring on Cloud Pub\/Sub to find the missing messages.<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='53922' \/><div class='watu-question-choice'><input type='radio' name='answer-13736[]' id='answer-id-53922' class='answer answer-10 js-answer-label answerof-13736' value='53922' \/>&nbsp;<label for='answer-id-53922' id='answer-label-53922' class='js-answer-label answer label-10'><span class='answer'>Switch Cloud Dataflow to pull messages from Cloud Pub\/Sub instead of Cloud Pub\/Sub pushing messages to Cloud Dataflow.<\/span><\/label><\/div>\n<\/div><div class='show-question-feedback' style='display:none;'>Explanation:<\/div><input type='button' class='showchecked' style='margin: 10px 0;' onclick='showanswer1(10,this)' id='btn-10' value='See Answer'  \/><input type='hidden' id='questionType10' value='radio' class=''><\/div><div class='watu-question' id='question-11'><div class='question-content'><p><strong>NEW QUESTION 35<\/strong><br \/>Which is not a valid reason for poor Cloud Bigtable performance?<\/p>\n<\/div><input type='hidden' name='question_id[]' value='13737' \/><div class='watu-questions-wrap '><input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='53923' \/><div class='watu-question-choice'><input type='radio' name='answer-13737[]' id='answer-id-53923' class='answer answer-11 js-answer-label answerof-13737' value='53923' \/>&nbsp;<label for='answer-id-53923' id='answer-label-53923' class='js-answer-label answer label-11'><span class='answer'>The workload isn&#8217;t appropriate for Cloud Bigtable.<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='53924' \/><div class='watu-question-choice'><input type='radio' name='answer-13737[]' id='answer-id-53924' class='answer answer-11 js-answer-label answerof-13737' value='53924' \/>&nbsp;<label for='answer-id-53924' id='answer-label-53924' class='js-answer-label answer label-11'><span class='answer'>The table&#8217;s schema is not designed correctly.<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='53925' \/><div class='watu-question-choice'><input type='radio' name='answer-13737[]' id='answer-id-53925' class='answer answer-11 php-answer-label answerof-13737' value='53925' \/>&nbsp;<label for='answer-id-53925' id='answer-label-53925' class='php-answer-label answer label-11'><span class='answer'>The Cloud Bigtable cluster has too many nodes.<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='53926' \/><div class='watu-question-choice'><input type='radio' name='answer-13737[]' id='answer-id-53926' class='answer answer-11 js-answer-label answerof-13737' value='53926' \/>&nbsp;<label for='answer-id-53926' id='answer-label-53926' class='js-answer-label answer label-11'><span class='answer'>There are issues with the network connection.<\/span><\/label><\/div>\n<\/div><div class='show-question-feedback' style='display:none;'>Explanation<br\/>The Cloud Bigtable cluster doesn&#8217;t have enough nodes. If your Cloud Bigtable cluster is overloaded, adding more nodes can improve performance. Use the monitoring tools to check whether the cluster is overloaded.<br\/>Reference: https:\/\/cloud.google.com\/bigtable\/docs\/performance<\/div><input type='button' class='showchecked' style='margin: 10px 0;' onclick='showanswer1(11,this)' id='btn-11' value='See Answer'  \/><input type='hidden' id='questionType11' value='radio' class=''><\/div><div class='watu-question' id='question-12'><div class='question-content'><p><strong>NEW QUESTION 36<\/strong><br \/>Which of the following statements about Legacy SQL and Standard SQL is not true?<\/p>\n<\/div><input type='hidden' name='question_id[]' value='13738' \/><div class='watu-questions-wrap '><input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='53927' \/><div class='watu-question-choice'><input type='radio' name='answer-13738[]' id='answer-id-53927' class='answer answer-12 js-answer-label answerof-13738' value='53927' \/>&nbsp;<label for='answer-id-53927' id='answer-label-53927' class='js-answer-label answer label-12'><span class='answer'>Standard SQL is the preferred query language for BigQuery.<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='53928' \/><div class='watu-question-choice'><input type='radio' name='answer-13738[]' id='answer-id-53928' class='answer answer-12 js-answer-label answerof-13738' value='53928' \/>&nbsp;<label for='answer-id-53928' id='answer-label-53928' class='js-answer-label answer label-12'><span class='answer'>If you write a query in Legacy SQL, it might generate an error if you try to run it with Standard SQL.<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='53929' \/><div class='watu-question-choice'><input type='radio' name='answer-13738[]' id='answer-id-53929' class='answer answer-12 js-answer-label answerof-13738' value='53929' \/>&nbsp;<label for='answer-id-53929' id='answer-label-53929' class='js-answer-label answer label-12'><span class='answer'>One difference between the two query languages is how you specify fully-qualified table names (i.e.<br \/>table names that include their associated project name).<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='53930' \/><div class='watu-question-choice'><input type='radio' name='answer-13738[]' id='answer-id-53930' class='answer answer-12 php-answer-label answerof-13738' value='53930' \/>&nbsp;<label for='answer-id-53930' id='answer-label-53930' class='php-answer-label answer label-12'><span class='answer'>You need to set a query language for each dataset and the default is Standard SQL.<\/span><\/label><\/div>\n<\/div><div class='show-question-feedback' style='display:none;'>You do not set a query language for each dataset. It is set each time you run a query and the default query language is Legacy SQL.<br\/>Standard SQL has been the preferred query language since BigQuery 2.0 was released. In legacy SQL, to query a table with a project-qualified name, you use a colon, :, as a separator. In standard SQL, you use a period, ., instead.<br\/>Due to the differences in syntax between the two query languages (such as with project-qualified table names), if you write a query in Legacy SQL, it might generate an error if you try to run it with Standard SQL.<br\/>Reference:<br\/>https:\/\/cloud.google.com\/bigquery\/docs\/reference\/standard-sql\/migrating-from-legacy-sql<\/div><input type='button' class='showchecked' style='margin: 10px 0;' onclick='showanswer1(12,this)' id='btn-12' value='See Answer'  \/><input type='hidden' id='questionType12' value='radio' class=''><\/div><div class='watu-question' id='question-13'><div class='question-content'><p><strong>NEW QUESTION 37<\/strong><br \/>Your company is performing data preprocessing for a learning algorithm in Google Cloud Dataflow.