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| Certification Vendor: | NVIDIA |
|---|---|
| Exam Name: | NVIDIA Certified Professional - Agentic AI |
| Exam Number: | NCP-AAI |
| Certificate Validity Period: | 2 years |
| Exam Price: | $200 USD |
| Passing Score: | Not officially disclosed |
| Exam Duration: | 120 minutes |
| Related Certifications: | NVIDIA-Certified Associate: Generative AI LLM (NCA-GENL) NVIDIA-Certified Professional: AI Operations (NCP-AIO) NVIDIA-Certified Professional: AI Infrastructure (NCP-AII) |
| Available Languages: | English |
| Real Exam Qty: | 60–70 |
| Exam Format: | Multiple choice, Multiple select |
| 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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질문 # 78
A company plans to launch a multi-agent system that must serve thousands of users simultaneously. The team needs to ensure the system remains reliable, scales efficiently as demand increases, and operates in a cost- effective manner.
Which approach is most effective for achieving robust and scalable deployment of an agentic AI system in production?
정답：B
설명：
The best answer is Option D when the design is judged by reliability, latency budget, auditability, and maintainability rather than demo simplicity. The stack-level anchor is clear: NVIDIA AI Enterprise deployments typically combine optimized containers, GPU Operator/DCGM visibility, and Kubernetes-native lifecycle management. The selected option specifically D states "Orchestrating agents using containerization platforms, combined with load balancing and ongoing performance monitoring", which matches the operational requirement rather than a superficial wording match. Container orchestration plus load balancing and monitoring creates a resilient serving plane. A single server may maximize utilization until it becomes the outage domain. The high-value engineering move is containerized services, HPA/cluster autoscaling, GPU- aware scheduling, health probes, rolling updates, and metric-driven capacity control. The distractors fail because bare-metal scripts can benchmark well once but are weak for failover, rollback, capacity changes, and fleet observability. Anything less would make the agent fragile when traffic, schemas, policies, or user behavior shift. GPU-aware scheduling and service-level metrics are essential because CPU utilization rarely predicts LLM inference saturation.
질문 # 79
Your deployed legal assistant shows great performance but occasionally repeats incorrect legal terms.
Which tuning method best improves factual reliability?
정답：D
설명：
The decisive point is failure isolation: Option D keeps the agent's decision path observable instead of burying behavior inside one prompt or one service. The stack-level anchor is clear: the Agent Toolkit model is to expose tools as reusable workflow components; that is what makes multi-tool agents testable under schema changes. The selected option specifically D states "Add fact-checking steps using external tools during generation", which matches the operational requirement rather than a superficial wording match. Factual reliability improves when generation is checked against external authoritative sources. Hardcoded snippets age badly and do not generalize across legal contexts. The implementation detail that matters is schema-bound tool invocation, typed parameters, timeout envelopes, retry policy, and traceable function execution. The distractors fail because embedding tools inside the agent loop makes security review, timeout handling, and version control unnecessarily difficult. That is the difference between an agent that works in a notebook and an agent that remains reliable in production.
질문 # 80
A company operates agent-based workloads in multiple data centers. They want to minimize latency for users in different regions, maintain continuous service during infrastructure upgrades, and keep operational costs predictable.
Which deployment practice best supports low-latency, resilient, and cost-efficient agent operations at scale?
정답：D
설명：
The decisive point is failure isolation: Option B keeps the agent's decision path observable instead of burying behavior inside one prompt or one service. Geo-distribution reduces user latency; rolling updates preserve service during upgrades; resource monitoring keeps cost predictable. Scheduled downtime violates the requirement. The implementation detail that matters is measurement of the whole agent path: prompt, retrieval, tool calls, reasoning steps, final answer, and user-facing outcome. The selected option specifically B states "Implement geo-distributed deployments with rolling updates and resource usage monitoring.", which matches the operational requirement rather than a superficial wording match. The alternatives would look simpler in a prototype, but aggregate metrics can hide the exact variant, time window, or complexity tier where the agent fails. The stack-level anchor is clear: Triton, Prometheus, GenAI-Perf, Nsight, and workflow traces give different slices of the same production behavior. That is the difference between an agent that works in a notebook and an agent that remains reliable in production.
질문 # 81
When evaluating coordination failures in a multi-agent system managing distributed manufacturing workflows, which analysis approach best identifies state management and planning synchronization issues?
정답：C
설명：
The rejected options are weaker because single-loop agents and isolated workers collapse planning, memory, and validation into one failure domain, which is brittle under real-time enterprise load. Coordination failures are temporal failures. You need transition timing, state visibility, and message-path analysis, not just local agent output review. Option B wins because it optimizes the system boundary around the risky component rather than hoping the base model behaves consistently. The selected option specifically B states "Deploy distributed state tracing across agents, analyze transition timing, study communication overhead, and verify synchronization accuracy.", which matches the operational requirement rather than a superficial wording match. The NVIDIA implementation angle is not cosmetic here: specialized agents can be served, evaluated, and replaced independently when their role or model changes. That matters because clear boundaries between planning, execution, validation, and escalation rather than one LLM attempting every responsibility. The result is a system that can be benchmarked, traced, and revised without destabilizing the whole agent fabric.
질문 # 82
A technology startup is preparing to launch an AI agent platform to serve clients with unpredictable usage patterns. They face periods of high user activity and low demand, so their deployment approach must minimize wasted resources during slow times and automatically allocate more resources during busy periods
- all while keeping operational costs reasonable.
Given these requirements, which deployment strategy most effectively ensures both cost-effectiveness and adaptability for scaling agentic AI systems?
정답：A
설명：
Autoscaling is the only choice that handles unknown usage without paying for peak capacity all day. Manual monthly analysis reacts after the cost or outage already happened. Option D fits the operating model because the problem describes an agent that must remain adaptive under changing inputs and infrastructure conditions.
The selected option specifically D states "Implementing autoscaling policies in a container orchestration environment to automatically adjust resources according to workload changes", which matches the operational requirement rather than a superficial wording match. That matters because containerized services, HPA/cluster autoscaling, GPU-aware scheduling, health probes, rolling updates, and metric-driven capacity control. This lines up with NVIDIA guidance because NVIDIA AI Enterprise deployments typically combine optimized containers, GPU Operator/DCGM visibility, and Kubernetes-native lifecycle management. The distractors fail because bare-metal scripts can benchmark well once but are weak for failover, rollback, capacity changes, and fleet observability. The result is a system that can be benchmarked, traced, and revised without destabilizing the whole agent fabric.
질문 # 83
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