NVIDIA NCA-GENL Actual Free Exam Questions & Community Discussion

Exam Code/Number: NCA-GENL
Exam Name/Title: NVIDIA Generative AI LLMs
Certification Provider: NVIDIA
Corresponding Certification: NVIDIA-Certified Associate

Exam Questions: 97
Updated On: Oct 03, 2026

Question #21

What is a Tokenizer in Large Language Models (LLM)?

A. A tool used to split text into smaller units called tokens for analysis and processing.

B. A machine learning algorithm that predicts the next word/token in a sequence of text.

C. A technique used to convert text data into numerical representations called tokens for machine learning.

D. A method to remove stop words and punctuation marks from text data.

Discussion 0

Correct Answer: A Vote an answer

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Question #22

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

A. To directly manage the hardware resources used by LLMs.

B. To act as a replacement for traditional programming languages.

C. To reduce the size of AI foundation models.

D. To orchestrate LLM components into complex workflows.

Discussion 0

Correct Answer: D Vote an answer

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Question #23

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

A. Pandas

B. HuggingFace Transformers

C. NumPy

D. Scikit-learn

Discussion 0

Correct Answer: B Vote an answer

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Question #24

What do we usually refer to as generative AI?

A. A branch of artificial intelligence that focuses on improving the efficiency of existing models.

B. A branch of artificial intelligence that focuses on auto generation of models for classification.

C. A branch of artificial intelligence that focuses on analyzing and interpreting existing data.

D. A branch of artificial intelligence that focuses on creating models that can generate new and original data.

Discussion 0

Correct Answer: D Vote an answer

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Question #25

Which of the following tasks is a primary application of XGBoost and cuML?

A. Data visualization and analysis

B. Inspecting, cleansing, and transforming data

C. Performing GPU-accelerated machine learning tasks

D. Training deep learning models

Discussion 0

Correct Answer: C Vote an answer

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Question #26

What is the purpose of few-shot learning in prompt engineering?

A. To fine-tune a model on a massive dataset

B. To optimize hyperparameters

C. To give a model some examples

D. To train a model from scratch

Discussion 0

Correct Answer: C Vote an answer

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Question #27

What is the main difference between forward diffusion and reverse diffusion in diffusion models of Generative AI?

A. Forward diffusion uses feed-forward networks, while reverse diffusion uses recurrent networks.

B. Forward diffusion focuses on generating a sample from a given noise vector, while reverse diffusion reverses the process by estimating the latent space representation of a given sample.

C. Forward diffusion uses bottom-up processing, while reverse diffusion uses top-down processing to generate samples from noise vectors.

D. Forward diffusion focuses on progressively injecting noise into data, while reverse diffusion focuses on generating new samples from the given noise vectors.

Discussion 0

Correct Answer: D Vote an answer

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Question #28

Which of the following is an activation function used in neural networks?

A. K-means clustering function

B. Diffusion function

C. Mean Squared Error function

D. Sigmoid function

Discussion 0

Correct Answer: D Vote an answer

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Question #29

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 select the appropriate learning rate for the model

B. To assess the computing resources required for fine-tuning

C. To determine the optimum number of layers in the neural network

D. To uncover patterns and anomalies in the dataset

Discussion 0

Correct Answer: D Vote an answer

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Question #30

Why might stemming or lemmatizing text be considered a beneficial preprocessing step in the context of computing TF-IDF vectors for a corpus?

A. It reduces the number of unique tokens by collapsing variant forms of a word into their root form, potentially decreasing noise in the data.

B. It guarantees an increase in the accuracy of TF-IDF vectors by ensuring more precise word usage distinction.

C. It increases the complexity of the dataset by introducing more unique tokens, enhancing the distinctiveness of each document.

D. It enhances the aesthetic appeal of the text, making it easier for readers to understand the document's content.

Discussion 0

Correct Answer: A Vote an answer

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