<br \/>Numerous data logs are being are being generated during this step, and the team wants to analyze them.<br \/>Due to the dynamic nature of the campaign, the data is growing exponentially every hour.<br \/>The data scientists have written the following code to read the data for a new key features in the logs.<br \/>BigQueryIO.Read<br \/>.named(&#8220;ReadLogData&#8221;)<br \/>.from(&#8220;clouddataflow-readonly:samples.log_data&#8221;)<br \/>You want to improve the performance of this data read. What should you do?<\/p>\n<\/div><input type='hidden' name='question_id[]' value='13739' \/><div class='watu-questions-wrap '><input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='53931' \/><div class='watu-question-choice'><input type='radio' name='answer-13739[]' id='answer-id-53931' class='answer answer-13 js-answer-label answerof-13739' value='53931' \/>&nbsp;<label for='answer-id-53931' id='answer-label-53931' class='js-answer-label answer label-13'><span class='answer'>Specify the TableReferenceobject in the code.<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='53932' \/><div class='watu-question-choice'><input type='radio' name='answer-13739[]' id='answer-id-53932' class='answer answer-13 js-answer-label answerof-13739' value='53932' \/>&nbsp;<label for='answer-id-53932' id='answer-label-53932' class='js-answer-label answer label-13'><span class='answer'>Use .fromQueryoperation to read specific fields from the table.<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='53933' \/><div class='watu-question-choice'><input type='radio' name='answer-13739[]' id='answer-id-53933' class='answer answer-13 js-answer-label answerof-13739' value='53933' \/>&nbsp;<label for='answer-id-53933' id='answer-label-53933' class='js-answer-label answer label-13'><span class='answer'>Use of both the Google BigQuery TableSchemaand TableFieldSchemaclasses.<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='53934' \/><div class='watu-question-choice'><input type='radio' name='answer-13739[]' id='answer-id-53934' class='answer answer-13 php-answer-label answerof-13739' value='53934' \/>&nbsp;<label for='answer-id-53934' id='answer-label-53934' class='php-answer-label answer label-13'><span class='answer'>Call a transform that returns TableRowobjects, where each element in the PCollectionrepresents a single row in the table.<\/span><\/label><\/div>\n<\/div><div class='show-question-feedback' style='display:none;'><\/div><input type='button' class='showchecked' style='margin: 10px 0;' onclick='showanswer1(13,this)' id='btn-13' value='See Answer'  \/><input type='hidden' id='questionType13' value='radio' class=''><\/div><div class='watu-question' id='question-14'><div class='question-content'><p><strong>NEW QUESTION 38<\/strong><br \/>Your neural network model is taking days to train. You want to increase the training speed. What can you do?<\/p>\n<\/div><input type='hidden' name='question_id[]' value='13740' \/><div class='watu-questions-wrap '><input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='53935' \/><div class='watu-question-choice'><input type='radio' name='answer-13740[]' id='answer-id-53935' class='answer answer-14 js-answer-label answerof-13740' value='53935' \/>&nbsp;<label for='answer-id-53935' id='answer-label-53935' class='js-answer-label answer label-14'><span class='answer'>Subsample your test dataset.<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='53936' \/><div class='watu-question-choice'><input type='radio' name='answer-13740[]' id='answer-id-53936' class='answer answer-14 php-answer-label answerof-13740' value='53936' \/>&nbsp;<label for='answer-id-53936' id='answer-label-53936' class='php-answer-label answer label-14'><span class='answer'>Subsample your training dataset.<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='53937' \/><div class='watu-question-choice'><input type='radio' name='answer-13740[]' id='answer-id-53937' class='answer answer-14 js-answer-label answerof-13740' value='53937' \/>&nbsp;<label for='answer-id-53937' id='answer-label-53937' class='js-answer-label answer label-14'><span class='answer'>Increase the number of input features to your model.<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='53938' \/><div class='watu-question-choice'><input type='radio' name='answer-13740[]' id='answer-id-53938' class='answer answer-14 js-answer-label answerof-13740' value='53938' \/>&nbsp;<label for='answer-id-53938' id='answer-label-53938' class='js-answer-label answer label-14'><span class='answer'>Increase the number of layers in your neural network.<\/span><\/label><\/div>\n<\/div><div class='show-question-feedback' style='display:none;'>Subsampling is the method to increase the training speed.<\/div><input type='button' class='showchecked' style='margin: 10px 0;' onclick='showanswer1(14,this)' id='btn-14' value='See Answer'  \/><input type='hidden' id='questionType14' value='radio' class=''><\/div><div class='watu-question' id='question-15'><div class='question-content'><p><strong>NEW QUESTION 39<\/strong><br \/>You are designing storage for two relational tables that are part of a 10-TB database on Google Cloud. You want to support transactions that scale horizontally. You also want to optimize data for range queries on nonkey columns. What should you do?<\/p>\n<\/div><input type='hidden' name='question_id[]' value='13741' \/><div class='watu-questions-wrap '><input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='53939' \/><div class='watu-question-choice'><input type='radio' name='answer-13741[]' id='answer-id-53939' class='answer answer-15 js-answer-label answerof-13741' value='53939' \/>&nbsp;<label for='answer-id-53939' id='answer-label-53939' class='js-answer-label answer label-15'><span class='answer'>Use Cloud SQL for storage. Add secondary indexes to support query patterns.<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='53940' \/><div class='watu-question-choice'><input type='radio' name='answer-13741[]' id='answer-id-53940' class='answer answer-15 js-answer-label answerof-13741' value='53940' \/>&nbsp;<label for='answer-id-53940' id='answer-label-53940' class='js-answer-label answer label-15'><span class='answer'>Use Cloud SQL for storage. Use Cloud Dataflow to transform data to support query patterns.