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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?

• A. Multi-modal model integration to handle both text and vision inputs
• B. Retry logic implementation to ensure robustness during API failures
• C. Use of guardrails to filter out hallucinated content
• D. Chain-of-thought prompting for reasoning accuracy

When evaluating optimization opportunities between NeMo Guardrails, NIM microservices, and TensorRT- LLM in a production healthcare agent, which analysis approach best identifies optimization opportunities across the NVIDIA stack?

• A. Tune each component individually, focusing primarily on local performance metrics with secondary attention to integration patterns.
• B. Conduct stress testing of individual microservices and guardrails to measure peak throughput and determine theoretical performance limits of each module.
• C. Create end-to-end latency waterfalls that capture guardrail overhead, NIM queuing delays, and TensorRT optimization benefits while assessing overall pipeline efficiency.
• D. Use default configurations to establish a deployment baseline, focusing on stability before conducting deeper performance profiling.

When evaluating a customer service agent's resilience to API failures and network issues, which analysis methods effectively identify weaknesses in error handling and retry mechanisms? (Choose two.)

• A. Use fixed retry intervals to avoid the pitfalls of dynamic tuning, keeping retry timing consistent across different error conditions.
• B. Analyze retry logic for exponential backoff patterns, retry limits, and circuit breaker integration to prevent cascading failures in distributed systems.
• C. Test under normal network conditions to establish baseline behavior, comparing results against production performance during degraded service scenarios.
• D. Conduct failure injection testing with varied error types (timeouts, rate limits, malformed responses) while monitoring recovery patterns and fallback behavior.
• E. Implement retry mechanisms that standardize recovery attempts across scenarios, emphasizing consistency in handling errors.

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

When evaluating an agent's integration with external tools and APIs for data retrieval and action execution, which analysis approaches effectively identify reliability and performance issues? (Choose two.)

• A. Use static API endpoints and parameters configured during development, allowing consistent and effective agent integration across predictable workflows.
• B. Design integration tests simulating API version changes, schema modifications, and backward compatibility scenarios to ensure reliable tool connections across updates.
• C. Implement comprehensive API call tracing with latency measurement, success rates per endpoint, and correlation analysis between tool failures and task completion.
• D. Connect to external APIs with standard procedures and monitor request and response exchanges to isolate the analysis of integration reliability and effectiveness.