Expert Answers to NVIDIA Exam NCP-AAI Questions

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NVIDIA-Certified Professional NVIDIA Agentic AI

NVIDIA Agentic AI

Last Update Aug 7, 2026
Total Questions : 121

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Questions 2

You are rolling out a multimodal conversational agent on NVIDIA’s stack: the model is containerized as a TensorRT-LLM engine, served via Triton Inference Server behind NIM microservices for routing and scaling, and protected by NeMo Guardrails for safety and compliance. During early testing, end-to-end latency exceeds your target budget, and you need to tune batching, model precision, and guardrail checks while maintaining both throughput and enforcement of safety policies.

Which configuration change is most effective for reducing latency under these constraints while still enforcing NeMo Guardrails policies?

Options:

Quantize the TensorRT-LLM engine to FP16, tune Triton’s dynamic batching, and integrate NeMo Guardrails alongside inference to run policy checks in parallel.

Quantize the TensorRT-LLM engine to INT8, disable dynamic batching, and invoke Guardrails checks synchronously within the inference path.

Deploy separate Triton servers for model inference and guardrail validation, routing requests sequentially and merging outputs at the application layer.

Keep FP32 precision, increase batch size aggressively, and perform Guardrails checks in a downstream microservice after inference.

Discussion 0

Questions 3

In your RAG deployment, you’ve identified a performance bottleneck in the retrieval phase – specifically, the time it takes to access the vector database.

Which of the following optimization strategies is most aligned with micro-service best practices, considering your RAG architecture?

Options:

Implement a “cache-and-check” mechanism where the retrieval microservice immediately returns the first matching chunk, regardless of relevance.

Increase the size of the LLM model itself, because it will automatically accelerate the overall response time.

Introduce a dedicated service responsible solely for querying the vector database and returning relevant chunks.

Optimize the LLM prompt to be shorter and more concise, significantly reducing the computational load.

Discussion 0

Questions 4

When evaluating optimization opportunities between NeMo Guardrails, NIM microservices, and TensorRT-LLM in a production healthcare agent, which analysis approach best identifies optimization opportunities across the NVIDIA stack?

Options:

Conduct stress testing of individual microservices and guardrails to measure peak throughput and determine theoretical performance limits of each module.

Use default configurations to establish a deployment baseline, focusing on stability before conducting deeper performance profiling.

Create end-to-end latency waterfalls that capture guardrail overhead, NIM queuing delays, and TensorRT optimization benefits while assessing overall pipeline efficiency.

Tune each component individually, focusing primarily on local performance metrics with secondary attention to integration patterns.

Discussion 0

Questions 5

What is RAG Fusion primarily designed to achieve?

Options:

Creating a separate, dedicated database for storing all the retrieved chunks.

Minimizing the need for retrieval, allowing the LLM to generate responses directly from its internal knowledge.

Blending information from multiple retrieved chunks into a single response generated by the LLM.

Automatically translating and integrating all retrieved chunks into a single language.

Discussion 0

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