最新的NVIDIA Agentic AI - NCP-AAI免費考試真題

A. Automated consistency checking across multiple agent runs

B. Performance testing during development phases

C. Random sampling of agent interactions for manual review

D. Structured output validation with Pydantic schemas

E. User satisfaction surveys as the primary quality metric

A. Increase output randomness to improve exploration

B. Replace retrieval with static hard-coded text snippets

C. Add fact-checking steps using external tools during generation

D. Use more verbose prompts to reinforce correct definitions

A. It eliminates the need for external storage like RAG.

B. It prevents the agent from becoming overly specialized to a single task.

C. It guarantees the agent will produce the same output for the same input.

D. It prevents the agent from forgetting past successes and failures.

A. Design monolithic LLM-based agents that handle all fraud detection tasks within a single loop, without modular roles or multi-agent coordination.

B. Design an agentic workflow where each agent acts independently on isolated data slices with no inter- agent communication to reduce latency and model complexity.

C. Design a multi-agent system where individual agents collaborate synchronously using shared memory and structured messaging.

D. Design a centralized rule-based service that checks all transactions against static fraud indicators and sends alerts when thresholds are exceeded.

E. Design a modular multi-agent system where individual agents collaborate asynchronously using shared memory and structured messaging.

A. Implement a benchmark pipeline that automatically compares the generated outputs using metrics like relevance, creativity, and grammatical correctness.

B. Gather ratings from a panel of users, with each rating marketing copy on a 1 to 5 scale for overall impression of relevance, creativity, and grammatical correctness.

C. Implement a human-in-the-loop to subjectively rate each output on a scale of 1 to 5 based on the user's personal preference.

D. Measure the click-through rate for each system's marketing copy as the primary indicator of performance.

A. Pseudonymize protected attributes, implement fairness-aware debiasing, maintain an audit trail, and enforce GDPR data-minimization and consent.

B. Perform a post-deployment GDPR and bias audit and process raw personal data as received.

C. Exclude gender and ethnicity fields during training, use a generic privacy policy for consent, and do not maintain audit logs or apply targeted debiasing.

D. Encrypt all candidate data at rest and in transit, remove protected attributes from analysis, and conduct manual bias checks on recommendations.

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

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

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

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

A. Containerized deployment with NIM (NVIDIA Inference Microservices)

B. Bare metal deployment with manual resource allocation

C. Static virtual machine deployment with fixed resources

D. Serverless deployment without GPU acceleration

E. Kubernetes orchestration with Horizontal Pod Autoscaling (HPA)