<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='53941' \/><div class='watu-question-choice'><input type='radio' name='answer-13741[]' id='answer-id-53941' class='answer answer-15 js-answer-label answerof-13741' value='53941' \/>&nbsp;<label for='answer-id-53941' id='answer-label-53941' class='js-answer-label answer label-15'><span class='answer'>Use Cloud Spanner for storage. Add secondary indexes to support query patterns.<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='53942' \/><div class='watu-question-choice'><input type='radio' name='answer-13741[]' id='answer-id-53942' class='answer answer-15 php-answer-label answerof-13741' value='53942' \/>&nbsp;<label for='answer-id-53942' id='answer-label-53942' class='php-answer-label answer label-15'><span class='answer'>Use Cloud Spanner for storage. Use Cloud Dataflow to transform data to support query patterns.<\/span><\/label><\/div>\n<\/div><div class='show-question-feedback' style='display:none;'><\/div><input type='button' class='showchecked' style='margin: 10px 0;' onclick='showanswer1(15,this)' id='btn-15' value='See Answer'  \/><input type='hidden' id='questionType15' value='radio' class=''><\/div><div class='watu-question' id='question-16'><div class='question-content'><p><strong>NEW QUESTION 40<\/strong><br \/>Which of these statements about exporting data from BigQuery is false?<\/p>\n<\/div><input type='hidden' name='question_id[]' value='13742' \/><div class='watu-questions-wrap '><input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='53943' \/><div class='watu-question-choice'><input type='radio' name='answer-13742[]' id='answer-id-53943' class='answer answer-16 js-answer-label answerof-13742' value='53943' \/>&nbsp;<label for='answer-id-53943' id='answer-label-53943' class='js-answer-label answer label-16'><span class='answer'>To export more than 1 GB of data, you need to put a wildcard in the destination filename.<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='53944' \/><div class='watu-question-choice'><input type='radio' name='answer-13742[]' id='answer-id-53944' class='answer answer-16 js-answer-label answerof-13742' value='53944' \/>&nbsp;<label for='answer-id-53944' id='answer-label-53944' class='js-answer-label answer label-16'><span class='answer'>The only supported export destination is Google Cloud Storage.<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='53945' \/><div class='watu-question-choice'><input type='radio' name='answer-13742[]' id='answer-id-53945' class='answer answer-16 php-answer-label answerof-13742' value='53945' \/>&nbsp;<label for='answer-id-53945' id='answer-label-53945' class='php-answer-label answer label-16'><span class='answer'>Data can only be exported in JSON or Avro format.<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='53946' \/><div class='watu-question-choice'><input type='radio' name='answer-13742[]' id='answer-id-53946' class='answer answer-16 js-answer-label answerof-13742' value='53946' \/>&nbsp;<label for='answer-id-53946' id='answer-label-53946' class='js-answer-label answer label-16'><span class='answer'>The only compression option available is GZIP.<\/span><\/label><\/div>\n<\/div><div class='show-question-feedback' style='display:none;'>Data can be exported in CSV, JSON, or Avro format. If you are exporting nested or repeated data, then CSV format is not supported.<br\/>Reference: https:\/\/cloud.google.com\/bigquery\/docs\/exporting-data<\/div><input type='button' class='showchecked' style='margin: 10px 0;' onclick='showanswer1(16,this)' id='btn-16' value='See Answer'  \/><input type='hidden' id='questionType16' value='radio' class=''><\/div><div class='watu-question' id='question-17'><div class='question-content'><p><strong>NEW QUESTION 41<\/strong><br \/>MJTelco Case Study<br \/>Company Overview<br \/>MJTelco is a startup that plans to build networks in rapidly growing, underserved markets around the world.<br \/>The company has patents for innovative optical communications hardware. Based on these patents, they can create many reliable, high-speed backbone links with inexpensive hardware.<br \/>Company Background<br \/>Founded by experienced telecom executives, MJTelco uses technologies originally developed to overcome communications challenges in space. Fundamental to their operation, they need to create a distributed data infrastructure that drives real-time analysis and incorporates machine learning to continuously optimize their topologies. Because their hardware is inexpensive, they plan to overdeploy the network allowing them to account for the impact of dynamic regional politics on location availability and cost.<br \/>Their management and operations teams are situated all around the globe creating many-to-many relationship between data consumers and provides in their system. After careful consideration, they decided public cloud is the perfect environment to support their needs.<br \/>Solution Concept<br \/>MJTelco is running a successful proof-of-concept (PoC) project in its labs. They have two primary needs:<br \/>* Scale and harden their PoC to support significantly more data flows generated when they ramp to more than 50,000 installations.<br \/>* Refine their machine-learning cycles to verify and improve the dynamic models they use to control topology definition.<br \/>MJTelco will also use three separate operating environments &#8211; development\/test, staging, and production &#8211; to meet the needs of running experiments, deploying new features, and serving production customers.<br \/>Business Requirements<br \/>* Scale up their production environment with minimal cost, instantiating resources when and where needed in an unpredictable, distributed telecom user community.<br \/>* Ensure security of their proprietary data to protect their leading-edge machine learning and analysis.<br \/>* Provide reliable and timely access to data for analysis from distributed research workers<br \/>* Maintain isolated environments that support rapid iteration of their machine-learning models without affecting their customers.<br \/>Technical Requirements<br \/>* Ensure secure and efficient transport and storage of telemetry data<br \/>* Rapidly scale instances to support between 10,000 and 100,000 data providers with multiple flows each.<br \/>* Allow analysis and presentation against data tables tracking up to 2 years of data storing approximately<br \/>100m records\/day<br \/>* Support rapid iteration of monitoring infrastructure focused on awareness of data pipeline problems both in telemetry flows and in production learning cycles.