A large enterprise is preparing to roll out its AI-powered customer support agents worldwide. To maintain high availability and reliability, the operations team must select the best approach for monitoring, updating, and managing all agent instances across different locations.
Which solution most effectively ensures reliable operation and simplified management of large-scale agent deployments?

• A. Provide separate monitoring tools and manual updates at each regional deployment for greater local control of agent health, trigger updates, and manage rollbacks across all environments
• B. Allocating a dedicated support team to monitor agent logs and perform manual restarts to ensure human interaction in the data flywheel
• C. Scheduling updates and health checks on an annual basis to minimize service disruptions and ensure agent health, trigger updates, and manage rollbacks across all environments
• D. Establishing centralized monitoring and automated deployment pipelines to oversee agent health, trigger updates, and manage rollbacks across all environments

Implement Memory Systems for Contextual Awareness
An enterprise AI system needs to maintain contextual information over multiple interactions with users.
Which memory implementation approach would be MOST effective for managing both immediate context and long-term historical interactions within an agentic workflow?

• A. Implement a hybrid memory system with short-term memory for immediate context and a vector database for long-term memory with semantic retrieval capabilities.
• B. Rely predominantly on the context window of the base LLM model to store all historical interactions with minimal external memory supplementation.
• C. Store all user interactions in a simple key-value database which will by default provide organization and retrieval strategy for historical context management.
• D. Use a static prompt template with fixed context for all interactions, thereby providing memory information in that form across conversation sessions.

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

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. Introduce a dedicated service responsible solely for querying the vector database and returning relevant chunks.
• C. Increase the size of the LLM model itself, because it will automatically accelerate the overall response time.
• D. Optimize the LLM prompt to be shorter and more concise, significantly reducing the computational load.