[UPDATED 2024] Snowflake ARA-C01 Questions Prepare with Free Demo of PDF [Q43-Q62]

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[UPDATED 2024] Snowflake ARA-C01 Questions Prepare with Free Demo of PDF

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Snowflake ARA-C01 (SnowPro Advanced Architect Certification) Certification Exam is a certification program designed for IT professionals who want to demonstrate their expertise in architecting data solutions using Snowflake. ARA-C01 exam is intended for individuals who have already obtained the SnowPro Core or SnowPro Advanced certification and have experience working with Snowflake as a data warehousing platform.

NO.43 A company’s client application supports multiple authentication methods, and is using Okta.
What is the best practice recommendation for the order of priority when applications authenticate to Snowflake?

1) OAuth (either Snowflake OAuth or External OAuth)
2) External browser
3) Okta native authentication
4) Key Pair Authentication, mostly used for service account users
5) Password

1) External browser, SSO
2) Key Pair Authentication, mostly used for development environment users
3) Okta native authentication
4) OAuth (ether Snowflake OAuth or External OAuth)
5) Password

1) Okta native authentication
2) Key Pair Authentication, mostly used for production environment users
3) Password
4) OAuth (either Snowflake OAuth or External OAuth)
5) External browser, SSO

1) Password
2) Key Pair Authentication, mostly used for production environment users
3) Okta native authentication
4) OAuth (either Snowflake OAuth or External OAuth)
5) External browser, SSO

NO.44 The Data Engineering team at a large manufacturing company needs to engineer data coming from many sources to support a wide variety of use cases and data consumer requirements which include:
1) Finance and Vendor Management team members who require reporting and visualization
2) Data Science team members who require access to raw data for ML model development
3) Sales team members who require engineered and protected data for data monetization What Snowflake data modeling approaches will meet these requirements? (Choose two.)

Consolidate data in the company’s data lake and use EXTERNAL TABLES.

Create a raw database for landing and persisting raw data entering the data pipelines.

Create a set of profile-specific databases that aligns data with usage patterns.

Create a single star schema in a single database to support all consumers’ requirements.

Create a Data Vault as the sole data pipeline endpoint and have all consumers directly access the Vault.

These two approaches are recommended by Snowflake for data modeling in a data lake scenario. Creating a raw database allows the data engineering team to ingest data from various sources without any transformation or cleansing, preserving the original data quality and format. This enables the data science team to access the raw data for ML model development. Creating a set of profile-specific databases allows the data engineering team to apply different transformations and optimizations for different use cases and data consumer requirements. For example, the finance and vendor management team can access a dimensional database that supports reporting and visualization, while the sales team can access a secure database that supports data monetization.
Reference:
Snowflake Data Lake Architecture | Snowflake Documentation
Snowflake Data Lake Best Practices | Snowflake Documentation

NO.45 What are characteristics of Dynamic Data Masking? (Select TWO).

A masking policy that Is currently set on a table can be dropped.

A single masking policy can be applied to columns in different tables.

A masking policy can be applied to the value column of an external table.

The role that creates the masking policy will always see unmasked data In query results

A masking policy can be applied to a column with the GEOGRAPHY data type.

Dynamic Data Masking is a feature that allows masking sensitive data in query results based on the role of the user who executes the query. A masking policy is a user-defined function that specifies the masking logic and can be applied to one or more columns in one or more tables. A masking policy that is currently set on a table can be dropped using the ALTER TABLE command. A single masking policy can be applied to columns in different tables using the ALTER TABLE command with the SET MASKING POLICY clause. The other options are either incorrect or not supported by Snowflake. A masking policy cannot be applied to the value column of an external table, as external tables do not support column-level security. The role that creates the masking policy will not always see unmasked data in query results, as the masking policy can be applied to the owner role as well. A masking policy cannot be applied to a column with the GEOGRAPHY data type, as Snowflake only supports masking policies for scalar data types. Reference: Snowflake Documentation: Dynamic Data Masking, Snowflake Documentation: ALTER TABLE

NO.46 You ran the below query. I have a warehouse with auto suspend set at 5 seconds
SELECT * FROM INVENTORY;
The query profile looks like as below. Please see below ‘Percentage scanned from cache’ is 0%

You ran the query again before 5 seconds has elapsed and the query profile looks as below. Look at the ‘Percentage scanned for cache’, it is 75%

You ran the query again after 5 seconds. The query profile looks as below. Look at the ‘Percentage scanned from cache’, it is zero again.

