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 #1

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

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

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

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

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

Discussion 0

Correct Answer: C Vote an answer

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Question #2

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

A. Sharing neural network literature

B. Reducing training time of neural networks

C. Compressing deep learning models

D. Representing deep learning models

Discussion 0

Correct Answer: D Vote an answer

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Question #3

Which of the following options describes best the NeMo Guardrails platform?

A. Ensuring the ethical use of artificial intelligence systems by monitoring and enforcing compliance with predefined rules and regulations.

B. Building advanced data factories for generative AI services in the context of language models.

C. Ensuring scalability and performance of large language models in pre-training and inference.

D. Developing and designing advanced machine learning models capable of interpreting and integrating various forms of data.

Discussion 0

Correct Answer: A Vote an answer

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Question #4

In transformer-based LLMs, how does the use of multi-head attention improve model performance compared to single-head attention, particularly for complex NLP tasks?

A. Multi-head attention simplifies the training process by reducing the number of parameters.

B. Multi-head attention reduces the model's memory footprint by sharing weights across heads.

C. Multi-head attention eliminates the need for positional encodings in the input sequence.

D. Multi-head attention allows the model to focus on multiple aspects of the input sequence simultaneously.

Discussion 0

Correct Answer: D Vote an answer

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Question #5

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. Retrieval-augmented generation with external mathematical databases.

B. Few-shot prompting with randomly selected examples.

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

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

Discussion 0

Correct Answer: D Vote an answer

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Question #6

Which principle of Trustworthy AI primarily concerns the ethical implications of AI's impact on society and includes considerations for both potential misuse and unintended consequences?

A. Certification

B. Data Privacy

C. Accountability

D. Legal Responsibility

Discussion 0

Correct Answer: C Vote an answer

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Question #7

In Natural Language Processing, there are a group of steps in problem formulation collectively known as word representations (also word embeddings). Which of the following are Deep Learning models that can be used to produce these representations for NLP tasks? (Choose two.)

A. WordNet

B. TensorRT

C. Kubernetes

D. BERT

E. Word2vec

Discussion 0

Correct Answer: D,E Vote an answer

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Question #8

Transformers are useful for language modeling because their architecture is uniquely suited for handling which of the following?

A. Embeddings

B. Translations

C. Long sequences

D. Class tokens

Discussion 0

Correct Answer: C Vote an answer

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Question #9

Which of the following is a feature of the NVIDIA Triton Inference Server?

A. Dynamic batching

B. Model quantization

C. Model pruning

D. Gradient clipping

Discussion 0

Correct Answer: A Vote an answer

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Question #10

Why is layer normalization important in transformer architectures?

A. To stabilize the learning process by adjusting the inputs across the features.

B. To encode positional information within the sequence.

C. To enhance the model's ability to generalize to new data.

D. To compress the model size for efficient storage.

Discussion 0

Correct Answer: A Vote an answer

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