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 #11

In the development of Trustworthy AI, what is the significance of 'Certification' as a principle?

A. It requires AI systems to be developed with an ethical consideration for societal impacts.

B. It involves verifying that AI models are fit for their intended purpose according to regional or industry- specific standards.

C. It mandates that AI models comply with relevant laws and regulations specific to their deployment region and industry.

D. It ensures that AI systems are transparent in their decision-making processes.

Discussion 0

Correct Answer: B Vote an answer

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Question #12

In the context of evaluating a fine-tuned LLM for a text classification task, which experimental design technique ensures robust performance estimation when dealing with imbalanced datasets?

A. Stratified k-fold cross-validation.

B. Grid search for hyperparameter tuning.

C. Single hold-out validation with a fixed test set.

D. Bootstrapping with random sampling.

Discussion 0

Correct Answer: A Vote an answer

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Question #13

Which calculation is most commonly used to measure the semantic closeness of two text passages?

A. Cosine similarity

B. Jaccard similarity

C. Euclidean distance

D. Hamming distance

Discussion 0

Correct Answer: A Vote an answer

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Question #14

When using NVIDIA RAPIDS to accelerate data preprocessing for an LLM fine-tuning pipeline, which specific feature of RAPIDS cuDF enables faster data manipulation compared to traditional CPU-based Pandas?

A. Conversion of Pandas DataFrames to SQL tables for faster querying.

B. Integration with cloud-based storage for distributed data access.

C. Automatic parallelization of Python code across CPU cores.

D. GPU-accelerated columnar data processing with zero-copy memory access.

Discussion 0

Correct Answer: D Vote an answer

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Question #15

You are in need of customizing your LLM via prompt engineering, prompt learning, or parameter-efficient fine-tuning. Which framework helps you with all of these?

A. NVIDIA TensorRT

B. NVIDIA NeMo

C. NVIDIA DALI

D. NVIDIA Triton

Discussion 0

Correct Answer: B Vote an answer

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Question #16

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

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

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

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

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

Discussion 0

Correct Answer: D Vote an answer

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Question #17

Why do we need positional encoding in transformer-based models?

A. To represent the order of elements in a sequence.

B. To reduce the dimensionality of the input data.

C. To increase the throughput of the model.

D. To prevent overfitting of the model.

Discussion 0

Correct Answer: A Vote an answer

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Question #18

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

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

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

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

Discussion 0

Correct Answer: A Vote an answer

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Question #19

In Exploratory Data Analysis (EDA) for Natural Language Understanding (NLU), which method is essential for understanding the contextual relationship between words in textual data?

A. Generating word clouds to visually represent word frequency and highlight key terms.

B. Computing the frequency of individual words to identify the most common terms in a text.

C. Creating n-gram models to analyze patterns of word sequences like bigrams and trigrams.

D. Applying sentiment analysis to gauge the overall sentiment expressed in a text.

Discussion 0

Correct Answer: C Vote an answer

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Question #20

You are using RAPIDS and Python for a data analysis project. Which pair of statements best explains how RAPIDS accelerates data science?

A. RAPIDS provides lossless compression of CPU-GPU memory transfers to speed up data analysis.

B. RAPIDS is a Python library that provides functions to accelerate the PCIe bus throughput via word- doubling.

C. RAPIDS enables on-GPU processing of computationally expensive calculations and minimizes CPU- GPU memory transfers.

Discussion 0

Correct Answer: C Vote an answer

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