NVIDIA Agentic AI - NCP-AAI 模擬練習

A. Implement exponential-backoff retries with a circuit breaker, and return a clear message to the user if all retries fail.

B. Return a standard fallback message on failures to maintain conversation flow and reduce the risk of service interruptions for the user.

C. Schedule retries using a fixed delay for all failure types, maintaining predictable timing and user notifications after each attempt.

D. Retry requests with a consistent short delay after each failure and notify the user as each retry takes place.

A. Monitor workflow completion times using analysis that subsumes inter-agent communication costs, coordination overhead, and task allocation balance.

B. Evaluate agents individually, allowing the toolkit to automatically infer interaction effects, communication patterns, and emergent behaviors from coordination.

C. Trace agent interaction patterns using observability features, measure communication overhead, identify redundant operations, and analyze task distribution efficiency.

D. Focus exclusively on individual agent accuracy without analyzing workflow-level efficiency, coordination costs, or overall system throughput.

A. To filter out all queries containing specific keywords that have been flagged as problematic.

B. To establish rules and constraints based on the meaning of user queries and generated responses.

C. To automatically translate all LLM responses into multiple languages for improved user comprehension.

D. To eliminate all potential harmful entries from the vector database.

A. Review performance across both high- and low-accuracy agent groups, comparing case outcomes and identifying patterns contributing to top and bottom results.

B. Average performance metrics across all agents as this will smooth individual variations, query distribution differences, and temporal factors affecting agent behavior and accuracy.

C. Assess performance data from recently improved agents and highlight strong results, using outcome comparisons to identify areas with the greatest impact on service quality.

D. Deploy stratified evaluation sampling across agent variants, query complexity levels, and temporal patterns while tracking decision paths using comparative analytics.

A. The total cost savings achieved through the agent's optimization, which represents a significant financial benefit.

B. The percentage of delivery times that fall within the acceptable delay window, considering customer satisfaction as a key factor.

C. The agent's adherence to the prescribed delivery schedules, as it's demonstrably improving efficiency.

D. The agent's ability to predict future demand fluctuations, as accurate forecasting is crucial for effective logistics.

A. Deploy agents directly on individual NVIDIA RTX workstations without containerization or orchestration, relying on load balancers with round-robin for traffic distribution.

B. Deploy each agent on dedicated NVIDIA DGX systems with manual scaling based on previous days traffic predictions and static resource allocation for peak loads.

C. Deploy all agents on a single large GPU instance without containerization, scaling compute by upgrading to larger GPU instances when needed.

D. Deploy NVIDIA NIM microservices on Kubernetes with auto-scaling capabilities, utilizing NVIDIA NIM Operator for lifecycle management and horizontal pod autoscaling based on custom metrics.

A. Use deterministic temperature settings for all generations

B. Reduce the number of tools available to avoid bad integrations

C. Re-train the model to avoid the use of third-party tools entirely

D. Add robust schema validation and exception handling for all tool outputs