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| Certification Vendor: | NVIDIA |
|---|---|
| Exam Name: | NVIDIA-Certified Professional: Agentic AI |
| Exam Number: | NCP-AAI |
| Exam Price: | $200 USD |
| Related Certifications: | NVIDIA AI Networking Professional NVIDIA AI Infrastructure Professional NVIDIA Generative AI LLM Associate |
| Exam Format: | Scenario-Based, Multiple Response, Multiple Choice |
| Exam Duration: | 120 minutes |
| Certificate Validity Period: | 2 years |
| Passing Score: | Not publicly disclosed |
| Real Exam Qty: | 60-70 |
| Available Languages: | English |
| Sample Questions: | NVIDIA NCP-AAI Sample Questions |
| Exam Way: | Online remotely proctored exam |
| Pre Condition: | Recommended 1-2 years of experience in AI/ML roles with hands-on experience in production-level agentic AI projects, multi-agent systems, orchestration, deployment, and evaluation. |
| Official Syllabus URL: | https://www.nvidia.com/en-us/learn/certification/agentic-ai-professional/ |
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質問 # 38
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?
正解：B
解説：
End-to-end latency waterfalls show where time is spent across guardrails, queues, and inference. Local component tuning misses cross-service overhead. The correct implementation surface is profiling the request path from ingress through guardrails, routing, Triton scheduling, TensorRT-LLM execution, and response assembly. The selected option specifically C states "Create end-to-end latency waterfalls that capture guardrail overhead, NIM queuing delays, and TensorRT optimization benefits while assessing overall pipeline efficiency.", which matches the operational requirement rather than a superficial wording match. From an NVIDIA systems-engineering lens, Option C aligns with the way agentic services should be decomposed and measured. The alternatives would look simpler in a prototype, but overlarge batches may improve throughput while violating interactive latency targets. The NVIDIA implementation angle is not cosmetic here: NVIDIA Perf Analyzer, GenAI-Perf, Nsight, and Triton metrics help isolate whether the bottleneck is batching, compute, memory, or request scheduling. This choice gives engineering teams the knobs they need for continuous tuning after deployment.
質問 # 39
A team is designing an AI assistant that helps users with travel planning. The assistant should remember user preferences, build personalized itineraries, and update plans when users provide new requirements.
Which approach best equips the AI assistant to provide personalized and adaptive travel recommendations?
正解：D
解説：
The NVIDIA implementation angle is not cosmetic here: long-running agents should retrieve compact relevant context instead of replaying the entire conversation history into every call. Travel personalization depends on persistent preferences and multi-step plan updates. A single-turn answerer cannot adapt itineraries as constraints change. From an NVIDIA systems-engineering lens, Option C aligns with the way agentic services should be decomposed and measured. The selected option specifically C states "Engineering multi- step reasoning frameworks with persistent memory systems to store and utilize user preferences.", which matches the operational requirement rather than a superficial wording match. The correct implementation surface is checkpointed state keyed by session or user, with schemas that preserve only the fields the workflow needs later. The losing choices mostly optimize for short-term convenience; unbounded memory creates privacy, relevance, and performance problems unless persistence is deliberate. This choice gives engineering teams the knobs they need for continuous tuning after deployment. The memory policy should define what is persisted, what is summarized, and what is discarded to avoid both context loss and prompt bloat.
質問 # 40
A development team is creating an AI assistant that interacts with employees to help manage schedules and tasks. The team wants to ensure users can easily provide feedback, understand the agent's decisions, and intervene when necessary to maintain control and trust.
Which practice best supports effective human oversight and interaction with the AI agent?
正解：C
解説：
The best answer is Option D when the design is judged by reliability, latency budget, auditability, and maintainability rather than demo simplicity. The selected option specifically D states "Designing intuitive user interfaces with integrated feedback loops and transparent explanations of agent decisions", which matches the operational requirement rather than a superficial wording match. Transparent UI plus feedback loops and explanation surfaces gives users control. Flexible commands alone do not create trust or intervention ability. The high-value engineering move is human checkpoints where domain experts can override, annotate, and feed corrections back into evaluation. The stack-level anchor is clear: the UI is part of the AI system because it determines whether users can inspect evidence and act before harm occurs. The losing choices mostly optimize for short-term convenience; a human-in-the-loop design fails if the human cannot intervene at the exact point where the decision matters. Anything less would make the agent fragile when traffic, schemas, policies, or user behavior shift.
質問 # 41
In a production agentic system handling thousands of concurrent conversations, which state management strategy provides optimal performance while ensuring context preservation?
正解：B
解説：
The rejected options are weaker because sending full history every turn inflates latency and cost, while stateless prompts lose unresolved tasks, user preferences, and multi-step plan continuity. Session-isolated state prevents concurrency collisions while lazy loading controls latency and memory footprint. Global locks are a scalability killer. Option B wins because it optimizes the system boundary around the risky component rather than hoping the base model behaves consistently. The selected option specifically B states "Session- isolated state with serialization and lazy loading", which matches the operational requirement rather than a superficial wording match. The NVIDIA implementation angle is not cosmetic here: memory is an orchestration concern as much as a model concern, because the agent must decide what to keep, retrieve, and forget. The durable control mechanism is a memory hierarchy that balances retrieval latency, relevance, privacy, and context-window cost. For certification purposes, read the question as asking for controlled autonomy, not raw LLM creativity. The memory policy should define what is persisted, what is summarized, and what is discarded to avoid both context loss and prompt bloat.
質問 # 42
An agent is tasked with solving a series of complex mathematical problems that require external tools to find information. It often struggles to keep track of intermediate steps and reasoning.
Which prompting technique would be MOST effective in improving the agent's clarity and reducing errors in its reasoning?
正解：D
解説：
ReAct is built for tool-using reasoning because each action is followed by an observation. That makes intermediate state visible and reduces arithmetic/tool-use drift. Option A is the correct engineering choice because the requirement is not just "make the model answer," but control the execution surface. The selected option specifically A states "ReAct", which matches the operational requirement rather than a superficial wording match. The durable control mechanism is schema-bound tool invocation, typed parameters, timeout envelopes, retry policy, and traceable function execution. In NVIDIA terms, the Agent Toolkit model is to expose tools as reusable workflow components; that is what makes multi-tool agents testable under schema changes. The distractors fail because embedding tools inside the agent loop makes security review, timeout handling, and version control unnecessarily difficult. For certification purposes, read the question as asking for controlled autonomy, not raw LLM creativity. Schema validation, typed return objects, and trace IDs also make post-incident debugging realistic when a third-party dependency changes behavior.
質問 # 43
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