Free NVIDIA NCA-GENL Exam Questions

Become NVIDIA Certified with updated NCA-GENL exam questions and correct answers

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Total 395 Questions | Updated On: Aug 18, 2026

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Question 1

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?

A : Containers automatically optimize the model's hyperparameters for better performance.

B : Containers encapsulate dependencies and configurations, ensuring consistent execution across systems.

C : Containers reduce the model's memory footprint by compressing the neural network.

D : Containers enable direct access to GPU hardware without driver installation.

Answer: B

Question 2

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?

A : Containers automatically optimize the model's hyperparameters for better performance.

B : Containers encapsulate dependencies and configurations, ensuring consistent execution across systems.

C : Containers reduce the model's memory footprint by compressing the neural network.

D : Containers enable direct access to GPU hardware without driver installation.

Answer: B

Question 3

What is the purpose of the NVIDIA NeMo Toolkit?

A : NeMo focuses on the morphology of a language by studying its words, and how they are formed.

B : NeMo helps researchers to develop models that trade-off size with minimum loss impact.

C : NeMo facilitates the creation of models for speech recognition and natural language understanding.

D : NeMo helps researchers develop state-of-the-art models for computer vision based on convolutions.

Answer: C

Question 4

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

A : To measure the importance of the words in the output sequence.

B : To determine the order in which words are generated.

C : To capture the order of the words in the input sequence.

D : To assign weights to each word in the input sequence.

Answer: D

Question 5

In the transformer architecture, what is the purpose of positional encoding?

A : To remove redundant information from the input sequence.

B : To encode the semantic meaning of each token in the input sequence.

C : To add information about the order of each token in the input sequence.

D : To encode the importance of each token in the input sequence.

Answer: C

Page: 1 / 79

Total 395 Questions | Updated On: Aug 18, 2026

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