<br \/>CEO Statement<br \/>Our business model relies on our patents, analytics and dynamic machine learning. Our inexpensive hardware is organized to be highly reliable, which gives us cost advantages. We need to quickly stabilize our large distributed data pipelines to meet our reliability and capacity commitments.<br \/>CTO Statement<br \/>Our public cloud services must operate as advertised. We need resources that scale and keep our data secure. We also need environments in which our data scientists can carefully study and quickly adapt our models. Because we rely on automation to process our data, we also need our development and test environments to work as we iterate.<br \/>CFO Statement<br \/>The project is too large for us to maintain the hardware and software required for the data and analysis. Also, we cannot afford to staff an operations team to monitor so many data feeds, so we will rely on automation and infrastructure. Google Cloud&#8217;s machine learning will allow our quantitative researchers to work on our high- value problems instead of problems with our data pipelines.<br \/>MJTelco&#8217;s Google Cloud Dataflow pipeline is now ready to start receiving data from the 50,000 installations.<br \/>You want to allow Cloud Dataflow to scale its compute power up as required. Which Cloud Dataflow pipeline configuration setting should you update?<\/p>\n<\/div><input type='hidden' name='question_id[]' value='13743' \/><div class='watu-questions-wrap '><input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='53947' \/><div class='watu-question-choice'><input type='radio' name='answer-13743[]' id='answer-id-53947' class='answer answer-17 php-answer-label answerof-13743' value='53947' \/>&nbsp;<label for='answer-id-53947' id='answer-label-53947' class='php-answer-label answer label-17'><span class='answer'>The zone<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='53948' \/><div class='watu-question-choice'><input type='radio' name='answer-13743[]' id='answer-id-53948' class='answer answer-17 js-answer-label answerof-13743' value='53948' \/>&nbsp;<label for='answer-id-53948' id='answer-label-53948' class='js-answer-label answer label-17'><span class='answer'>The number of workers<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='53949' \/><div class='watu-question-choice'><input type='radio' name='answer-13743[]' id='answer-id-53949' class='answer answer-17 js-answer-label answerof-13743' value='53949' \/>&nbsp;<label for='answer-id-53949' id='answer-label-53949' class='js-answer-label answer label-17'><span class='answer'>The disk size per worker<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='53950' \/><div class='watu-question-choice'><input type='radio' name='answer-13743[]' id='answer-id-53950' class='answer answer-17 js-answer-label answerof-13743' value='53950' \/>&nbsp;<label for='answer-id-53950' id='answer-label-53950' class='js-answer-label answer label-17'><span class='answer'>The maximum number of workers<\/span><\/label><\/div>\n<\/div><div class='show-question-feedback' style='display:none;'><\/div><input type='button' class='showchecked' style='margin: 10px 0;' onclick='showanswer1(17,this)' id='btn-17' value='See Answer'  \/><input type='hidden' id='questionType17' value='radio' class=''><\/div><div class='watu-question' id='question-18'><div class='question-content'><p><strong>NEW QUESTION 42<\/strong><br \/>What are two methods that can be used to denormalize tables in BigQuery?<\/p>\n<\/div><input type='hidden' name='question_id[]' value='13744' \/><div class='watu-questions-wrap '><input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='53951' \/><div class='watu-question-choice'><input type='radio' name='answer-13744[]' id='answer-id-53951' class='answer answer-18 js-answer-label answerof-13744' value='53951' \/>&nbsp;<label for='answer-id-53951' id='answer-label-53951' class='js-answer-label answer label-18'><span class='answer'>1) Use a partitioned table; 2) Join tables into one table<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='53952' \/><div class='watu-question-choice'><input type='radio' name='answer-13744[]' id='answer-id-53952' class='answer answer-18 js-answer-label answerof-13744' value='53952' \/>&nbsp;<label for='answer-id-53952' id='answer-label-53952' class='js-answer-label answer label-18'><span class='answer'>1) Split table into multiple tables; 2) Use a partitioned table<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='53953' \/><div class='watu-question-choice'><input type='radio' name='answer-13744[]' id='answer-id-53953' class='answer answer-18 php-answer-label answerof-13744' value='53953' \/>&nbsp;<label for='answer-id-53953' id='answer-label-53953' class='php-answer-label answer label-18'><span class='answer'>1) Join tables into one table; 2) Use nested repeated fields<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='53954' \/><div class='watu-question-choice'><input type='radio' name='answer-13744[]' id='answer-id-53954' class='answer answer-18 js-answer-label answerof-13744' value='53954' \/>&nbsp;<label for='answer-id-53954' id='answer-label-53954' class='js-answer-label answer label-18'><span class='answer'>1) Use nested repeated fields; 2) Use a partitioned table<\/span><\/label><\/div>\n<\/div><div class='show-question-feedback' style='display:none;'><\/div><input type='button' class='showchecked' style='margin: 10px 0;' onclick='showanswer1(18,this)' id='btn-18' value='See Answer'  \/><input type='hidden' id='questionType18' value='radio' class=''><\/div><div class='watu-question' id='question-19'><div class='question-content'><p><strong>NEW QUESTION 43<\/strong><br \/>You are managing a Cloud Dataproc cluster. You need to make a job run faster while minimizing costs, without losing work in progress on your clusters. What should you do?<\/p>\n<\/div><input type='hidden' name='question_id[]' value='13745' \/><div class='watu-questions-wrap '><input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='53955' \/><div class='watu-question-choice'><input type='radio' name='answer-13745[]' id='answer-id-53955' class='answer answer-19 js-answer-label answerof-13745' value='53955' \/>&nbsp;<label for='answer-id-53955' id='answer-label-53955' class='js-answer-label answer label-19'><span class='answer'>Increase the cluster size with more non-preemptible workers.<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='53956' \/><div class='watu-question-choice'><input type='radio' name='answer-13745[]' id='answer-id-53956' class='answer answer-19 js-answer-label answerof-13745' value='53956' \/>&nbsp;<label for='answer-id-53956' id='answer-label-53956' class='js-answer-label answer label-19'><span class='answer'>Increase the cluster size with preemptible worker nodes, and configure them to forcefully decommission.