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NVIDIA NCP-AAI Exam
Agentic AI (Page 2 )

Updated On: 7-Feb-2026
Page 2 of 26
PDF with 121 Questions

When designing tool integration for an agent that needs to perform mathematical calculations, web searches, and API calls, which architecture pattern provides the most scalable and maintainable approach?

  1. External tool services with manual configuration for each agent instance
  2. Microservice-based tool architecture with standardized interfaces
  3. Monolithic tool handler with conditional logic for different tool types
  4. Embedded tool functions within the main agent code

Answer(s): B

Explanation:

A microservice-based tool architecture with standardized interfaces allows each tool to scale independently, supports clean separation of concerns, and enables long-term maintainability as new tools or capabilities are added without requiring changes to the core agent logic.



A company is deploying an AI-powered customer support agent that integrates external APIs and handles a wide range of customer inputs dynamically.

Which of the following strategies are appropriate when designing an AI agent for dynamic conversation management and external system interaction? (Choose two.)

  1. Integrating a feedback loop from user interactions to iteratively improve agent behavior.
  2. Using rule-based logic as the primary framework to maintain consistency in agent decisions.
  3. Implementing retry logic for API failures to ensure robustness in external communications.
  4. Preferring hardcoded responses for frequent queries to deliver reliable and low-latency answers.

Answer(s): A,C

Explanation:

A feedback loop enables continuous refinement of agent behavior based on real-world interactions, improving adaptability in dynamic conversations. Retry logic strengthens robustness when interacting with external systems by handling transient API failures gracefully, ensuring more reliable task execution.



In the context of agent development, how does an autonomous agent differ from a predefined workflow when applied to complex enterprise tasks?

  1. Agents optimize for execution speed under fixed input-output mappings, while workflows prioritize goal alignment through adaptive reasoning and memory mechanisms.
  2. Workflows provide deterministic task sequencing with conditional branching, while agents adapt decisions dynamically based on goals, context, and environment feedback.
  3. Workflows emphasize parallelism and distributed coordination of processes, while agents emphasize serialization and isolated problem solving.

Answer(s): B

Explanation:

A predefined workflow follows fixed, deterministic task sequences, whereas an autonomous agent adjusts its decisions in real time based on evolving context, goals, and feedback. This dynamic reasoning enables effective handling of complex, variable enterprise tasks.



A Lead AI Architect at a global financial institution is designing a multi-agent fraud detection system using an agentic AI framework. The system must operate in real time, with distinct agents working collaboratively to monitor and analyze transactional patterns across accounts, retain and share contextual information over time, and escalate suspicious behaviors to a human fraud analyst when needed.

Which architectural approach enables intelligent specialization, shared memory, and inter-agent coordination in a dynamic and evolving threat environment?

  1. Design a modular multi-agent system where individual agents collaborate asynchronously using shared memory and structured messaging.
  2. Design a multi-agent system where individual agents collaborate synchronously using shared memory and structured messaging.
  3. Design a centralized rule-based service that checks all transactions against static fraud indicators and sends alerts when thresholds are exceeded.
  4. Design an agentic workflow where each agent acts independently on isolated data slices with no inter-agent communication to reduce latency and model complexity.
  5. Design monolithic LLM-based agents that handle all fraud detection tasks within a single loop, without modular roles or multi-agent coordination.

Answer(s): A

Explanation:

An asynchronous modular multi-agent system with shared memory and structured messaging supports specialization, continuous context sharing, and adaptive coordination. This architecture allows agents to operate independently yet collaborate intelligently in real time, which is essential for detecting evolving fraud patterns and escalating high-risk cases to human analysts.



When designing complex agentic workflows that include both sequential and parallel task execution, which orchestration pattern offers the greatest flexibility?

  1. Graph-based workflow orchestration incorporating conditional branches
  2. Linear pipeline orchestration with a fixed task sequence
  3. Event-driven orchestration that triggers tasks reactively, in series or in parallel

Answer(s): A

Explanation:

A graph-based orchestration pattern provides maximum flexibility because it supports conditional logic, branching, and both sequential and parallel execution paths. This enables the design of highly adaptive agentic workflows that can dynamically respond to context and task complexity.



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