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NVIDIA NCP-AAI : Agentic AI

Exam Code: NCP-AAI

Exam Name: Agentic AI

Updated: Nov 09, 2026

Q & A: 123 Questions and Answers

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NVIDIA NCP-AAI Exam Syllabus Topics:

TopicDetails
Topic 1
  • Human-AI Interaction and Oversight: Focuses on designing systems that enable effective human supervision, control, and collaboration with AI agents.
Topic 2
  • Safety, Ethics, and Compliance: Covers the principles and practices needed to ensure agents operate responsibly, ethically, and within legal and regulatory requirements.
Topic 3
  • Deployment and Scaling: Covers operationalizing agentic systems for production use, including containerization, orchestration, and scaling strategies.
Topic 4
  • Run, Monitor, and Maintain: Addresses the ongoing operation, health monitoring, and routine maintenance of agentic systems after deployment.
Topic 5
  • Agent Development: Focuses on the practical building, integration, and enhancement of agents using tools, frameworks, and APIs.
Topic 6
  • NVIDIA Platform Implementation: Focuses on leveraging NVIDIA's AI hardware and software stack to build and optimize agentic AI systems.

Reference: https://www.nvidia.com/en-us/learn/certification/agentic-ai-professional/

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NVIDIA Agentic AI Sample Questions:

Question #1

You've deployed an agent that helps users troubleshoot technical issues with their devices. After several weeks in production, user feedback indicates a decline in response accuracy, especially for newer issues.
Which monitoring method is most appropriate for identifying the root cause of declining agent performance?

  • A. Schedule a weekly re-deployment cycle to reset the model and improve freshness
  • B. Review output token counts across sessions to detect unusual model behavior
  • C. Compare average prompt length over time to analyze common input patterns
  • D. Analyze logs of tool usage frequency and error rates during inference
Reveal Solution  Discussion  0

Correct Answer: D  🗳️

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Question #2

A company plans to launch a multi-agent system that must serve thousands of users simultaneously. The team needs to ensure the system remains reliable, scales efficiently as demand increases, and operates in a cost- effective manner.
Which approach is most effective for achieving robust and scalable deployment of an agentic AI system in production?

  • A. Establishing a continuous monitoring framework to track system performance and adapt resources as usage patterns evolve
  • B. Deploying all agents on a single server with ongoing performance monitoring to maximize hardware utilization
  • C. Orchestrating agents using containerization platforms, combined with load balancing and ongoing performance monitoring
  • D. Running agents without load balancing to reduce infrastructure complexity and achieve robust and scalable deployment of an agentic system
Reveal Solution  Discussion  0

Correct Answer: C  🗳️

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Question #3

You are using an LLM-as-a-Judge to evaluate a RAG pipeline.
What is the primary benefit of synthetically generating question-answer pairs, rather than relying solely on human-created test cases?

  • A. Synthetic generation eliminates the need for any human validation of the RAG pipeline's output.
  • B. Synthetic generation allows for systematic testing of the RAG pipeline across a wider range of scenarios and query types.
  • C. Synthetically generated answers are inherently more accurate than those produced by the LLM.
  • D. Synthetically generated questions are more challenging and reveal deeper flaws in the RAG pipeline.
Reveal Solution  Discussion  0

Correct Answer: B  🗳️

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Question #4

An AI Engineer has deployed a multi-agent system to manage supply chain logistics. Stakeholders request greater insight into how the agents decide on actions across tasks.
Which approach would best improve decision transparency without modifying the underlying model architecture?

  • A. Record a step-by-step reasoning log throughout each agent workflow
  • B. Gather structured user evaluations after each completed subtask
  • C. Generate visual summaries of attention patterns for every decision
  • D. Retain and share the full sequence of task instructions with stakeholders
Reveal Solution  Discussion  0

Correct Answer: A  🗳️

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Question #5

When analyzing throughput bottlenecks in a multi-modal agent processing text, images, and audio, which Triton configuration evaluations identify optimization opportunities? (Choose two.)

  • A. Deploy each modality on separate Triton instances, allowing Triton to automatically manage ensemble coordination, shared memory usage, and pipeline integration.
  • B. Analyze model ensemble pipelines for sequential dependencies, identify parallelization opportunities, and optimize inter-model data transfer using Triton's scheduler.
  • C. Profile GPU memory allocation patterns across modalities, implement model instance batching strategies, and tune concurrency limits to maximize utilization.
  • D. Use a single model instance per GPU, allowing Triton to automatically optimize concurrency, batching, and multi-instance settings for throughput scaling.
Reveal Solution  Discussion  0

Correct Answer: B,C  🗳️

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