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 #41

When preprocessing text data for an LLM fine-tuning task, why is it critical to apply subword tokenization (e.
g., Byte-Pair Encoding) instead of word-based tokenization for handling rare or out-of-vocabulary words?

A. Subword tokenization reduces the model's computational complexity by eliminating embeddings.

B. Subword tokenization creates a fixed-size vocabulary to prevent memory overflow.

C. Subword tokenization removes punctuation and special characters to simplify text input.

D. Subword tokenization breaks words into smaller units, enabling the model to generalize to unseen words.

Discussion 0

Correct Answer: D Vote an answer

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Question #42

Which technique is designed to train a deep learning model by adjusting the weights of the neural network based on the error between the predicted and actual outputs?

A. Gradient Boosting

B. Backpropagation

C. K-means Clustering

D. Principal Component Analysis

Discussion 0

Correct Answer: B Vote an answer

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Question #43

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

A. cuDF

B. cuGraph

C. cuML

D. XGBoost

Discussion 0

Correct Answer: A Vote an answer

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Question #44

Which model deployment framework is used to deploy an NLP project, especially for high-performance inference in production environments?

A. NVIDIA Triton

B. NeMo

C. HuggingFace

D. NVIDIA DeepStream

Discussion 0

Correct Answer: A Vote an answer

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Question #45

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 capture the order of the words in the input sequence.

C. To assign weights to each word in the input sequence.

D. To determine the order in which words are generated.

Discussion 0

Correct Answer: C Vote an answer

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Question #46

Which aspect in the development of ethical AI systems ensures they align with societal values and norms?

A. Developing AI systems with autonomy from human decision-making.

B. Implementing complex algorithms to enhance AI's problem-solving capabilities.

C. Ensuring AI systems have explicable decision-making processes.

D. Achieving the highest possible level of prediction accuracy in AI models.

Discussion 0

Correct Answer: C Vote an answer

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Question #47

In the field of AI experimentation, what is the GLUE benchmark used to evaluate performance of?

A. AI models on a range of natural language understanding tasks.

B. AI models on speech recognition tasks.

C. AI models on image recognition tasks.

D. AI models on reinforcement learning tasks.

Discussion 0

Correct Answer: A Vote an answer

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Question #48

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

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

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

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

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

Discussion 0

Correct Answer: B Vote an answer

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Question #49

What is 'chunking' in Retrieval-Augmented Generation (RAG)?

A. A method used in RAG to generate random text.

B. A concept in RAG that refers to the training of large language models.

C. A technique used in RAG to split text into meaningful segments.

D. Rewrite blocks of text to fill a context window.

Discussion 0

Correct Answer: C Vote an answer

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Question #50

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 optimize the model's inference speed for production deployment.

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

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

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

Discussion 0

Correct Answer: B Vote an answer

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