A company operates agent-based workloads in multiple data centers. They want to minimize latency for users in different regions, maintain continuous service during infrastructure upgrades, and keep operational costs predictable.
Which deployment practice best supports low-latency, resilient, and cost-efficient agent operations at scale?

• A. Schedule regular agent downtime for system updates and operational recalibration.
• B. Apply static infrastructure allocation with centralized resource usage monitoring at a single data center.
• C. Implement geo-distributed deployments with rolling updates and resource usage monitoring.
• D. Prioritize high-performance GPUs for all agents in geo-distributed deployments.

A social media company wants to expand its agentic system to support global users, minimize downtime, and ensure smooth operation during usage spikes. The team is considering various deployment and scaling strategies to achieve these goals.
Which solution most effectively supports reliable and scalable deployment for an agentic AI system serving a global user base?

• A. Designing a distributed system architecture with multi-region deployment, automated failover, and dynamic resource allocation
• B. Integrating MLOps practices for continuous deployment and rapid model updates in production environments
• C. Using hardware profiling to optimize agent workloads for efficient GPU utilization across all deployed instances
• D. Implementing containerization with Docker to simplify deployment and streamline updates

You are using an LLM-as-a-Judge to evaluate a RAG pipeline.
What is the primary benefit of synthetically generating question-answer pairs, rather than relying solely on human-created test cases?

• A. Synthetically generated answers are inherently more accurate than those produced by the LLM.
• B. Synthetically generated questions are more challenging and reveal deeper flaws in the RAG pipeline.
• C. Synthetic generation eliminates the need for any human validation of the RAG pipeline's output.
• D. Synthetic generation allows for systematic testing of the RAG pipeline across a wider range of scenarios and query types.

A development team is building an AI agent capable of autonomously planning and executing multi-step tasks while retaining context and learning from past interactions.
Which practice is most important to enable the agent to effectively manage long-term memory and complex tasks?

• A. Use basic rule-based decision methods that emphasize fast responses over adaptive planning.
• B. Apply short-term memory approaches that handle each interaction independently of previous ones.
• C. Implement memory mechanisms for context retention and apply chain-of-thought prompts to enhance reasoning.
• D. Reduce planning features and memory management to keep the system streamlined.