<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='53957' \/><div class='watu-question-choice'><input type='radio' name='answer-13745[]' id='answer-id-53957' class='answer answer-19 js-answer-label answerof-13745' value='53957' \/>&nbsp;<label for='answer-id-53957' id='answer-label-53957' class='js-answer-label answer label-19'><span class='answer'>Increase the cluster size with preemptible worker nodes, and use Cloud Stackdriver to trigger a script to preserve work.<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='53958' \/><div class='watu-question-choice'><input type='radio' name='answer-13745[]' id='answer-id-53958' class='answer answer-19 php-answer-label answerof-13745' value='53958' \/>&nbsp;<label for='answer-id-53958' id='answer-label-53958' class='php-answer-label answer label-19'><span class='answer'>Increase the cluster size with preemptible worker nodes, and configure them to use graceful decommissioning.<\/span><\/label><\/div>\n<\/div><div class='show-question-feedback' style='display:none;'>Explanation\/Reference:<br\/>Reference https:\/\/cloud.google.com\/dataproc\/docs\/concepts\/configuring-clusters\/flex<\/div><input type='button' class='showchecked' style='margin: 10px 0;' onclick='showanswer1(19,this)' id='btn-19' value='See Answer'  \/><input type='hidden' id='questionType19' value='radio' class=''><\/div><div class='watu-question' id='question-20'><div class='question-content'><p><strong>NEW QUESTION 44<\/strong><br \/>You are deploying a new storage system for your mobile application, which is a media streaming service.<br \/>You decide the best fit is Google Cloud Datastore. You have entities with multiple properties, some of<br \/>which can take on multiple values. For example, in the entity &#8216;Movie&#8217;the property &#8216;actors&#8217;and the<br \/>property &#8216;tags&#8217; have multiple values but the property &#8216;date released&#8217; does not. A typical query<br \/>would ask for all movies with actor=&lt;actorname&gt;ordered by date_releasedor all movies with<br \/>tag=Comedyordered by date_released. How should you avoid a combinatorial explosion in the<br \/>number of indexes?<\/p>\n<\/div><input type='hidden' name='question_id[]' value='13746' \/><div class='watu-questions-wrap '><input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='53959' \/><div class='watu-question-choice'><input type='radio' name='answer-13746[]' id='answer-id-53959' class='answer answer-20 php-answer-label answerof-13746' value='53959' \/>&nbsp;<label for='answer-id-53959' id='answer-label-53959' class='php-answer-label answer label-20'><span class='answer'>Manually configure the index in your index config as follows:<br \/><img decoding=\"async\" src=\"https:\/\/blog.examboosts.com\/wp-content\/uploads\/2023\/06\/Professional-Data-Engineer-6fb750fb094a438031abda7b58c20159.jpg\"\/><\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='53960' \/><div class='watu-question-choice'><input type='radio' name='answer-13746[]' id='answer-id-53960' class='answer answer-20 js-answer-label answerof-13746' value='53960' \/>&nbsp;<label for='answer-id-53960' id='answer-label-53960' class='js-answer-label answer label-20'><span class='answer'>Manually configure the index in your index config as follows:<br \/><img decoding=\"async\" src=\"https:\/\/blog.examboosts.com\/wp-content\/uploads\/2023\/06\/Professional-Data-Engineer-32461dd2c792d3f9ad34b47cdbacd653.jpg\"\/><\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='53961' \/><div class='watu-question-choice'><input type='radio' name='answer-13746[]' id='answer-id-53961' class='answer answer-20 js-answer-label answerof-13746' value='53961' \/>&nbsp;<label for='answer-id-53961' id='answer-label-53961' class='js-answer-label answer label-20'><span class='answer'>Set the following in your entity options: exclude_from_indexes = &#8216;actors, tags&#8217;<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='53962' \/><div class='watu-question-choice'><input type='radio' name='answer-13746[]' id='answer-id-53962' class='answer answer-20 js-answer-label answerof-13746' value='53962' \/>&nbsp;<label for='answer-id-53962' id='answer-label-53962' class='js-answer-label answer label-20'><span class='answer'>Set the following in your entity options: exclude_from_indexes = &#8216;date_published&#8217;<\/span><\/label><\/div>\n<\/div><div class='show-question-feedback' style='display:none;'><\/div><input type='button' class='showchecked' style='margin: 10px 0;' onclick='showanswer1(20,this)' id='btn-20' value='See Answer'  \/><input type='hidden' id='questionType20' value='radio' class=''><\/div><div class='watu-question' id='question-21'><div class='question-content'><p><strong>NEW QUESTION 45<\/strong><br \/>You are building a new data pipeline to share data between two different types of applications: jobs generators and job runners. Your solution must scale to accommodate increases in usage and must accommodate the addition of new applications without negatively affecting the performance of existing ones. What should you do?<\/p>\n<\/div><input type='hidden' name='question_id[]' value='13747' \/><div class='watu-questions-wrap '><input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='53963' \/><div class='watu-question-choice'><input type='radio' name='answer-13747[]' id='answer-id-53963' class='answer answer-21 js-answer-label answerof-13747' value='53963' \/>&nbsp;<label for='answer-id-53963' id='answer-label-53963' class='js-answer-label answer label-21'><span class='answer'>Create an API using App Engine to receive and send messages to the applications<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='53964' \/><div class='watu-question-choice'><input type='radio' name='answer-13747[]' id='answer-id-53964' class='answer answer-21 php-answer-label answerof-13747' value='53964' \/>&nbsp;<label for='answer-id-53964' id='answer-label-53964' class='php-answer-label answer label-21'><span class='answer'>Use a Cloud Pub\/Sub topic to publish jobs, and use subscriptions to execute them<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='53965' \/><div class='watu-question-choice'><input type='radio' name='answer-13747[]' id='answer-id-53965' class='answer answer-21 js-answer-label answerof-13747' value='53965' \/>&nbsp;<label for='answer-id-53965' id='answer-label-53965' class='js-answer-label answer label-21'><span class='answer'>Create a table on Cloud SQL, and insert and delete rows with the job information<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='53966' \/><div class='watu-question-choice'><input type='radio' name='answer-13747[]' id='answer-id-53966' class='answer answer-21 js-answer-label answerof-13747' value='53966' \/>&nbsp;<label for='answer-id-53966' id='answer-label-53966' class='js-answer-label answer label-21'><span class='answer'>Create a table on Cloud Spanner, and insert and delete rows with the job information<\/span><\/label><\/div>\n<\/div><div class='show-question-feedback' style='display:none;'>Pubsub is used to transmit data in real time and scale automatically.