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NVIDIA NCP-AAI Exam
Agentic AI (Page 4 )

Updated On: 7-Feb-2026
Page 4 of 26
PDF with 121 Questions

A senior AI architect at a public electricity utility is designing an AI system to automate grid operations such as outage detection, load balancing, and escalation handling. The system involves multiple intelligent agents that must operate concurrently, respond to changing data in real time, and collaborate on tasks that evolve over multiple interaction steps. The architect must choose a design pattern that supports coordination, flexible task delegation, and responsiveness without sacrificing maintainability.

Which design approach is most appropriate for this scenario?

  1. Use an agent service architecture with decoupled execution units managed by a shared interface layer that handles communication and task routing.
  2. Build a rule-driven control structure that maps task flows to predefined paths for fast and efficient execution under known operating conditions.
  3. Design the system as a stepwise sequence of agent functions, where each stage processes and passes data to the next in a fixed functional chain.
  4. Adopt a role-based agent model coordinated through a shared task planner, where agent decisions are informed by centralized policy logic and runtime context signals.

Answer(s): D

Explanation:

A role-based agent model coordinated through a shared task planner enables dynamic task delegation, multi- step collaboration, and real-time responsiveness. Centralized policy logic and contextual signals guide each agent's decisions while preserving modularity and maintainability -- making it well suited for complex, evolving grid-operations workflows.



An AI engineer is evaluating an underperforming multi-agent workflow built with NVIDIA agentic frameworks.

Which analysis approach most effectively identifies optimization opportunities in agent coordination and communication patterns?

  1. Monitor workflow completion times using analysis that subsumes inter-agent communication costs, coordination overhead, and task allocation balance.
  2. Focus exclusively on individual agent accuracy without analyzing workflow-level efficiency, coordination costs, or overall system throughput.
  3. Evaluate agents individually, allowing the toolkit to automatically infer interaction effects, communication patterns, and emergent behaviors from coordination.
  4. Trace agent interaction patterns using observability features, measure communication overhead, identify redundant operations, and analyze task distribution efficiency.

Answer(s): D

Explanation:

Tracing inter-agent communication and coordination patterns reveals bottlenecks, redundant steps, and inefficient task distributions. This provides detailed insight into where workflow-level optimizations can improve multi-agent performance.



You are designing a virtual assistant that helps users check weather updates via external APIs. During testing, the agent frequently calls the incorrect tools, often hallucinating endpoints or returning incorrect formats. You suspect the prompt structure might be the root cause of these failures.

Which prompt design best supports consistent tool invocation in this agent?

  1. Rely on the agent's internal knowledge to infer tool usage
  2. Include tool names in natural language but without parameter examples
  3. Provide only a generic system instruction with no examples
  4. Use structured prompt templates with few-shot tool usage examples

Answer(s): D

Explanation:

Structured prompts with few-shot examples clearly demonstrate the correct tool names, parameters, and response formats, giving the agent explicit guidance for reliable tool invocation. This reduces hallucinated endpoints and mismatched formats by anchoring behavior to concrete, consistent examples.



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?

  1. Asking the LLM to "summarize the paper."
  2. Asking the LLM to "understand" the paper to generate a summary.
  3. Having the LLM generate the summaries and then manually review every output.
  4. Asking the LLM to "extract the key findings."

Answer(s): D

Explanation:

Prompting the LLM to "extract the key findings" focuses its attention on the most critical insights rather than producing general or vague summaries. This improves alignment between the model's output and the essential content of the research paper.



When implementing tool orchestration for an agent that needs to dynamically select from multiple tools (calculator, web search, API calls), which selection strategy provides the most reliable results?

  1. Random dynamic tool selection with retry mechanisms and usage examples
  2. LLM-based tool selection with structured tool descriptions and usage examples
  3. Rule-based selection with predefined tool mappings and usage examples
  4. Configuration-based tool selection with manual specifications and usage examples

Answer(s): B

Explanation:

Providing structured tool descriptions and few-shot usage examples enables the LLM to reliably infer which tool to invoke based on task requirements. This strategy supports dynamic, context-aware tool selection with higher accuracy than rule-based or random approaches.



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