Question 1

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 2

You are creating a virtual assistant agent that needs to handle an increasingly wide range of tasks over an extended period. What is the primary benefit of combining external storage (like RAG) with fine-tuning (embodied memory) in this context?

Options :

A : To enhance long-term reasoning capabilities and adaptability

B : To accelerate the agent’s initial response time

C : To ensure the agent doesn’t make any errors

D : To eliminate the need for external knowledge

Answer: A

Question 3

When evaluating a multi-agent customer service system experiencing unpredictable scaling costs and performance bottlenecks during peak hours, which analysis approaches effectively identify optimization opportunities for both infrastructure efficiency and service reliability? (Choose two.)

Options :

A : Maintain consistent resource allocation across all service hours, for a more precise view of baseline traffic impact on long-term infrastructure efficiency.

B : Scale agent infrastructure based on aggregate performance trends, using system-wide monitoring toolsto identify broader optimization patterns across resources.

C : Deploy agents with configurable scaling workflows, allowing analysis of resource adjustment strategies and their effects on service stability during variable demand periods.

D : Deploy distributed tracing with cost attribution per agent type, correlating resource consumption withbusiness value metrics to identify optimization opportunities in agent deployment strategies.

E : Implement comprehensive workload profiling using NVIDIA Nsight to analyze GPU utilization patterns, identify underutilized resources, and optimize batch sizing for dynamic scaling withKubernetes HPA.

Answer: D,E

Question 4

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 5

When implementing stateful orchestration for agentic workflows using LangGraph, which memory management approach provides the best balance of performance and context retention?

Options :

A : Store complete conversation history in memory with periodic database syncing

B : Implement rolling window memory with fixed conversation length limits

C : Use session-ID based checkpointer with user-defined schema for selective state persistence

Answer: C

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