Why is this happening?

The second run of the query used data cache to retrieve part of the result since it ran before the warehouse was suspended

The second run of the query used query result cache

The third run of the query used query result cache

NO.47 This privilege applies to only shared databases. It grants ability to enable roles other than the owning role to access a shared database.
Which is that role?

IMPORTED PRIVILEGES

SHARED PRIVILEGES

IMPORT SHARE

NO.48 Assuming all Snowflake accounts are using an Enterprise edition or higher, in which development and testing scenarios would be copying of data be required, and zero-copy cloning not be suitable? (Select TWO).

Developers create their own datasets to work against transformed versions of the live data.

Production and development run in different databases in the same account, and Developers need to see production-like data but with specific columns masked.

Data is in a production Snowflake account that needs to be provided to Developers in a separate development/testing Snowflake account in the same cloud region.

Developers create their own copies of a standard test database previously created for them in the development account, for their initial development and unit testing.

The release process requires pre-production testing of changes with data of production scale and complexity. For security reasons, pre-production also runs in the production account.

NO.49 Which of the below operations are allowed on an inbound share data?

MERGE

CREATE/DROP/ALTER TABLE

ALTER SCHEMA

SELECT WITH JOIN

SELECT WITH GROUP BY

INSERT INTO

NO.50 Which data models can be used when modeling tables in a Snowflake environment? (Select THREE).

Graph model

Dimensional/Kimball

Data lake

lnmon/3NF

Bayesian hierarchical model

Data vault

Snowflake is a cloud data platform that supports various data models for modeling tables in a Snowflake environment. The data models can be classified into two categories: dimensional and normalized. Dimensional data models are designed to optimize query performance and ease of use for business intelligence and analytics. Normalized data models are designed to reduce data redundancy and ensure data integrity for transactional and operational systems. The following are some of the data models that can be used in Snowflake:
Dimensional/Kimball: This is a popular dimensional data model that uses a star or snowflake schema to organize data into fact and dimension tables. Fact tables store quantitative measures and foreign keys to dimension tables. Dimension tables store descriptive attributes and hierarchies. A star schema has a single denormalized dimension table for each dimension, while a snowflake schema has multiple normalized dimension tables for each dimension. Snowflake supports both star and snowflake schemas, and allows users to create views and joins to simplify queries.
Inmon/3NF: This is a common normalized data model that uses a third normal form (3NF) schema to organize data into entities and relationships. 3NF schema eliminates data duplication and ensures data consistency by applying three rules: 1) every column in a table must depend on the primary key, 2) every column in a table must depend on the whole primary key, not a part of it, and 3) every column in a table must depend only on the primary key, not on other columns. Snowflake supports 3NF schema and allows users to create referential integrity constraints and foreign key relationships to enforce data quality.
Data vault: This is a hybrid data model that combines the best practices of dimensional and normalized data models to create a scalable, flexible, and resilient data warehouse. Data vault schema consists of three types of tables: hubs, links, and satellites. Hubs store business keys and metadata for each entity. Links store associations and relationships between entities. Satellites store descriptive attributes and historical changes for each entity or relationship. Snowflake supports data vault schema and allows users to leverage its features such as time travel, zero-copy cloning, and secure data sharing to implement data vault methodology.

NO.51 What is a characteristic of Role-Based Access Control (RBAC) as used in Snowflake?

Privileges can be granted at the database level and can be inherited by all underlying objects.

A user can use a “super-user” access along with securityadmin to bypass authorization checks and access all databases, schemas, and underlying objects.

