100% Pass Top-selling NCA-GENL Exams – New 2026 NVIDIA Pratice Exam [Q21-Q35]

QUESTION 21
What is Retrieval Augmented Generation (RAG)?

QUESTION 22
Which metric is commonly used to evaluate machine-translation models?

QUESTION 23
What is the fundamental role of LangChain in an LLM workflow?

QUESTION 24
In the context of developing an AI application using NVIDIA’s NGC containers, how does the use of containerized environments enhance the reproducibility of LLM training and deployment workflows?

QUESTION 25
Which Python library is specifically designed for working with large language models (LLMs)?

QUESTION 26
What statement best describes the diffusion models in generative AI?

QUESTION 27
In large-language models, what is the purpose of the attention mechanism?

QUESTION 28
You are in need of customizing your LLM via prompt engineering, prompt learning, or parameter-efficient fine-tuning. Which framework helps you with all of these?

QUESTION 29
Which of the following optimizations are provided by TensorRT? (Choose two.)

QUESTION 30
You have access to training data but no access to test data. What evaluation method can you use to assess the performance of your AI model?

QUESTION 31
When implementing data parallel training, which of the following considerations needs to be taken into account?

QUESTION 32
In the context of evaluating a fine-tuned LLM for a text classification task, which experimental design technique ensures robust performance estimation when dealing with imbalanced datasets?

QUESTION 33
Why do we need positional encoding in transformer-based models?

QUESTION 34
In Natural Language Processing, there are a group of steps in problem formulation collectively known as word representations (also word embeddings). Which of the following are Deep Learning models that can be used to produce these representations for NLP tasks? (Choose two.)

QUESTION 35
Which of the following principles are widely recognized for building trustworthy AI? (Choose two.)