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| Certification Vendor: | NVIDIA |
|---|---|
| Exam Name: | NVIDIA Certified Professional - Agentic AI |
| Exam Number: | NCP-AAI |
| Exam Price: | $200 USD |
| Available Languages: | English |
| Exam Format: | Multiple select, Multiple choice |
| Real Exam Qty: | 60–70 |
| Certificate Validity Period: | 2 years |
| Related Certifications: | NVIDIA-Certified Associate: Generative AI LLM (NCA-GENL) NVIDIA-Certified Professional: AI Operations (NCP-AIO) NVIDIA-Certified Professional: AI Infrastructure (NCP-AII) |
| Exam Duration: | 120 minutes |
| Passing Score: | Not officially disclosed |
| Recommended Training: | NVIDIA Learning Path: Agentic AI Professional |
| Exam Registration: | NVIDIA Certification Portal Certiverse Exam Platform |
| Sample Questions: | NVIDIA NCP-AAI Sample Questions |
| Exam Way: | Online, remotely proctored; closed-book |
| Pre Condition: | 1–2 years experience in AI/ML roles; hands-on experience building or operating agentic AI systems; knowledge of LLM, orchestration, multi-agent design, and production AI deployment |
| Official Syllabus URL: | https://www.nvidia.com/en-us/learn/certification/agentic-ai-professional/ |
試験の概要は毎年新しいポリシーに基づいて変更され、NCP-AAI質問トレントおよびその他の教育用ソフトウェアは、新しい試験の概要の後、シラバスおよび理論と実践の最新の開発および改訂に従って変更されます対応する変更は、アウトラインに非常に同意します。 NCP-AAI試験問題は、教材の完全なセットの完璧な形です。教育概要は、カバーされているすべての知識ポイントの概要を網羅し、NCP-AAI候補者のデッドアングルは、毎年の提案範囲と傾向を示します。
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質問 # 27
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?
正解：D
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 stack-level anchor is clear: NVIDIA AI Enterprise deployments typically combine optimized containers, GPU Operator/DCGM visibility, and Kubernetes-native lifecycle management. The selected option specifically D states "Orchestrating agents using containerization platforms, combined with load balancing and ongoing performance monitoring", which matches the operational requirement rather than a superficial wording match. Container orchestration plus load balancing and monitoring creates a resilient serving plane. A single server may maximize utilization until it becomes the outage domain. The high-value engineering move is containerized services, HPA/cluster autoscaling, GPU- aware scheduling, health probes, rolling updates, and metric-driven capacity control. The distractors fail because bare-metal scripts can benchmark well once but are weak for failover, rollback, capacity changes, and fleet observability. Anything less would make the agent fragile when traffic, schemas, policies, or user behavior shift. GPU-aware scheduling and service-level metrics are essential because CPU utilization rarely predicts LLM inference saturation.
質問 # 28
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?
正解：A
解説：
Synthetic QA generation expands coverage across scenarios humans may not enumerate. It still needs validation, but it improves test breadth for RAG evaluation. The durable control mechanism is measurement of the whole agent path: prompt, retrieval, tool calls, reasoning steps, final answer, and user-facing outcome.
The selected option specifically D states "Synthetic generation allows for systematic testing of the RAG pipeline across a wider range of scenarios and query types.", which matches the operational requirement rather than a superficial wording match. Option D is the correct engineering choice because the requirement is not just "make the model answer," but control the execution surface. The alternatives would look simpler in a prototype, but aggregate metrics can hide the exact variant, time window, or complexity tier where the agent fails. In NVIDIA terms, Triton, Prometheus, GenAI-Perf, Nsight, and workflow traces give different slices of the same production behavior. For certification purposes, read the question as asking for controlled autonomy, not raw LLM creativity.
質問 # 29
When evaluating GPU utilization inefficiencies in deploying Llama Nemotron models across A100 and H100 clusters, which approaches help identify optimal resource allocation strategies? (Choose two.)
正解：A、B
解説：
The decisive point is failure isolation: the combination of Options B and D keeps the agent's decision path observable instead of burying behavior inside one prompt or one service. Together, B states "Profile resource utilization for each Nemotron variant and match models to appropriate GPU tiers."; D states "Assess concurrent execution capabilities by employing multi-instance GPU partitioning for varying workload types.", so the answer covers both sides of the requirement instead of solving only the model or only the infrastructure layer. Profiling each Nemotron variant and using MIG/concurrent execution where appropriate gives resource fit. Sending every workload to H100s wastes premium capacity. The runtime should therefore be built around matching model precision, batch windows, model instances, and GPU memory behavior to the latency service- level objective. The stack-level anchor is clear: TensorRT-LLM and NIM reduce inference overhead, but they still need serving-level tuning to avoid queue buildup under concurrency. The losing choices mostly optimize for short-term convenience; hardware upgrades alone do not fix poor batching, serial ensembles, guardrail overhead, or KV-cache pressure. The answer is therefore about engineered control planes, not simply model capability.
質問 # 30
In the context of agent development, how does an autonomous agent differ from a predefined workflow when applied to complex enterprise tasks?
正解：C
解説：
The implementation detail that matters is clear boundaries between planning, execution, validation, and escalation rather than one LLM attempting every responsibility. The decisive point is failure isolation: Option B keeps the agent's decision path observable instead of burying behavior inside one prompt or one service. A workflow is a known path with branches; an agent chooses actions as goals and environment feedback change. That distinction is the core cognition boundary in agentic architecture. The stack-level anchor is clear:
specialized agents can be served, evaluated, and replaced independently when their role or model changes.
The selected option specifically B states "Workflows provide deterministic task sequencing with conditional branching, while agents adapt decisions dynamically based on goals, context, and environment feedback.", which matches the operational requirement rather than a superficial wording match. The rejected options are weaker because single-loop agents and isolated workers collapse planning, memory, and validation into one failure domain, which is brittle under real-time enterprise load. That is the difference between an agent that works in a notebook and an agent that remains reliable in production.
質問 # 31
A Lead AI Architect at a global financial institution is designing a multi-agent fraud detection system using an agentic AI framework. The system must operate in real time, with distinct agents working collaboratively to monitor and analyze transactional patterns across accounts, retain and share contextual information over time, and escalate suspicious behaviors to a human fraud analyst when needed.
Which architectural approach enables intelligent specialization, shared memory, and inter-agent coordination in a dynamic and evolving threat environment?
正解：D
解説：
The selected option specifically A states "Design a modular multi-agent system where individual agents collaborate asynchronously using shared memory and structured messaging.", which matches the operational requirement rather than a superficial wording match. Fraud monitoring needs specialization: transaction monitors, pattern analysts, memory stores, and escalation agents. Asynchronous collaboration prevents one slow analytical path from blocking the entire detection fabric. Option A fits the operating model because the problem describes an agent that must remain adaptive under changing inputs and infrastructure conditions.
This lines up with NVIDIA guidance because NeMo Agent Toolkit is framework-agnostic and can orchestrate LangChain, CrewAI, LlamaIndex, Semantic Kernel, and custom Python agents behind a common workflow layer. The durable control mechanism is workflow graphs where agent responsibilities, inputs, and completion criteria are visible to both orchestration and evaluation layers. That is why the other options are traps: random routing or unstructured collaboration wastes specialization and makes coordination failures look like model hallucinations. For certification purposes, read the question as asking for controlled autonomy, not raw LLM creativity.
質問 # 32
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