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| Certification Vendor: | NVIDIA |
|---|---|
| Exam Name: | NVIDIA Certified Professional: Agentic AI |
| Exam Number: | NCP-AAI |
| Exam Duration: | 120 minutes |
| Exam Price: | $200 USD |
| Related Certifications: | NVIDIA Certified Professional: Generative AI LLMs |
| Available Languages: | English |
| Certificate Validity Period: | 2 years |
| Exam Format: | Multiple Choice, Scenario-based, Multiple Response |
| Passing Score: | Not officially disclosed (commonly referenced ~70%) |
| Real Exam Qty: | 60–70 |
| Recommended Training: | NVIDIA Agentic AI Certification Page |
| Exam Registration: | NVIDIA Certification Portal |
| Sample Questions: | NVIDIA NCP-AAI Sample Questions |
| Exam Way: | Online, remotely proctored |
| Pre Condition: | Recommended: 1–2 years experience in AI/ML roles, familiarity with LLM APIs, agent frameworks, and production AI systems |
| Official Syllabus URL: | https://www.nvidia.com/en-us/learn/certification/agentic-ai-professional/ |
>> Valid NCP-AAI Exam Materials <<
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NEW QUESTION # 96
What is RAG Fusion primarily designed to achieve?
Answer: D
Explanation:
RAG Fusion improves generation by blending evidence from multiple retrieved chunks. It is about combining retrieved context, not eliminating retrieval. In a GPU-backed agent deployment, Option C maps closest to how the NVIDIA stack expects orchestration, inference, and control policies to be separated. The selected option specifically C states "Blending information from multiple retrieved chunks into a single response generated by the LLM.", which matches the operational requirement rather than a superficial wording match.
The correct implementation surface is retriever isolation, vector index quality, reranking, freshness-aware ingestion, query expansion, and retrieval guardrails. This lines up with NVIDIA guidance because NeMo Guardrails can add retrieval rails around RAG context, while the serving layer remains independent from the vector database. The distractors fail because keyword-only retrieval misses semantic matches, while unfiltered concatenation can pollute the answer with weak evidence. This choice gives engineering teams the knobs they need for continuous tuning after deployment. The retrieval layer should be independently measured for recall, relevance, freshness, and latency before blaming the generator.
NEW QUESTION # 97
An AI agent is being built to execute database queries, generate reports, and interact with cloud services.
Which design choice best improves long-term scalability and maintainability when adding new tools?
Answer: D
Explanation:
Option B is the right call because it gives the platform team levers to tune behavior without rewriting the entire agent loop. A plugin registry with uniform invocation keeps tools addable without rewriting core agent logic. Hardcoded tool branches become unmaintainable fast. The runtime should therefore be built around a tool boundary where every API has declared inputs, declared outputs, validation, retry behavior, and instrumentation. The selected option specifically B states "Using a plugin-based system with uniform tool registration and invocation", which matches the operational requirement rather than a superficial wording match. The alternatives would look simpler in a prototype, but relying on the model to infer API behavior invites fabricated endpoints, malformed arguments, and brittle production behavior. Within the NVIDIA stack, NVIDIA's agent tooling favors explicit function specifications and observable execution paths instead of free-form API narration in the prompt. The answer is therefore about engineered control planes, not simply model capability. Schema validation, typed return objects, and trace IDs also make post-incident debugging realistic when a third-party dependency changes behavior.
NEW QUESTION # 98
An autonomous vehicle company operates a multi-agent AI system across its fleet to process real-time sensor data, make driving decisions, and communicate with cloud infrastructure. The company needs fleet-wide monitoring to track GPU utilization, inference times, and memory usage, correlate performance with driving conditions and system load, and predict safety issues before they occur.
Which monitoring and observability approach would BEST meet these fleet-scale, safety-critical requirements?
Answer: A
Explanation:
Option A is the right call because it gives the platform team levers to tune behavior without rewriting the entire agent loop. Within the NVIDIA stack, Triton dynamic batching and model configuration are where throughput and tail latency tradeoffs become controllable. The selected option specifically A states "Deploy NVIDIA NIM microservices with Prometheus integration, NVIDIA Nsight Systems profiling, and Kubernetes-native monitoring to provide detailed metrics, profiling, and container orchestration observability across the entire stack.", which matches the operational requirement rather than a superficial wording match.
NIM, Prometheus, Nsight, and Kubernetes observability cover GPU, inference, and orchestration layers. That is the best NVIDIA-specific fleet monitoring answer. The runtime should therefore be built around dynamic batching, model instance tuning, concurrency control, precision optimization, KV-cache-aware LLM serving, and end-to-end latency waterfalls. The distractors fail because sequential microservices can add avoidable hops and tail latency even when every individual model looks fast. The answer is therefore about engineered control planes, not simply model capability.
NEW QUESTION # 99
When analyzing safety violations in a financial advisory agent that uses NeMo Guardrails, which evaluation approach best identifies gaps in guardrail coverage?
Answer: D
Explanation:
Coverage gaps appear under adversarial and observed-violation testing. Activation counts alone do not prove that the right policies fired. From an NVIDIA systems-engineering lens, Option B aligns with the way agentic services should be decomposed and measured. The selected option specifically B states "Analyze violation patterns, test adversarial prompts, measure guardrail activation, and align policies with observed failures.", which matches the operational requirement rather than a superficial wording match. The correct implementation surface is trajectory-level evaluation, distributed tracing, task-completion metrics, latency breakdowns, and regression gates. The NVIDIA implementation angle is not cosmetic here: NeMo Evaluator and agentic metrics focus on trajectories and goal completion, not only the fluency of the last response. The distractors fail because manual spot checks are useful but cannot replace regression tests across query classes, temporal drift, and tool failure modes. This choice gives engineering teams the knobs they need for continuous tuning after deployment. A strong evaluation setup must preserve both the trajectory and the final outcome so optimization does not improve one metric while damaging another.
NEW QUESTION # 100
An AI Engineer is experimenting with data retrieval performance within a RAG system.
Which of the following techniques is most likely to improve the quality of the retrieved chunks?
Answer: B
Explanation:
Query expansion with clarifying keywords and synonyms improves recall without abandoning relevance. A single keyword is usually too brittle for semantic retrieval. The durable control mechanism is a separated data plane where ingestion, indexing, retrieval, reranking, and generation can each be measured and updated. The selected option specifically A states "Adding clarifying keywords and synonyms to the original query to broaden the search.", which matches the operational requirement rather than a superficial wording match.
Option A fits the operating model because the problem describes an agent that must remain adaptive under changing inputs and infrastructure conditions. The alternatives would look simpler in a prototype, but synchronous monoliths make freshness and latency fight each other because indexing and generation cannot scale independently. This lines up with NVIDIA guidance because a production RAG workflow should treat the retriever as a measurable service, not as an invisible prelude to LLM generation. For certification purposes, read the question as asking for controlled autonomy, not raw LLM creativity.
NEW QUESTION # 101
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