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: Oct 26, 2026

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Question 1

When fine-tuning an LLM for a specific application, why is it essential to perform exploratory data analysis (EDA) on the new training dataset?

A : To uncover patterns and anomalies in the dataset

B : To select the appropriate learning rate for the model

C : To assess the computing resources required for fine-tuning

D : To determine the optimum number of layers in the neural network

Answer: A

Question 2

Which technology will allow you to deploy an LLM for production application?

A : Git

B : Pandas

C : Falcon

D : Triton

Answer: D

Question 3

Which of the following best describes Word2vec?

A : A programming language used to build artificial intelligence models

B : A statistical technique used to analyze word frequency in a text corpus.

C : A deep learning algorithm used to generate word embeddings from text data.

D : A database management system designed for storing and querying word data.

Answer: C

Question 4

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 5

In neural networks, the vanishing gradient problem refers to what problem or issue?

A : The problem of overfitting in neural networks, where the model performs well on the training data but poorly on new, unseen data.

B : The issue of gradients becoming too large during backpropagation, leading to unstable training.

C : The problem of underfitting in neural networks, where the model fails to capture the underlying patterns in the data.

D : The issue of gradients becoming too small during backpropagation, resulting in slow convergence or stagnation of the training process.

Answer: D

Page: 1 / 79

Total 395 Questions | Updated On: Oct 26, 2026

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