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?

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

When evaluating coordination failures in a multi-agent system managing distributed manufacturing workflows, which analysis approach best identifies state management and planning synchronization issues?

• A. Assess synchronization methods during design reviews and use simulations to evaluate coordination across representative workflow scenarios.
• B. Monitor agent outputs individually to confirm local correctness and examine results of specific workflow steps.
• C. Deploy distributed state tracing across agents, analyze transition timing, study communication overhead, and verify synchronization accuracy.
• D. Track workflow throughput and task completions to measure performance trends and highlight workflow outcomes.

Your team notices a spike in failed tool calls from a deployed workflow agent after a recent API schema update. The agent still returns outputs, but many are irrelevant or incomplete.
Which maintenance task should be prioritized to restore accurate behavior?

• A. Reduce tool retrieval vector similarity threshold to broaden context.
• B. Increase model temperature to encourage tool exploration.
• C. Update the tool function specifications and re-test action sequences.
• D. Reset the agent's long-term memory and reinitialize logs.

A team is evaluating multiple versions of an AI agent designed for customer support. They want to identify which version completes tasks more efficiently, responds accurately, and improves over time using user feedback.
Which practice is most important to ensure continuous refinement and optimal performance of the AI agent?

• A. Comparing agents on isolated tasks without standardized benchmarking pipelines
• B. Implementing an evaluation framework that quantifies task efficiency and incorporates human-in-the- loop feedback
• C. Tuning model parameters once before deployment to maximize initial accuracy
• D. Relying solely on offline benchmarks without incorporating live user feedback during tuning

When evaluating GPU utilization inefficiencies in deploying Llama Nemotron models across A100 and H100 clusters, which approaches help identify optimal resource allocation strategies? (Choose two.)

• A. Allow Nemotron variants to profile actual workload characteristics and allocate resources based on observed demands.
• B. Allocate all agents to Hl00 GPUs, allowing resource profiles to automatically adjust for model size and computational requirements.
• C. Profile resource utilization for each Nemotron variant and match models to appropriate GPU tiers.
• D. Assess concurrent execution capabilities by employing multi-instance GPU partitioning for varying workload types.