Question 1

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.)

Options :

A : Remove schema validations and assertions on tool outputs to avoid inconsistency.

B : Increase randomness (e.g., temperature) and remove fixed seeds to avoid determinism.

C : Identify where dividing the tasks into subtasks and handling them by multiple agents can help.

D : Refine the prompt given to the AI Agent; be clear on objectives

Answer: C,D

Question 2

You are designing the architecture for a RAG (Retrieval-Augmented Generation) system, and you are concerned about ensuring data freshness and minimizing latency. Which of the following is the most important consideration when designing the architecture?

Options :

A : Employing a consolidated architecture with a large service handling all data retrieval and LLMinteraction. This ensures consistent performance and simplifies debugging.

B : Using a synchronous, block-level approach, where the LLM continuously monitors the database forupdates and retrieves the entire dataset with each prompt.

C : Implementing a single, centralized database for all data, updated with a synchronous polling mechanismfor the LLM to retrieve the latest information.

D : Use a loosely coupled, event-driven micro-service architecture where separate services handle dataindexing, retrieval, and LLM prompting.

Answer: D

Question 3

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?

Options :

A : Analyzing the agent’s decision-making process, focusing on the specific criteria it uses to classifytickets, and identifying potential biases or blind spots.

B : Adjusting the agent’s reward function to prioritize speed of resolution over accuracy, as a first step inanalysis of the problem.

C : Increasing the agent’s autonomy, granting it more decision-making power during triage to improve itsefficiency.

D : Conducting a “red-teaming” exercise, having human agents deliberately create complex and ambiguousscenarios to analyze the agent’s robustness.

Answer: A

Question 4

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?

Options :

A : Use of guardrails to filter out hallucinated content

B : Retry logic implementation to ensure robustness during API failures

C : Chain-of-thought prompting for reasoning accuracy

D : Multi-modal model integration to handle both text and vision inputs

Answer: D

Question 5

In a global financial firm, an AI Architect is building a multi-agent compliance assistant using an agentic AI framework. The system must manage short-term memory for multi-turn interactions and long-term memory for persistent user and policy context. It should enable contextual recall and adaptation across sessions using NVIDIA’s tool stack. Which architectural approach best supports these requirements?

Options :

A : Leverage NVIDIA NeMo Framework with modular memory management, integrating conversationalstate tracking, knowledge graphs, and vector store retrieval, while using LoRA-tuned models to adaptresponses overtime.

B : Leverage RAPIDS cuDF for memory tracking by streaming multi-turn conversation logs as GPUresident data frames, assuming transactional history can be recalled and reasoned over using dataframeoperations.

C : Rely exclusively on TensorRT to encode all prior knowledge into compiled model weights, allowinginference-only execution with no external memory dependencies across sessions.

D : Leverage NVIDIA Triton Inference Server with dynamic batching to cache session-level inputsbetween inference calls, and use an external Redis store for long-term memory.

Answer: A

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