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Deploy a containerized application on Amazon EC2 instances in an Auto Scaling group. Use an Application Load Balancer for traffic distribution. Store user data and media files in an Amazon RDS Multi-AZ DB instance.

Refactor the application into AWS Lambda functions with Amazon API Gateway. Use Amazon DynamoDB for user data and media storage. Implement Amazon EventBridge for event-driven communication between services.

Use Amazon EKS with self-managed EC2 nodes to host a containerized application. Store user data in an Amazon Aurora Serverless cluster. Use AWS Batch for media processing and store media files in Amazon S3 Glacier Flexible Retrieval.

Deploy a containerized application using Amazon ECS with AWS Fargate. Store user data in an Amazon Aurora Multi-AZ database. Use Amazon S3 Standard-Infrequent Access for media file storage.

Use a data stream in Amazon Kinesis Data Streams in on-demand mode to capture the logs. Use AWS Lambda to process the data in real time.

Use Amazon Kinesis Data Firehose to capture the logs. Use AWS Glue to process the data in real time.

Use Amazon Kinesis Video Streams to capture the logs. Use AWS Glue to process the data in real time.

Use Amazon Managed Service for Apache Flink to capture the logs. Use AWS Lambda to process the data in real time.

Create an Amazon S3 bucket to host a static error page. Upload the custom error page to S3.

Configure a CloudWatch alarm for the ALB's HTTPCodeTargetTimeout_Count metric. Use a Lambda function to redirect traffic to a backup server.

Modify Route 53 health checks to route traffic to an S3-hosted error page when health checks fail.

Use Lambda@Edge to intercept 504 errors and redirect requests to a static error page hosted on S3.

Configure CloudFront to return a custom error page for HTTP 504 status codes, pointing to the S3-hosted error page.

Create an IAM policy granting read and write access with a condition that restricts S3 object keys to start with ${aws:username}. Attach this policy to the IAM user group.

Set up an AWS CloudTrail trail to log S3 object-level events, storing the logs in a separate S3 bucket. Use Amazon Athena to analyze these logs for audit reports.

Enable S3 server access logging on the bucket and configure another bucket for log storage. Use AWS Lambda to process logs and generate audit reports.

Implement an S3 bucket policy that allows read and write access for the IAM user group. Use Amazon QuickSight to visualize access patterns from the bucket logs.

Configure AWS Config to record S3 resource changes and use AWS CloudTrail to capture API activity. Use Athena to query both logs for compliance reporting.

{\n \"Condition\": {\n \"ForAllValues:StringEquals\": {\n \"dynamodb:LeadingKeys\": [\n \"${www.amazon.com:userid}\"\n ],\n \"dynamodb:Attributes\": [\n \"useremail\"\n ]\n }\n }\n}

{\n \"Condition\": {\n \"ForAllValues:StringEquals\": {\n \"dynamodb:LeadingKeys\": [\n \"${www.amazon.com:useremail}\"\n ],\n \"dynamodb:Attributes\": [\n \"userid\"\n ]\n }\n }\n}

{\n \"Condition\": {\n \"ForAllValues:StringEquals\": {\n \"dynamodb:LeadingKeys\": [\n \"${www.amazon.com:userid}\"\n ],\n \"dynamodb:Attributes\": [\n \"useremail\",\"user_status\"\n ]\n }\n }\n}

{\n \"Condition\": {\n \"ForAllValues:StringEquals\": {\n \"dynamodb:LeadingKeys\": [\n \"${www.amazon.com:userstatus}\"\n ],\n \"dynamodb:Attributes\": [\n \"useremail\",\"user_id\"\n ]\n }\n }\n}

Enable AWS X-Ray integration for API Gateway and Lambda. Analyze traces and service maps to identify latency hotspots.

Implement custom metrics in Lambda functions using CloudWatch Embedded Metric Format. Create dashboards to monitor function execution times.

Configure API Gateway to publish latency metrics to CloudWatch. Set alarms for high latency values in the integration responses.

Enable DynamoDB Contributor Insights and monitor throttling events using CloudWatch metrics for consumed read/write capacity.