Amazon Web Services SAP-C02 Exam With Confidence Using Practice Dumps

Create a transit gateway in the infrastructure account.

Enable resource sharing from the AWS Organizations management account.

Create VPCs in each AWS account within the organization in AWS Organizations. Configure the VPCs to share the same CIDR range and subnets as the VPC in the infrastructure account. Peer the VPCs in each individual account with the VPC in the infrastructure account,

Create a resource share in AWS Resource Access Manager in the infrastructure account. Select the specific AWS Organizations OU that will use the shared network. Select each subnet to associate with the resource share.

Create a resource share in AWS Resource Access Manager in the infrastructure account. Select the specific AWS Organizations OU that will use the shared network. Select each prefix list to associate with the resource share.

Create a Lambda function that extracts metrics data for each API Lambda function from Amazon CloudWatch Logs for the 2-week penod_ Collate the data into tabular format. Store the data as a _csvfile in an S3 bucket. Create an Amazon Eventaridge rule to schedulethe Lambda function to run every 2 weeks.

Opt in to AWS Compute Optimizer. Create a Lambda function that calls the ExportLambdaFunctionRecommendatlons operation. Export the _csv file to an S3 bucket. Create an Amazon Eventaridge rule to schedule the Lambda function to run every 2 weeks.

Opt in to AWS Compute Optimizer. Set up enhanced infrastructure metrics. Within the Compute Optimizer console, schedule a job to export the Lambda recommendations to a _csvfile_ Store the file in an S3 bucket every 2 weeks.

Purchase the AWS Business Support plan for the production account. Opt in to AWS Compute Optimizer for AWS Trusted Advisor checks. In the Trusted Advisor console, schedule a job to export the cost optimization checks to a _csvfile_ Store the file in an S3 bucket every 2 weeks.

Build custom checks by using Amazon CloudWatch Logs and AWS Lambda to parse tool calls and response text. Use weekly manual reviews to decide whether the deployed AI agent meets performance standards.

Use an Amazon Bedrock model evaluation job on the underlying foundation model (FM) by using prompt-response pairs. Promote the release if the model ' s helpfulness and correctness scores improve over the previous release.

Configure agent trace logging. Add Amazon Bedrock AgentCore Evaluations to the pipeline for on-demand evaluation by using built-in and custom evaluators. Enable online evaluation after deployment. Periodically review a sampled subset of sessions.

Use Amazon Bedrock AgentCore Evaluations in online mode after deployment by using built-in evaluators. Use post-deployment rollback if the evaluation detects regression.