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NVIDIA NCP-AAI Exam
Agentic AI (Page 6 )

Updated On: 7-Feb-2026
Page 5 of 26
PDF with 121 Questions

An engineer has created a working AI agent solution providing helpful services to users. However, during live testing, the AI agent does not perform tasks consistently.

Which two potential solutions might help with this issue? (Choose two.)

  1. Remove schema validations and assertions on tool outputs to avoid inconsistency.
  2. Increase randomness (e.g., temperature) and remove fixed seeds to avoid determinism.
  3. Identify where dividing the tasks into subtasks and handling them by multiple agents can help.
  4. Refine the prompt given to the AI Agent; be clear on objectives

Answer(s): C,D

Explanation:

Breaking tasks into smaller, well-defined subtasks handled by specialized agents improves reliability and reduces failure points. Clarifying and refining the agent's prompt strengthens instruction quality, ensuring more consistent execution of tasks during real-world operation.



A development team is building a customer support agent that interacts with users via chat. The agent must reliably fetch information from external databases, handle occasional API failures without crashing, and improve its responses by learning from user feedback over time.

Which of the following tasks is most critical when enhancing an AI agent to handle real-world interactions and improve over time?

  1. Applying a well-structured training process with foundational generative models and prompt engineering
  2. Utilizing internal knowledge bases to support agent responses alongside external APIs
  3. Implementing retry logic for error handling and integrating user feedback loops for iterative improvement
  4. Designing conversation flows that provide consistent responses based on predefined scripts

Answer(s): C

Explanation:

Reliable external interaction requires robust retry mechanisms, while user feedback loops enable continuous learning and refinement. Together, these capabilities allow the agent to function effectively in real-world conditions and improve over time.



What NVIDIA framework can be used to train a better agent?

  1. NeMo-RL
  2. NeMo Guardrails
  3. TensorRT-LLM

Answer(s): A

Explanation:

NeMo-RL provides reinforcement-learning capabilities specifically designed to improve agent behavior through iterative training, enabling performance enhancement beyond inference-only frameworks.



You are evaluating your RAG pipeline. You notice that the LLM-as-a-Judge consistently assigns high similarity scores to responses that contain irrelevant information.

What should you investigate as the most likely potential cause with the least development effort?

  1. The temperature setting used by the LLM during response generation.
  2. The size of the knowledge base used to power the RAG pipeline.
  3. The quality of the synthetic questions used for evaluation.
  4. The prompt used to instruct the LLM-as-a-Judge to assess the response.

Answer(s): D

Explanation:

The evaluative behavior of an LLM-as-a-Judge is primarily governed by its instruction prompt. If the prompt does not clearly define relevance criteria, the model may reward answers containing extra or unrelated details, making prompt refinement the most direct and lowest-effort fix.



You're managing an agentic AI responsible for customer support ticket triage. The agent has been consistently accurate in routing tickets to the appropriate departments. However, a team leader has noticed a significant increase in the number of tickets requiring "escalation" ­ cases where the agent initially misclassified a complex issue as a simple, routine one, leading to delays and frustrated customers.

What would be an appropriate first step in resolving this issue?

  1. Analyzing the agent's decision-making process, focusing on the specific criteria it uses to classify tickets, and identifying potential biases or blind spots.
  2. Adjusting the agent's reward function to prioritize speed of resolution over accuracy, as a first step in analysis of the problem.
  3. Increasing the agent's autonomy, granting it more decision-making power during triage to improve its efficiency.
  4. Conducting a "red-teaming" exercise, having human agents deliberately create complex and ambiguous scenarios to analyze the agent's robustness.

Answer(s): A

Explanation:

Examining the agent's decision criteria reveals where its reasoning fails to distinguish complex cases from simple ones. Identifying these blind spots provides the necessary insight to adjust model logic, training data, or routing thresholds to reduce misclassification and escalation events.



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