<\/div><input type='button' class='showchecked' style='margin: 10px 0;' onclick='showanswer1(21,this)' id='btn-21' value='See Answer'  \/><input type='hidden' id='questionType21' value='radio' class=''><\/div><div class='watu-question' id='question-22'><div class='question-content'><p><strong>NEW QUESTION 46<\/strong><br \/>You have developed three data processing jobs. One executes a Cloud Dataflow pipeline that transforms data uploaded to Cloud Storage and writes results to BigQuery. The second ingests data from on- premises servers and uploads it to Cloud Storage. The third is a Cloud Dataflow pipeline that gets information from third-party data providers and uploads the information to Cloud Storage. You need to be able to schedule and monitor the execution of these three workflows and manually execute them when needed. What should you do?<\/p>\n<\/div><input type='hidden' name='question_id[]' value='13748' \/><div class='watu-questions-wrap '><input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='53967' \/><div class='watu-question-choice'><input type='radio' name='answer-13748[]' id='answer-id-53967' class='answer answer-22 php-answer-label answerof-13748' value='53967' \/>&nbsp;<label for='answer-id-53967' id='answer-label-53967' class='php-answer-label answer label-22'><span class='answer'>Create a Direct Acyclic Graph in Cloud Composer to schedule and monitor the jobs.<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='53968' \/><div class='watu-question-choice'><input type='radio' name='answer-13748[]' id='answer-id-53968' class='answer answer-22 js-answer-label answerof-13748' value='53968' \/>&nbsp;<label for='answer-id-53968' id='answer-label-53968' class='js-answer-label answer label-22'><span class='answer'>Use Stackdriver Monitoring and set up an alert with a Webhook notification to trigger the jobs.<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='53969' \/><div class='watu-question-choice'><input type='radio' name='answer-13748[]' id='answer-id-53969' class='answer answer-22 js-answer-label answerof-13748' value='53969' \/>&nbsp;<label for='answer-id-53969' id='answer-label-53969' class='js-answer-label answer label-22'><span class='answer'>Develop an App Engine application to schedule and request the status of the jobs using GCP API calls.<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='53970' \/><div class='watu-question-choice'><input type='radio' name='answer-13748[]' id='answer-id-53970' class='answer answer-22 js-answer-label answerof-13748' value='53970' \/>&nbsp;<label for='answer-id-53970' id='answer-label-53970' class='js-answer-label answer label-22'><span class='answer'>Set up cron jobs in a Compute Engine instance to schedule and monitor the pipelines using GCP API calls.<\/span><\/label><\/div>\n<\/div><div class='show-question-feedback' style='display:none;'>Cloud composer is used to schedule the interdependent jobs.<\/div><input type='button' class='showchecked' style='margin: 10px 0;' onclick='showanswer1(22,this)' id='btn-22' value='See Answer'  \/><input type='hidden' id='questionType22' value='radio' class=''><\/div><div class='watu-question' id='question-23'><div class='question-content'><p><strong>NEW QUESTION 47<\/strong><br \/>Flowlogistic Case Study<br \/>Company Overview<br \/>Flowlogistic is a leading logistics and supply chain provider. They help businesses throughout the world manage their resources and transport them to their final destination. The company has grown rapidly, expanding their offerings to include rail, truck, aircraft, and oceanic shipping.<br \/>Company Background<br \/>The company started as a regional trucking company, and then expanded into other logistics market. Because they have not updated their infrastructure, managing and tracking orders and shipments has become a bottleneck. To improve operations, Flowlogistic developed proprietary technology for tracking shipments in real time at the parcel level. However, they are unable to deploy it because their technology stack, based on Apache Kafka, cannot support the processing volume. In addition, Flowlogistic wants to further analyze their orders and shipments to determine how best to deploy their resources.<br \/>Solution Concept<br \/>Flowlogistic wants to implement two concepts using the cloud:<br \/>* Use their proprietary technology in a real-time inventory-tracking system that indicates the location of their loads<br \/>* Perform analytics on all their orders and shipment logs, which contain both structured and unstructured data, to determine how best to deploy resources, which markets to expand info. They also want to use predictive analytics to learn earlier when a shipment will be delayed.<br \/>Existing Technical Environment<br \/>Flowlogistic architecture resides in a single data center:<br \/>* Databases<br \/>&#8211; 8 physical servers in 2 clusters<br \/>&#8211; SQL Server &#8211; user data, inventory, static data<br \/>&#8211; 3 physical servers<br \/>&#8211; Cassandra &#8211; metadata, tracking messages<br \/>10 Kafka servers &#8211; tracking message aggregation and batch insert<br \/>* Application servers &#8211; customer front end, middleware for order\/customs<br \/>&#8211; 60 virtual machines across 20 physical servers<br \/>&#8211; Tomcat &#8211; Java services<br \/>&#8211; Nginx &#8211; static content<br \/>&#8211; Batch servers<br \/>* Storage appliances<br \/>&#8211; iSCSI for virtual machine (VM) hosts<br \/>&#8211; Fibre Channel storage area network (FC SAN) &#8211; SQL server storage<br \/>Network-attached storage (NAS) image storage, logs, backups<br \/>* 10 Apache Hadoop \/Spark servers<br \/>&#8211; Core Data Lake<br \/>&#8211; Data analysis workloads<br \/>* 20 miscellaneous servers<br \/>&#8211; Jenkins, monitoring, bastion hosts,<br \/>Business Requirements<br \/>* Build a reliable and reproducible environment with scaled panty of production.