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Integrate NeMo Guardrails, configure NIM microservices for optimized inference, use TensorRT-LLM for deployment, and profile the system using Triton Inference Server with multi-modal support.
Which of the following strategies aligns with best practices for operationalizing and scaling such Agentic systems?

• A. Deploy agents on bare-metal servers to maximize performance and avoid container overhead, using manual scripts for orchestration and monitoring.
• B. Use Docker containers orchestrated by Kubernetes, implement MLOps pipelines for CI/CD, monitor agent health with Prometheus/Grafana.
• C. Run agents as independent serverless functions to minimize infrastructure management, relying primarily on cloud provider auto-scaling and logging tools.
• D. Deploy all agents on a single high-performance GPU node to reduce latency, and use cron jobs for periodic health checks and updates.

An AI Engineer has deployed a multi-agent system to manage supply chain logistics. Stakeholders request greater insight into how the agents decide on actions across tasks.
Which approach would best improve decision transparency without modifying the underlying model architecture?

• A. Generate visual summaries of attention patterns for every decision
• B. Retain and share the full sequence of task instructions with stakeholders
• C. Record a step-by-step reasoning log throughout each agent workflow
• D. Gather structured user evaluations after each completed subtask

Your team has deployed a generative agent for internal HR use, including summarizing candidate resumes and suggesting interview questions. After deployment, you've noticed that the model occasionally associates certain names or genders with particular roles.
Which mitigation strategy is the most effective and scalable for reducing this type of bias in agent outputs?

• A. Adjust system prompts to explicitly instruct the agent to avoid assumptions based on demographic features
• B. Add more training examples to the training dataset and re-train the model
• C. Implement guardrails to prevent outputs referencing protected attributes
• D. Randomly replace names in prompts to reduce identity correlation

In your RAG deployment, you've identified a performance bottleneck in the retrieval phase - specifically, the time it takes to access the vector database.
Which of the following optimization strategies is most aligned with micro-service best practices, considering your RAG architecture?

• A. Implement a "cache-and-check" mechanism where the retrieval microservice immediately returns the first matching chunk, regardless of relevance.
• B. Optimize the LLM prompt to be shorter and more concise, significantly reducing the computational load.
• C. Increase the size of the LLM model itself, because it will automatically accelerate the overall response time.
• D. Introduce a dedicated service responsible solely for querying the vector database and returning relevant chunks.