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| Certification Vendor: | NVIDIA |
|---|---|
| Exam Name: | NVIDIA-Certified Professional: Agentic AI |
| Exam Number: | NCP-AAI |
| Real Exam Qty: | 60-70 |
| Passing Score: | Not publicly disclosed |
| Certificate Validity Period: | 2 years |
| Related Certifications: | NVIDIA Generative AI LLM Associate NVIDIA AI Infrastructure Professional NVIDIA AI Networking Professional |
| Available Languages: | English |
| Exam Format: | Multiple Choice, Scenario-Based, Multiple Response |
| Exam Price: | $200 USD |
| Exam Duration: | 120 minutes |
| 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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NEW QUESTION # 18
You're managing an agentic AI responsible for customer support ticket triage. The agent has been consistently accurate in routing tickets to the appropriate departments. However, a team leader has noticed a significant increase in the number of tickets requiring "escalation" - cases where the agent initially misclassified a complex issue as a simple, routine one, leading to delays and frustrated customers.
What would be an appropriate first step in resolving this issue?
Answer: A
Explanation:
Escalation drift starts in decision criteria. Before changing autonomy or reward functions, inspect classification logic, feature cues, and examples that trigger "routine" versus "complex." Option A wins because it optimizes the system boundary around the risky component rather than hoping the base model behaves consistently. The selected option specifically A states "Analyzing the agent's decision-making process, focusing on the specific criteria it uses to classify tickets, and identifying potential biases or blind spots.", 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. The NVIDIA implementation angle is not cosmetic here: 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.
NEW QUESTION # 19
An enterprise wants their AI agent to support complex project management tasks. The agent should remember ongoing project details, adjust its plans based on new information, and break down large goals into actionable steps.
Which strategy best enables the AI agent to autonomously decompose tasks and adapt to new Information over time?
Answer: A
Explanation:
For this scenario, Option B is defensible because it exposes the control plane that a senior engineer can test, scale, and harden. Within the NVIDIA stack, NVIDIA's agent tooling expects state, tools, and model calls to be separable so memory can be persisted without recompiling the model. The selected option specifically B states "Developing long-term knowledge retention strategies and dynamic state management for adaptive planning", which matches the operational requirement rather than a superficial wording match. Project management needs dynamic state and long-term knowledge retention. Static workflows cannot adapt when priorities, dependencies, or deadlines shift. Operationally, the design depends on session-local working memory, persistent profile/history stores, vector recall, selective checkpointing, and summarization
/compression policies. The distractors fail because global shared state creates concurrency hazards, while tiny rolling windows silently discard important commitments. It also creates clean evidence for audits, incident review, and root-cause analysis when behavior drifts. The memory policy should define what is persisted, what is summarized, and what is discarded to avoid both context loss and prompt bloat.
NEW QUESTION # 20
You're working with an LLM to automatically summarize research papers. The summaries often omit critical findings.
What's the best way to ensure that the summaries accurately reflect the core insights of the research papers?
Answer: A
Explanation:
The selected option specifically D states "Asking the LLM to "extract the key findings."", which matches the operational requirement rather than a superficial wording match. "Extract key findings" forces the model to privilege claims, methods, results, and conclusions. Generic summarization tends to compress prose while dropping the very facts the user needs. From an NVIDIA systems-engineering lens, Option D aligns with the way agentic services should be decomposed and measured. The NVIDIA implementation angle is not cosmetic here: TensorRT-LLM compiles optimized LLM engines; Triton schedules inference, exposes model metrics, and supports ensembles across multiple backends and modalities. The correct implementation surface is optimizing the multimodal ensemble as a pipeline, not as disconnected text, image, and audio models. That is why the other options are traps: a single model instance per GPU is rarely a complete answer because utilization depends on request shape, modality, and concurrency. This choice gives engineering teams the knobs they need for continuous tuning after deployment.
NEW QUESTION # 21
When evaluating an agent's degrading response times under increasing load, which analysis approach most effectively identifies scalability bottlenecks and optimization opportunities?
Answer: B
Explanation:
Distributed tracing plus GPU profiling shows where load creates queueing, memory pressure, or blocked tool calls. Average response time alone hides the bottleneck. 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 "Profile each major system stage using distributed tracing, analyze GPU utilization with NVIDIA performance tools, and map queuing delays against varying workload patterns.", which matches the operational requirement rather than a superficial wording match. The practical pattern 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 is exactly where NVIDIA's stack is strongest: separating acceleration, orchestration, policy, and observability.
NEW QUESTION # 22
An AI Engineer is analyzing a production agentic AI system's compliance with responsible AI standards.
Which evaluation approaches effectively identify potential safety vulnerabilities and ethical risks in multi- agent workflows? (Choose two.)
Answer: C,D
Explanation:
Operationally, the design depends on guardrail coverage that is tested against observed failures and adversarial prompts rather than assumed from policy text. For this scenario, the combination of Options B and D is defensible because it exposes the control plane that a senior engineer can test, scale, and harden. Audit trails, semantic policy checks, bias metrics, and adversarial tests expose ethical and safety risk. Latency is operational, not sufficient for responsible AI evaluation. Within the NVIDIA stack, Guardrails are most effective when paired with evaluation, red-team prompts, and audit metadata so coverage gaps become visible. Together, B states "Implement comprehensive audit trails using NVIDIA NeMo Guardrails with semantic similarity checks, tracking agent decisions across conversation flows and evaluating policy violations through automated compliance scoring."; D states "Deploy multi-layered evaluation combining bias detection metrics (demographic parity, equalized odds) with adversarial testing to probe agent responses for harmful outputs across diverse user populations", so the answer covers both sides of the requirement instead of solving only the model or only the infrastructure layer. The rejected options are weaker because keyword filters and one-time prompt disclaimers do not enforce policy under prompt injection, ambiguous requests, or regulated-domain escalation paths. It also creates clean evidence for audits, incident review, and root-cause analysis when behavior drifts.
NEW QUESTION # 23
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