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| Certification Vendor: | NVIDIA |
|---|---|
| Exam Name: | NVIDIA-Certified Professional: Agentic AI |
| Exam Number: | NCP-AAI |
| Exam Duration: | 120 minutes |
| Certificate Validity Period: | 2 years |
| Related Certifications: | NVIDIA AI Infrastructure Professional NVIDIA AI Networking Professional NVIDIA Generative AI LLM Associate |
| Real Exam Qty: | 60-70 |
| Exam Format: | Scenario-Based, Multiple Choice, Multiple Response |
| Available Languages: | English |
| Passing Score: | Not publicly disclosed |
| Exam Price: | $200 USD |
| 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 # 20
Which memory architecture is most appropriate for an agent that must track conversation flow and remember user preferences across multiple interactions?
Answer: D
Explanation:
The runtime should therefore be built around a memory hierarchy that balances retrieval latency, relevance, privacy, and context-window cost. The decisive point is failure isolation: Option C keeps the agent's decision path observable instead of burying behavior inside one prompt or one service. Short-term and long-term memory have different latency and retention requirements. A hierarchy avoids mixing conversational scratchpad with durable preferences. The stack-level anchor is clear: 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 C states "Hierarchical memory with separate short-term and long-term layers", 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. 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 # 21
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: D,E
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 # 22
Which two deployment patterns are MOST suitable for scaling agentic workloads on NVIDIA Infrastructure?
(Choose two.)
Answer: B,E
Explanation:
Together, D states "Containerized deployment with NIM (NVIDIA Inference Microservices)"; E states
"Kubernetes orchestration with Horizontal Pod Autoscaling (HPA)", so the answer covers both sides of the requirement instead of solving only the model or only the infrastructure layer. At production scale, the combination of Options D and E preserves separability between reasoning, state, tools, and runtime operations. Operationally, the design depends on independent scaling of agent components so embeddings, reranking, reasoning, and guardrails do not share one rigid capacity pool. NIM containers package optimized inference services, and Kubernetes HPA scales them. Bare metal and fixed VMs remove the elasticity needed for agent workloads. That is why the other options are traps: CPU-only or memory-only scaling signals rarely capture the saturation profile of GPU-backed LLM inference. For a production build, NIM microservices and the NIM Operator fit Kubernetes production operations; Triton provides serving primitives and Prometheus- exportable inference metrics for GPUs and models. It also creates clean evidence for audits, incident review, and root-cause analysis when behavior drifts.
NEW QUESTION # 23
You're evaluating the performance of a tool-using agent (e.g., one that issues API calls or executes functions).
From the list below, what are two important features to evaluate? (Choose two.)
Answer: A,B
Explanation:
The runtime should therefore be built around wrappers that convert messy external services into stable functions with bounded latency and predictable failure semantics. the combination of Options A and D is the right call because it gives the platform team levers to tune behavior without rewriting the entire agent loop.
For tool agents, the two decisive signals are whether the correct tool was chosen and whether the task completed. Tokens per second is infrastructure performance, not agent competence. Within the NVIDIA stack, tool execution should sit behind adapters that can be profiled and regression-tested just like retrieval and inference services. Together, A states "Tool use accuracy"; D states "Task completion rate", 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 hardcoded endpoints, loose parsers, or monolithic handlers turn every API change into an application release and hide failures from observability. The answer is therefore about engineered control planes, not simply model capability.
NEW QUESTION # 24
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: C
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 # 25
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2026 Latest Exam4Tests NCP-AAI PDF Dumps and NCP-AAI Exam Engine Free Share: https://drive.google.com/open?id=1I_8S5T0nWvn5bY8JgLFiJucEgXeW9oPH