<br \/>* Aggregate data in a centralized Data Lake for analysis<br \/>* Use historical data to perform predictive analytics on future shipments<br \/>* Accurately track every shipment worldwide using proprietary technology<br \/>* Improve business agility and speed of innovation through rapid provisioning of new resources<br \/>* Analyze and optimize architecture for performance in the cloud<br \/>* Migrate fully to the cloud if all other requirements are met<br \/>Technical Requirements<br \/>* Handle both streaming and batch data<br \/>* Migrate existing Hadoop workloads<br \/>* Ensure architecture is scalable and elastic to meet the changing demands of the company.<br \/>* Use managed services whenever possible<br \/>* Encrypt data flight and at rest<br \/>Connect a VPN between the production data center and cloud environment<br \/>SEO Statement<br \/>We have grown so quickly that our inability to upgrade our infrastructure is really hampering further growth and efficiency. We are efficient at moving shipments around the world, but we are inefficient at moving data around.<br \/>We need to organize our information so we can more easily understand where our customers are and what they are shipping.<br \/>CTO Statement<br \/>IT has never been a priority for us, so as our data has grown, we have not invested enough in our technology. I have a good staff to manage IT, but they are so busy managing our infrastructure that I cannot get them to do the things that really matter, such as organizing our data, building the analytics, and figuring out how to implement the CFO&#8217; s tracking technology.<br \/>CFO Statement<br \/>Part of our competitive advantage is that we penalize ourselves for late shipments and deliveries. Knowing where out shipments are at all times has a direct correlation to our bottom line and profitability. Additionally, I don&#8217;t want to commit capital to building out a server environment.<br \/>Flowlogistic&#8217;s management has determined that the current Apache Kafka servers cannot handle the data volume for their real-time inventory tracking system. You need to build a new system on Google Cloud Platform (GCP) that will feed the proprietary tracking software. The system must be able to ingest data from a variety of global sources, process and query in real-time, and store the data reliably. Which combination of GCP products should you choose?<\/p>\n<\/div><input type='hidden' name='question_id[]' value='13749' \/><div class='watu-questions-wrap '><input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='53971' \/><div class='watu-question-choice'><input type='radio' name='answer-13749[]' id='answer-id-53971' class='answer answer-23 js-answer-label answerof-13749' value='53971' \/>&nbsp;<label for='answer-id-53971' id='answer-label-53971' class='js-answer-label answer label-23'><span class='answer'>Cloud Pub\/Sub, Cloud Dataflow, and Cloud Storage<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='53972' \/><div class='watu-question-choice'><input type='radio' name='answer-13749[]' id='answer-id-53972' class='answer answer-23 js-answer-label answerof-13749' value='53972' \/>&nbsp;<label for='answer-id-53972' id='answer-label-53972' class='js-answer-label answer label-23'><span class='answer'>Cloud Pub\/Sub, Cloud Dataflow, and Local SSD<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='53973' \/><div class='watu-question-choice'><input type='radio' name='answer-13749[]' id='answer-id-53973' class='answer answer-23 php-answer-label answerof-13749' value='53973' \/>&nbsp;<label for='answer-id-53973' id='answer-label-53973' class='php-answer-label answer label-23'><span class='answer'>Cloud Pub\/Sub, Cloud SQL, and Cloud Storage<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='53974' \/><div class='watu-question-choice'><input type='radio' name='answer-13749[]' id='answer-id-53974' class='answer answer-23 js-answer-label answerof-13749' value='53974' \/>&nbsp;<label for='answer-id-53974' id='answer-label-53974' class='js-answer-label answer label-23'><span class='answer'>Cloud Load Balancing, Cloud Dataflow, and Cloud Storage<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='53975' \/><div class='watu-question-choice'><input type='radio' name='answer-13749[]' id='answer-id-53975' class='answer answer-23 js-answer-label answerof-13749' value='53975' \/>&nbsp;<label for='answer-id-53975' id='answer-label-53975' class='js-answer-label answer label-23'><span class='answer'>Cloud Dataflow, Cloud SQL, and Cloud Storage<\/span><\/label><\/div>\n<\/div><div class='show-question-feedback' style='display:none;'>Explanation<\/div><input type='button' class='showchecked' style='margin: 10px 0;' onclick='showanswer1(23,this)' id='btn-23' value='See Answer'  \/><input type='hidden' id='questionType23' value='radio' class=''><\/div><div class='watu-question' id='question-24'><div class='question-content'><p><strong>NEW QUESTION 48<\/strong><br \/>You are deploying a new storage system for your mobile application, which is a media streaming service.<br \/>You decide the best fit is Google Cloud Datastore. You have entities with multiple properties, some of which can take on multiple values. For example, in the entity &#8216;Movie&#8217;the property &#8216;actors&#8217;and the property &#8216;tags&#8217; have multiple values but the property &#8216;date released&#8217; does not. A typical query would ask for all movies with actor=&lt;actorname&gt;ordered by date_releasedor all movies with tag=Comedyordered by date_released. How should you avoid a combinatorial explosion in the number of indexes?