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| Certification Vendor: | NVIDIA |
|---|---|
| Exam Name: | NVIDIA Certified Professional - Agentic AI |
| Exam Number: | NCP-AAI |
| Related Certifications: | NVIDIA-Certified Professional: AI Infrastructure (NCP-AII) NVIDIA-Certified Professional: AI Operations (NCP-AIO) NVIDIA-Certified Associate: Generative AI LLM (NCA-GENL) |
| Passing Score: | Not officially disclosed |
| Real Exam Qty: | 60–70 |
| Available Languages: | English |
| Exam Format: | Multiple choice, Multiple select |
| Exam Duration: | 120 minutes |
| Exam Price: | $200 USD |
| Certificate Validity Period: | 2 years |
| 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/ |
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NEW QUESTION # 117
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: C
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 # 118
After deploying a financial assistant agent, users report occasional inconsistencies in how transactions are categorized.
What is the best first step for diagnosing the issue?
Answer: A
Explanation:
The runtime should therefore be built around a memory hierarchy that balances retrieval latency, relevance, privacy, and context-window cost. This is a lifecycle problem, not a wording problem, and Option D gives the team a controllable lifecycle for the agent behavior. Transaction categorization depends on tool inputs and outputs. Before retraining, inspect recent traces to see whether the model received incorrect or incomplete structured data. For a production build, memory is an orchestration concern as much as a model concern, because the agent must decide what to keep, retrieve, and forget. The selected option specifically D states
"Review tool call inputs and outputs in recent session logs", which matches the operational requirement rather than a superficial wording match. 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. The answer is therefore about engineered control planes, not simply model capability.
NEW QUESTION # 119
In designing an AI workflow which of the following best describes a comprehensive approach to improving the performance of AI agents?
Answer: A
Explanation:
Agent improvement is iterative: benchmark, collect feedback, tune, regress-test, repeat. Monitoring token speed alone misses reasoning quality and task completion. The architecture implied by Option B is the one that survives real workloads: separate responsibilities, explicit contracts, and measurable runtime behavior.
The selected option specifically B states "Implementing benchmarking pipelines, collecting user feedback, and tuning model parameters iteratively", 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. In NVIDIA terms, 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 # 120
When evaluating a customer service agent's resilience to API failures and network issues, which analysis methods effectively identify weaknesses in error handling and retry mechanisms? (Choose two.)
Answer: A,C
Explanation:
Together, A states "Analyze retry logic for exponential backoff patterns, retry limits, and circuit breaker integration to prevent cascading failures in distributed systems."; E states "Conduct failure injection testing with varied error types (timeouts, rate limits, malformed responses) while monitoring recovery patterns and fallback behavior.", so the answer covers both sides of the requirement instead of solving only the model or only the infrastructure layer. Retry analysis and failure injection expose whether the agent handles timeout, rate-limit, and malformed-response paths. Normal-condition tests are insufficient. In a GPU-backed agent deployment, the combination of Options A and E maps closest to how the NVIDIA stack expects orchestration, inference, and control policies to be separated. This lines up with NVIDIA guidance because NeMo Agent Toolkit treats agents, tools, and workflows as composable functions, so tool-calling agents can choose from names, descriptions, and schemas rather than guessed endpoints. The correct implementation surface is tool contracts that can be versioned, tested, and observed independently from the reasoning loop.
That is why the other options are traps: manual tool wiring scales poorly as the catalog grows and usually fails silently when a vendor updates parameters or response fields. This choice gives engineering teams the knobs they need for continuous tuning after deployment.
NEW QUESTION # 121
A recently deployed Agentic AI system designed for automated incident response within a cloud infrastructure has been consistently failing to identify and resolve 'high-priority' alerts - specifically, those related to increased CPU utilization across several virtual machines. Initial logs show the agent is primarily focusing on alerts with related network traffic spikes, ignoring the CPU metrics.
What is the most appropriate initial step for a senior Agentic AI engineer to take to resolve this issue, considering the system's reliance on benchmarking and iterative improvement?
Answer: D
Explanation:
Operationally, the design depends on observability that captures decision paths, failed calls, queueing delay, and quality regressions under realistic load. The best answer is Option A when the design is judged by reliability, latency budget, auditability, and maintainability rather than demo simplicity. The first move is benchmark review, because the system is optimizing what it is measured on. If CPU alerts were underrepresented, threshold tuning alone treats a symptom. The stack-level anchor is clear: proper maintenance compares agent versions with stable inputs and preserved traces so teams can detect regressions before rollout. The selected option specifically A states "Review the agent's evaluation framework, focusing on the defined benchmarks used to assess its response efficiency and impact on overall system performance.", which matches the operational requirement rather than a superficial wording match. The rejected options are weaker because averages, anecdotal reviews, and final-answer-only scoring miss coordination errors, hidden retries, stale tools, and user-visible quality regressions. It also creates clean evidence for audits, incident review, and root-cause analysis when behavior drifts.
NEW QUESTION # 122
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