Free NVIDIA NCA-GENL Practice Test & Real Exam Questions

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: Jun 27, 2026

Question #21

In the context of fine-tuning LLMs, which of the following metrics is most commonly used to assess the performance of a fine-tuned model?

A. Accuracy on a validation set

B. Number of layers

C. Model size

D. Training duration

Discussion 0

Correct Answer: A Vote an answer

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Question #22

In ML applications, which machine learning algorithm is commonly used for creating new data based on existing data?

A. Support vector machine

B. Generative adversarial network

C. Decision tree

D. K-means clustering

Discussion 0

Correct Answer: B Vote an answer

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Question #23

In the development of trustworthy AI systems, what is the primary purpose of implementing red-teaming exercises during the alignment process of large language models?

A. To identify and mitigate potential biases, safety risks, and harmful outputs.

B. To increase the model's parameter count for better performance.

C. To optimize the model's inference speed for production deployment.

D. To automate the collection of training data for fine-tuning.

Discussion 0

Correct Answer: A Vote an answer

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Question #24

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

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

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

Discussion 0

Correct Answer: A Vote an answer

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Question #25

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

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

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

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

Discussion 0

Correct Answer: B Vote an answer

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Question #26

What is the Open Neural Network Exchange (ONNX) format used for?

A. Reducing training time of neural networks

B. Sharing neural network literature

C. Representing deep learning models

D. Compressing deep learning models

Discussion 0

Correct Answer: C Vote an answer

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Question #27

Which library is used to accelerate data preparation operations on the GPU?

A. XGBoost

B. cuDF

C. cuML

D. cuGraph

Discussion 0

Correct Answer: B Vote an answer

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Question #28

Which of the following contributes to the ability of RAPIDS to accelerate data processing? (Pick the 2 correct responses)

A. Using the GPU for parallel processing of data.

B. Ensuring that CPUs are running at full clock speed.

C. Enabling data processing to scale to multiple GPUs.

D. Providing more memory for data analysis.

E. Subsampling datasets to provide rapid but approximate answers.

Discussion 0

Correct Answer: A,C Vote an answer

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Question #29

How can Retrieval Augmented Generation (RAG) help developers to build a trustworthy AI system?

A. RAG can generate responses that cite reference material from an external knowledge base, ensuring transparency and verifiability.

B. RAG can enhance the security features of AI systems, ensuring confidential computing and encrypted traffic.

C. RAG can improve the energy efficiency of AI systems, reducing their environmental impact and cooling requirements.

D. RAG can align AI models with one another, improving the accuracy of AI systems through cross- checking.

Discussion 0

Correct Answer: A Vote an answer

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Question #30

When designing prompts for a large language model to perform a complex reasoning task, such as solving a multi-step mathematical problem, which advanced prompt engineering technique is most effective in ensuring robust performance across diverse inputs?

A. Few-shot prompting with randomly selected examples.

B. Zero-shot prompting with a generic task description.

C. Chain-of-thought prompting with step-by-step reasoning examples.

D. Retrieval-augmented generation with external mathematical databases.

Discussion 0

Correct Answer: C Vote an answer

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