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| Certification Vendor: | NVIDIA |
|---|---|
| Exam Name: | NVIDIA Certified Professional: Agentic AI |
| Exam Number: | NCP-AAI |
| Certificate Validity Period: | 2 years |
| Related Certifications: | NVIDIA Certified Professional: Generative AI LLMs |
| Exam Duration: | 120 minutes |
| Exam Price: | $200 USD |
| Passing Score: | Not officially disclosed (commonly referenced ~70%) |
| Real Exam Qty: | 60–70 |
| Exam Format: | Scenario-based, Multiple Response, Multiple Choice |
| Available Languages: | English |
| 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/ |
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NEW QUESTION # 112
When evaluating coordination failures in a multi-agent system managing distributed manufacturing workflows, which analysis approach best identifies state management and planning synchronization issues?
Answer: D
NEW QUESTION # 113
A company is deploying a multi-agent AI system to handle large-scale customer interactions. They want to ensure the system is highly available, cost-effective, and scalable across multiple NVIDIA GPUs using container orchestration tools.
Which practice is most crucial for successfully deploying and scaling an agentic AI system in production?
Answer: B
Explanation:
Option D is the right call because it gives the platform team levers to tune behavior without rewriting the entire agent loop. The selected option specifically D states "Implementing automated workload management and resource scheduling frameworks to optimize GPU utilization and maintain service availability.", which matches the operational requirement rather than a superficial wording match. Automated workload management assigns GPU capacity according to demand while preserving availability. Static request assignment cannot handle traffic skew or accelerator saturation. The runtime should therefore be built around asynchronous collaboration, state checkpoints, and topic-based communication so one blocked agent does not stall the whole workflow. Within the NVIDIA stack, multi-agent execution should expose traces for delegation, handoff, retries, and final task completion rather than treating the conversation as a black box. The losing choices mostly optimize for short-term convenience; centralized rules handle known paths but fail when the environment changes or when tasks need dynamic decomposition. The answer is therefore about engineered control planes, not simply model capability.
NEW QUESTION # 114
You're employing an LLM to automate the generation of email responses for a customer service team. The generated responses frequently miss the mark, failing to address the customer's underlying concerns.
What's the most crucial element to add to the prompt to enhance the quality of the email responses?
Answer: C
Explanation:
This is a lifecycle problem, not a wording problem, and Option A gives the team a controllable lifecycle for the agent behavior. A detailed response-composition prompt forces the model to address intent, structure, and tone. Vague "be helpful" language does not bind the output to the customer's actual concern. The runtime should therefore be built around a prompt contract that tells the model what to extract, which evidence to preserve, and what output format is valid. The selected option specifically A states "Instructing the LLM with a detailed prompt containing instructions on how to format and compose the response in an easy-to- understand structure.", which matches the operational requirement rather than a superficial wording match.
The alternatives would look simpler in a prototype, but asking for final accuracy alone hides whether the intermediate decomposition was valid. For a production build, prompt design is still an engineering control when it defines extraction targets, tool names, parameter examples, and evaluation rubrics. The answer is therefore about engineered control planes, not simply model capability.
NEW QUESTION # 115
When evaluating optimization opportunities between NeMo Guardrails, NIM microservices, and TensorRT- LLM in a production healthcare agent, which analysis approach best identifies optimization opportunities across the NVIDIA stack?
Answer: B
Explanation:
End-to-end latency waterfalls show where time is spent across guardrails, queues, and inference. Local component tuning misses cross-service overhead. The correct implementation surface is profiling the request path from ingress through guardrails, routing, Triton scheduling, TensorRT-LLM execution, and response assembly. The selected option specifically C states "Create end-to-end latency waterfalls that capture guardrail overhead, NIM queuing delays, and TensorRT optimization benefits while assessing overall pipeline efficiency.", which matches the operational requirement rather than a superficial wording match. From an NVIDIA systems-engineering lens, Option C aligns with the way agentic services should be decomposed and measured. The alternatives would look simpler in a prototype, but overlarge batches may improve throughput while violating interactive latency targets. The NVIDIA implementation angle is not cosmetic here: NVIDIA Perf Analyzer, GenAI-Perf, Nsight, and Triton metrics help isolate whether the bottleneck is batching, compute, memory, or request scheduling. This choice gives engineering teams the knobs they need for continuous tuning after deployment.
NEW QUESTION # 116
You are designing an AI agent for summarizing medical documents that include images and text as well. It must extract key information and recognize dates.
Which feature is most critical for ensuring the agent performs well across multiple input and output formats?
Answer: A
Explanation:
The selected option specifically D states "Multi-modal model integration to handle both text and vision inputs", which matches the operational requirement rather than a superficial wording match. The best answer is Option D when the design is judged by reliability, latency budget, auditability, and maintainability rather than demo simplicity. Operationally, the design depends on tool contracts that can be versioned, tested, and observed independently from the reasoning loop. Medical images and text require a model path that can encode vision and language. Guardrails and retries improve safety and reliability, but they do not create multimodal perception. 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. The stack-level anchor is clear: 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. It also creates clean evidence for audits, incident review, and root-cause analysis when behavior drifts.
NEW QUESTION # 117
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