Operational Efficiency And Optimization

A company runs a customer-facing chatbot on Amazon Bedrock with provisioned throughput purchased as a model unit for a specific foundation model in us-east-1. Traffic is steady during business hours but drops to near zero overnight, and the team is paying for the commitment around the clock. Leadership wants to stop paying for idle overnight capacity without risking daytime throttling. Which action best fits?

You have a fleet of SageMaker endpoints. You want to implement an automatic cost reduction strategy during off-peak hours for specific endpoints. Which approach is most efficient?

Which TWO actions should you perform to identify cost savings in an existing SageMaker environment?

Which THREE metrics are best used for setting up auto-scaling policies on a SageMaker endpoint?

You need to ensure that your SageMaker inference endpoint does not exceed a specific monthly budget. Which AWS feature allows you to set up notifications when spending thresholds are crossed?

Your team uses SageMaker Pipelines for training. How can you optimize the storage costs of the artifacts generated by these runs?

Which TWO metrics are critical for monitoring an Amazon SageMaker inference endpoint to ensure operational health?

You are deploying a foundation model on Amazon SageMaker. To optimize inference costs for an application with unpredictable traffic patterns and significant idle periods, which deployment option should you select?

You are tuning a Large Language Model on SageMaker and notice that GPU memory utilization is consistently low despite high latency. Which action is most appropriate to optimize performance?

Which TWO of the following strategies are best for reducing the cost of SageMaker training jobs?

Which THREE factors should you consider when tuning your inference endpoint for cost-efficiency?

You notice that your SageMaker model endpoint experiences cold starts. Which feature can mitigate this?

You are optimizing the prompt handling of a production application built on Amazon Bedrock. The application invokes Anthropic Claude 3 Sonnet with a large, static system prompt that instructs the model to act as a customer support agent for a specific product catalog. Each user request also includes a small variable user message. You want to reduce the per-token cost and latency of these invocations without changing the model or the instructions. Which Amazon Bedrock feature should you use?

Which SageMaker feature allows you to observe model performance drifts over time?

A company uses Amazon Bedrock to power a chatbot. They want to monitor the usage and cost of their Bedrock invocations and set alerts when spending exceeds a threshold. Which AWS service should they use to track and alert on Bedrock costs?

An application using Amazon Bedrock is experiencing high latency. You need to monitor the 'ModelInvocationLatency' metric to identify performance bottlenecks. Which AWS service provides the primary integration for this telemetry?

You are troubleshooting a SageMaker inference endpoint and need to see the container logs. Which service stores these logs?

Which AWS service is used to centralize and visualize costs for your various SageMaker projects?

Which TWO approaches help optimize the costs of using Amazon Bedrock?

A developer is building a document summarization feature on Amazon Bedrock. The same 30-page policy document is summarized by hundreds of users each day, and the document changes only when legal publishes a new revision. The summaries are currently regenerated per request, and the team wants to cut token spend without changing the summary text. Which approach best meets the goal?

You are deploying a model using SageMaker Multi-Model Endpoints (MME). What is the primary benefit of this strategy for operational efficiency?

A generative AI team runs nightly batch inference on Amazon Bedrock for 40 million prompts. Each job currently invokes the model once per prompt with a synchronous Converse call and the job takes nine hours, exceeding the six-hour window. The prompts are independent and the team needs the same model quality. Which change most directly reduces wall-clock time while preserving per-prompt output quality?

A generative AI application uses Amazon Bedrock to generate product descriptions. The application invokes the model for each product individually, resulting in many small requests that are throttled by the model's per-account quota. You need to increase throughput and reduce the number of API calls while keeping the same model and output quality. Which approach is MOST operationally efficient?

When configuring an Amazon Bedrock Knowledge Base, which setting helps optimize the retrieval process for RAG applications?

You are using Amazon Bedrock and need to ensure your application respects regional service quotas. Which service allows you to request a quota increase?