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Free NVIDIA GenAI LLM Associate Practice Test (2026) - NVIDIA Generative AI LLM Associate Questions

NVIDIA GenAI LLM Associate Practice Test

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Questions updated at Jul 10, 2026, 12:01 AM CDT

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Question 1 of 10

Objective NCA-GENL-5.1 Safety, Governance, and Responsible AI

A chatbot must block unsafe requests and keep responses within approved policies. Which NVIDIA capability is aligned?

Concept tested: Safety, Governance, and Responsible AI (NCA-GENL-5.1)

Source:NVIDIA NeMo Guardrails

Question 2 of 10

Objective NCA-GENL-6.3 Experimentation and Evaluation

A RAG team is tasked with evaluating the quality of their retrieval and generated answers. What evaluation design is MOST appropriate for assessing both retrieval relevance and answer faithfulness?

Concept tested: Experimentation and Evaluation (NCA-GENL-6.3)

Question 3 of 10

Objective NCA-GENL-2.5 Prompting and Adaptation

A team wants to adapt behavior with trainable virtual tokens while keeping the base model weights frozen. Which method is being described?

Concept tested: Prompting and Adaptation (NCA-GENL-2.5)

Question 4 of 10

Objective NCA-GENL-4.1 Deployment and Inference

A team wants a packaged way to deploy optimized inference for a foundation model with a standard API. Which NVIDIA offering fits?

Concept tested: Deployment and Inference (NCA-GENL-4.1)

Question 5 of 10

Objective NCA-GENL-3.1 RAG and Knowledge Integration

A RAG prototype has ingestion, embeddings, vector search, and a generator. What is the retrieval stage responsible for?

Concept tested: RAG and Knowledge Integration (NCA-GENL-3.1)

Question 6 of 10

Objective NCA-GENL-1.1 LLM Fundamentals

A chatbot generates responses one token at a time, iteratively refining its output based on the prompt and previously generated tokens. What LLM concept best describes this behavior?

Concept tested: LLM Fundamentals (NCA-GENL-1.1)

Question 7 of 10

Objective NCA-GENL-5.6 Safety, Governance, and Responsible AI

A company has deployed an AI assistant that occasionally produces inaccurate responses. A user reports unreliable performance. Which action should the company take to address this issue and ensure responsible AI usage?

Concept tested: Safety, Governance, and Responsible AI (NCA-GENL-5.6)

Question 8 of 10

Objective NCA-GENL-6.2 Experimentation and Evaluation

After releasing an AI assistant service, a team needs to monitor its performance and identify potential issues. Which set of metrics is MOST essential for ongoing operational oversight?

Concept tested: Experimentation and Evaluation (NCA-GENL-6.2)

Question 9 of 10

Objective NCA-GENL-2.1 Prompting and Adaptation

A team needs responses in strict JSON so a downstream service can parse them. Which prompt practice should come first?

Concept tested: Prompting and Adaptation (NCA-GENL-2.1)

Question 10 of 10

Objective NCA-GENL-4.6 Deployment and Inference

A serving team wants to speed generation by having a smaller model draft tokens that the larger model verifies. Which technique is this?

Concept tested: Deployment and Inference (NCA-GENL-4.6)

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Question 1 A chatbot must block unsafe requests and keep responses within approved policies. Which NVIDIA capability is aligned?

Answer choices

A. A larger temperature value for every answer
B. NVIDIA NeMo Guardrails for controlling inputs, outputs, and dialog flow
C. A folder of screenshots with no checks
D. A policy to trust every prompt

Correct answer

NVIDIA NeMo Guardrails for controlling inputs, outputs, and dialog flow

The relevant concept is NeMo Guardrails. NVIDIA NeMo Guardrails for controlling inputs, outputs, and dialog flow is aligned with enforcing safety and behavior boundaries. Higher temperature, screenshots, and trusting every prompt do not provide guardrail controls.

Wrong-answer review

A. A larger temperature value for every answer: A larger temperature value for every answer is not appropriate here because higher temperature can increase variation rather than safety.
C. A folder of screenshots with no checks: A folder of screenshots with no checks is not appropriate here because screenshots without checks do not enforce behavior.
D. A policy to trust every prompt: A policy to trust every prompt is not appropriate here because prompts can be malicious or unsafe and should not be trusted blindly.

Objective/domain: Safety, Governance, and Responsible AI (NCA-GENL-5.1)

Question 2 A RAG team is tasked with evaluating the quality of their retrieval and generated answers. What evaluation design is MOST appropriate for assessing both retrieval relevance and answer faithfulness?

A. Only a speed test of the login page
B. A RAG evaluation workflow covering retrieval relevance and answer faithfulness
C. A count of how many documents exist
D. A rule that citations are never reviewed

A RAG evaluation workflow covering retrieval relevance and answer faithfulness

Objective/domain: Experimentation and Evaluation (NCA-GENL-6.3)

Source: NVIDIA NIM

Question 3 A team wants to adapt behavior with trainable virtual tokens while keeping the base model weights frozen. Which method is being described?