A user can create managed access schemas to support future grants and ensure only schema owners can grant privileges to other roles.

A user can create managed access schemas to support current and future grants and ensure only object owners can grant privileges to other roles.

Role-Based Access Control (RBAC) is the Snowflake Access Control Framework that allows privileges to be granted by object owners to roles, and roles, in turn, can be assigned to users to restrict or allow actions to be performed on objects. A characteristic of RBAC as used in Snowflake is:
Privileges can be granted at the database level and can be inherited by all underlying objects. This means that a role that has a certain privilege on a database, such as CREATE SCHEMA or USAGE, can also perform the same action on any schema, table, view, or other object within that database, unless explicitly revoked. This simplifies the access control management and reduces the number of grants required.
A user can create managed access schemas to support future grants and ensure only schema owners can grant privileges to other roles. This means that a user can create a schema with the MANAGED ACCESS option, which changes the default behavior of object ownership and privilege granting within the schema. In a managed access schema, object owners lose the ability to grant privileges on their objects to other roles, and only the schema owner or a role with the MANAGE GRANTS privilege can do so. This enhances the security and governance of the schema and its objects.
The other options are not characteristics of RBAC as used in Snowflake:
A user can use a “super-user” access along with securityadmin to bypass authorization checks and access all databases, schemas, and underlying objects. This is not true, as there is no such thing as a “super-user” access in Snowflake. The securityadmin role is a predefined role that can manage users and roles, but it does not have any privileges on any database objects by default. To access any object, the securityadmin role must be explicitly granted the appropriate privilege by the object owner or another role with the grant option.
A user can create managed access schemas to support current and future grants and ensure only object owners can grant privileges to other roles. This is not true, as this contradicts the definition of a managed access schema. In a managed access schema, object owners cannot grant privileges on their objects to other roles, and only the schema owner or a role with the MANAGE GRANTS privilege can do so.
Reference:
Overview of Access Control
A Functional Approach For Snowflake’s Role-Based Access Controls
Snowflake Role-Based Access Control simplified
Snowflake RBAC security prefers role inheritance to role composition
Overview of Snowflake Role Based Access Control

NO.52 Which system functions does Snowflake provide to monitor clustering information within a table (Choose two.)

SYSTEM$CLUSTERING_INFORMATION

SYSTEM$CLUSTERING_USAGE

SYSTEM$CLUSTERING_DEPTH

SYSTEM$CLUSTERING_KEYS

SYSTEM$CLUSTERING_PERCENT

Explanation
According to the Snowflake documentation, these two system functions are provided by Snowflake to monitor clustering information within a table. A system function is a type of function that allows executing actions or returning information about the system. A clustering key is a feature that allows organizing data across micro-partitions based on one or more columns in the table. Clustering can improve query performance by reducing the number of files to scan.
* SYSTEM$CLUSTERING_INFORMATION is a system function that returns clustering information, including average clustering depth, for a table based on one or more columns in the table. The function takes a table name and an optional column name or expression as arguments, and returns a JSON string with the clustering information. The clustering information includes the cluster by keys, the total partition count, the total constant partition count, the average overlaps, and the average depth1.
* SYSTEM$CLUSTERING_DEPTH is a system function that returns the clustering depth for a table based on one or more columns in the table. The function takes a table name and an optional column name or expression as arguments, and returns an integer value with the clustering depth. The clustering depth is the maximum number of overlapping micro-partitions for any micro-partition in the table. A lower clustering depth indicates a better clustering2.
References:
* SYSTEM$CLUSTERING_INFORMATION | Snowflake Documentation
* SYSTEM$CLUSTERING_DEPTH | Snowflake Documentation

NO.53 With default settings, how long will a query run on snowflake

Snowflake will cancel the query if it runs more than 48 hours

Snowflake will cancel the query if it runs more than 24 hours

Snowflake will cancel the query if the warehouse runs out of memory

Snowflake will cancel the query if the warehouse runs out of memory and hard disk storage

NO.54 A Snowflake Architect is designing a multi-tenant application strategy for an organization in the Snowflake Data Cloud and is considering using an Account Per Tenant strategy.
Which requirements will be addressed with this approach? (Choose two.)