<br \/><img decoding=\"async\" src=\"https:\/\/blog.examboosts.com\/wp-content\/uploads\/2023\/06\/professional-data-engineer-7d2a06ac5987a5038038bd702c5d8d80.jpg\"\/><br \/><img decoding=\"async\" src=\"https:\/\/blog.examboosts.com\/wp-content\/uploads\/2023\/06\/professional-data-engineer-083083c7ab17b9e62947658c3e2fede0.jpg\"\/><br \/>C: Set the following in your entity options: exclude_from_indexes = &#8216;actors, tags&#8217; D: Set the following in your entity options: exclude_from_indexes = &#8216;date_published&#8217;<\/p>\n<\/div><input type='hidden' name='question_id[]' value='13750' \/><div class='watu-questions-wrap '><input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='53976' \/><div class='watu-question-choice'><input type='radio' name='answer-13750[]' id='answer-id-53976' class='answer answer-24 php-answer-label answerof-13750' value='53976' \/>&nbsp;<label for='answer-id-53976' 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value='53979' \/><div class='watu-question-choice'><input type='radio' name='answer-13750[]' id='answer-id-53979' class='answer answer-24 js-answer-label answerof-13750' value='53979' \/>&nbsp;<label for='answer-id-53979' id='answer-label-53979' class='js-answer-label answer label-24'><span class='answer'>Option D<\/span><\/label><\/div>\n<\/div><div class='show-question-feedback' style='display:none;'><\/div><input type='button' class='showchecked' style='margin: 10px 0;' onclick='showanswer1(24,this)' id='btn-24' value='See Answer'  \/><input type='hidden' id='questionType24' value='radio' class=''><\/div><div style='display:none' id='question-25'><br \/><div class='question-content'><img loading=\"lazy\" decoding=\"async\" src=\"https:\/\/blog.examboosts.com\/wp-content\/plugins\/watu\/loading.gif\" width=\"16\" height=\"16\" alt=\"Loading ...\" title=\"Loading ...\" \/>&nbsp;Loading &#8230;<\/div><\/div><br \/>\n<input type=\"button\" name=\"action\" onclick=\"Watu.submitResult()\" 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textval == undefined){\n\t\t\/\/jQuery(\".hint\").stop().fadeIn(300)\n\t\talert('Please first answer the question');\n\t}\n}\nvar btnisshow = jQuery(\".php-answer-label\").length\nif (btnisshow > 0) {\n\tjQuery('.showchecked').show()\n} else {\n\tjQuery('.showchecked').hide()\n}\n<\/script>\n<h3>Exam Topics<\/h3>\n<p><strong>The syllabus of the Google Professional Data Engineer exam is divided into 4 topics, each covering specific knowledge and skills that the candidates need to develop while preparing for the test. A full outline of the exam content can be viewed on the official website. The highlights of the domains covered in the test are as follows:<\/strong><\/p>\n<p><strong>Topic 1. Designing Data Processing Systems<\/strong><\/p>\n<p>To answer the questions related to this first topic of the certification exam, the individuals need to demonstrate their proficiency in selecting the proper storage technologies. This includes their understanding of data modeling, schema design, distributed systems, as well as tradeoffs involving throughput, latency, and transactions. Moreover, the applicants need to have the ability to map storage systems to the business needs. It also measures one\u2019s skills in designing data pipelines, designing a data processing solution, as well as migrating data warehousing &amp; data processing. <\/p>\n<p><\/p>\n<h3>Understanding functional and technical aspects of Google Professional Data Engineer Exam Ensuring solution quality<\/h3>\n<p>The following will be discussed here:<\/p>\n<ul>\n<li>Legal compliance (e.g., Health Insurance Portability and Accountability Act (HIPAA), Children&#8217;s Online Privacy Protection Act (COPPA), FedRAMP, General Data Protection Regulation (GDPR))<\/li>\n<li>Assessing, troubleshooting, and improving data representations and data processing infrastructure<\/li>\n<li>Mapping to current and future business requirements<\/li>\n<li>Planning, executing, and stress testing data recovery (fault tolerance, rerunning failed jobs, performing retrospective re-analysis)<\/li>\n<li>Ensuring privacy (e.g., Data Loss Prevention API)<\/li>\n<li>Resizing and autoscaling resources<\/li>\n<li>Ensuring reliability and fidelity<\/li>\n<li>Data staging, cataloging, and discovery<\/li>\n<li>Ensuring flexibility and portability<\/li>\n<li>Designing for data and application portability (e.g., multi-cloud, data residency requirements)<\/li>\n<li>Choosing between ACID, idempotent, eventually consistent requirements<\/li>\n<li>Ensuring scalability and efficiency<\/li>\n<li>Data security (encryption, key management)<\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n<p><strong>Professional-Data-Engineer Dumps for Pass Guaranteed &#8211; Pass Professional-Data-Engineer Exam: <a href=\"https:\/\/www.examboosts.com\/Google\/Professional-Data-Engineer-practice-exam-dumps.html\" target=\"_blank\" rel=\"noopener\">https:\/\/www.examboosts.com\/Google\/Professional-Data-Engineer-practice-exam-dumps.html<\/a><\/strong><\/p>\n\n","protected":false},"excerpt":{"rendered":"<p>Verified Professional-Data-Engineer dumps Q&amp;As &#8211; Pass Guarantee Exam Dumps Test Engine [2023] Professional-Data-Engineer dumps and 270 unique questions Career Opportunities The certified individuals can explore a variety of job opportunities. Some of the positions that they can take up include a Software Engineer, a Cloud Architect, a Data Engineer, a Sales Engineer, a Data Scientist,&hellip; <br \/> <a class=\"button small blue\" href=\"https:\/\/blog.examboosts.com\/ko\/2023\/06\/q25-q48-verified-professional-data-engineer-dumps-qas-pass-guarantee-exam-dumps-test-engine-2023\/\">Read more<\/a><\/p>\n","protected":false},"author":1,"featured_media":1818,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[34,2534],"tags":[4796,4795,4798,4800,4801,4797,4799,4802],"class_list":["post-1817","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-google","category-professional-data-engineer","tag-professional-data-engineer-best-vce","tag-professional-data-engineer-certification-dump-free-download","tag-professional-data-engineer-exam-topic","tag-professional-data-engineer-latest-exam-tips","tag-professional-data-engineer-latest-test-format","tag-professional-data-engineer-new-test-camp-file","tag-professional-data-engineer-study-demo","tag-professional-data-engineer-sure-pass"],"_links":{"self":[{"href":"https:\/\/blog.examboosts.com\/ko\/wp-json\/wp\/v2\/posts\/1817","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/blog.examboosts.com\/ko\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/blog.examboosts.com\/ko\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/blog.examboosts.com\/ko\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/blog.examboosts.com\/ko\/wp-json\/wp\/v2\/comments?post=1817"}],"version-history":[{"count":0,"href":"https:\/\/blog.examboosts.com\/ko\/wp-json\/wp\/v2\/posts\/1817\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/blog.examboosts.com\/ko\/wp-json\/wp\/v2\/media\/1818"}],"wp:attachment":[{"href":"https:\/\/blog.examboosts.com\/ko\/wp-json\/wp\/v2\/media?parent=1817"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/blog.examboosts.com\/ko\/wp-json\/wp\/v2\/categories?post=1817"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/blog.examboosts.com\/ko\/wp-json\/wp\/v2\/tags?post=1817"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}