A. Manual prompt typing with no training
B. Prompt tuning with learned virtual tokens
C. Full pretraining from random initialization
D. Database sharding for storage capacity

Prompt tuning with learned virtual tokens

Objective/domain: Prompting and Adaptation (NCA-GENL-2.5)

Source: NVIDIA NeMo PEFT Module Documentation

Question 4 A team wants a packaged way to deploy optimized inference for a foundation model with a standard API. Which NVIDIA offering fits?

A. A handwritten notebook cell run by one developer
B. A public wiki page with no serving endpoint
C. A spreadsheet formula for every token
D. NVIDIA NIM microservices for model inference deployment

NVIDIA NIM microservices for model inference deployment

Objective/domain: Deployment and Inference (NCA-GENL-4.1)

Question 5 A RAG prototype has ingestion, embeddings, vector search, and a generator. What is the retrieval stage responsible for?

A. Training the GPU driver from scratch
B. Finding relevant chunks to place into the model context
C. Changing the company billing account
D. Replacing every source document with a slogan

Finding relevant chunks to place into the model context

Objective/domain: RAG and Knowledge Integration (NCA-GENL-3.1)

Source: NVIDIA RAG Blueprint Documentation

Question 6 A chatbot generates responses one token at a time, iteratively refining its output based on the prompt and previously generated tokens. What LLM concept best describes this behavior?

A. Batch database replication
B. Static keyword replacement
C. Autoregressive next-token generation
D. Manual rule-table lookup

Autoregressive next-token generation

Objective/domain: LLM Fundamentals (NCA-GENL-1.1)

Source: NVIDIA NeMo Framework: Positional Embeddings

Question 7 A company has deployed an AI assistant that occasionally produces inaccurate responses. A user reports unreliable performance. Which action should the company take to address this issue and ensure responsible AI usage?

A. That the system is always perfect
B. Nothing about AI involvement
C. The system’s AI nature, limitations, and appropriate use boundaries
D. That citations are forbidden

The system’s AI nature, limitations, and appropriate use boundaries

Objective/domain: Safety, Governance, and Responsible AI (NCA-GENL-5.6)

Source: Trustworthy AI For A Better World

Question 8 After releasing an AI assistant service, a team needs to monitor its performance and identify potential issues. Which set of metrics is MOST essential for ongoing operational oversight?

A. Latency, error rate, throughput, cost, and quality indicators
B. Only the number of office chairs
C. The color of the release notes
D. Whether every user has the same initials

Latency, error rate, throughput, cost, and quality indicators

Objective/domain: Experimentation and Evaluation (NCA-GENL-6.2)

Source: AI Trust Center

Question 9 A team needs responses in strict JSON so a downstream service can parse them. Which prompt practice should come first?

A. Provide clear format instructions and examples of the required output shape
B. Ask the model to be as creative as possible
C. Avoid mentioning the expected structure
D. Increase randomness so each response differs

Provide clear format instructions and examples of the required output shape

Objective/domain: Prompting and Adaptation (NCA-GENL-2.1)

Source: Generative AI with LLMs Certification

Question 10 A serving team wants to speed generation by having a smaller model draft tokens that the larger model verifies. Which technique is this?

A. Speculative decoding with a draft model and target model
B. Manual proofreading before every token
C. A vector database backup policy
D. A rule to disable all batching

Speculative decoding with a draft model and target model

Objective/domain: Deployment and Inference (NCA-GENL-4.6)

NVIDIA GenAI LLM Associate Practice Test

NVIDIA GenAI LLM Associate

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A chatbot must block unsafe requests and keep responses within approved policies. Which NVIDIA capability is aligned?

A RAG team is tasked with evaluating the quality of their retrieval and generated answers. What evaluation design is MOST appropriate for assessing both retrieval relevance and answer faithfulness?

A team wants to adapt behavior with trainable virtual tokens while keeping the base model weights frozen. Which method is being described?

A team wants a packaged way to deploy optimized inference for a foundation model with a standard API. Which NVIDIA offering fits?

A RAG prototype has ingestion, embeddings, vector search, and a generator. What is the retrieval stage responsible for?

A chatbot generates responses one token at a time, iteratively refining its output based on the prompt and previously generated tokens. What LLM concept best describes this behavior?

A company has deployed an AI assistant that occasionally produces inaccurate responses. A user reports unreliable performance. Which action should the company take to address this issue and ensure responsible AI usage?

After releasing an AI assistant service, a team needs to monitor its performance and identify potential issues. Which set of metrics is MOST essential for ongoing operational oversight?

A team needs responses in strict JSON so a downstream service can parse them. Which prompt practice should come first?

A serving team wants to speed generation by having a smaller model draft tokens that the larger model verifies. Which technique is this?

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