There needs to be fewer objects per tenant.

Security and Role-Based Access Control (RBAC) policies must be simple to configure.

Compute costs must be optimized.

Tenant data shape may be unique per tenant.

Storage costs must be optimized.

NO.55 Bytes spilled to remote storage in query profile indicates volume of data spilled to remote disk

TRUE

FALSE

NO.56 Remote service in external function can be an AWS Lambda function

TRUE

FALSE

NO.57 When activating Tri-Secret Secure in a hierarchical encryption model in a Snowflake account, at what level is the customer-managed key used?

At the root level (HSM)

At the account level (AMK)

At the table level (TMK)

At the micro-partition level

NO.58 Select the true statements about TASKS

TASKS cannot be triggered manually

TASKS can be scheduled for SQL execution

TASKS can be used for change data capture

TASKS can be used with STREAMS

NO.59 Snowflake includes administration settings for resource consumption in order to

Help control costs associated with unexpected credit usage of data

Manage access to data warehouses for specific users

Maintain data availability

NO.60 A table for IOT devices that measures water usage is created. The table quickly becomes large and contains more than 2 billion rows.

The general query patterns for the table are:
1. DeviceId, lOT_timestamp and Customerld are frequently used in the filter predicate for the select statement
2. The columns City and DeviceManuf acturer are often retrieved
3. There is often a count on Uniqueld
Which field(s) should be used for the clustering key?

lOT_timestamp

City and DeviceManuf acturer

Deviceld and Customerld

Uniqueld

NO.61 An Architect uses COPY INTO with the ON_ERROR=SKIP_FILE option to bulk load CSV files into a table called TABLEA, using its table stage. One file named file5.csv fails to load. The Architect fixes the file and re-loads it to the stage with the exact same file name it had previously.
Which commands should the Architect use to load only file5.csv file from the stage? (Choose two.)

COPY INTO tablea FROM @%tablea RETURN_FAILED_ONLY = TRUE;

COPY INTO tablea FROM @%tablea;

COPY INTO tablea FROM @%tablea FILES = (‘file5.csv’);

COPY INTO tablea FROM @%tablea FORCE = TRUE;

COPY INTO tablea FROM @%tablea NEW_FILES_ONLY = TRUE;

COPY INTO tablea FROM @%tablea MERGE = TRUE;

NO.62 An Architect is designing a file ingestion recovery solution. The project will use an internal named stage for file storage. Currently, in the case of an ingestion failure, the Operations team must manually download the failed file and check for errors.
Which downloading method should the Architect recommend that requires the LEAST amount of operational overhead?

Use the Snowflake Connector for Python, connect to remote storage and download the file.

Use the get command in SnowSQL to retrieve the file.

Use the get command in Snowsight to retrieve the file.

Use the Snowflake API endpoint and download the file.

The get command in SnowSQL is a convenient way to download files from an internal stage to a local directory. The get command can be used in interactive mode or in a script, and it supports wildcards and parallel downloads. The get command also allows specifying the overwrite option, which determines how to handle existing files with the same name2 The Snowflake Connector for Python, the Snowflake API endpoint, and the get command in Snowsight are not recommended methods for downloading files from an internal stage, because they require more operational overhead than the get command in SnowSQL. The Snowflake Connector for Python and the Snowflake API endpoint require writing and maintaining code to handle the connection, authentication, and file transfer. The get command in Snowsight requires using the web interface and manually selecting the files to download34 Reference:
1: SnowPro Advanced: Architect | Study Guide
2: Snowflake Documentation | Using the GET Command
3: Snowflake Documentation | Using the Snowflake Connector for Python
4: Snowflake Documentation | Using the Snowflake API
: Snowflake Documentation | Using the GET Command in Snowsight
: SnowPro Advanced: Architect | Study Guide
: Using the GET Command
: Using the Snowflake Connector for Python
: Using the Snowflake API
: [Using the GET Command in Snowsight]

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