A company's application has an API that retrieves workload metrics. The company needs to audit, analyze, and visualize these metrics from the application to detect issues at scale. Which combination…

Question

Options

• AConfigure an Amazon EventBridge schedule to invoke an AWS Lambda function that calls the
• BConfigure an Amazon EventBridge schedule to invoke an AWS Lambda function that calls the
• CCreate an AWS Glue crawler to catalog the workload metric data in the Amazon S3 bucket.
• DConnect an AWS Glue crawler to the Amazon DynamoDB stream to catalog the workload metric
• ECreate Amazon QuickSight datasets from the Amazon Athena views. Create a QuickSight
• FCreate an Amazon CloudWatch dashboard that has custom widgets that invoke AWS Lambda

• A
  63% (12)
• B
  11% (2)
• D
  5% (1)
• F
  21% (4)

Explanation

The recommended architecture to audit, analyze, and visualize application workload metrics at Using Amazon EventBridge to schedule AWS Lambda invocations that call the application API and fetch metrics (Option A). The data is stored in Amazon S3, which is ideal for scalable, cost- effective storage of large datasets. Cataloging the stored data with AWS Glue crawlers, enabling schema discovery and making data queryable via Amazon Athena (Option C). Visualizing the data by creating Amazon QuickSight datasets from Athena views and building dashboards for analysis (Option E). Option B and D introduce DynamoDB, which is less suitable for large-scale analytics and Athena querying. Option F suggests querying Athena via Lambda widgets in CloudWatch, which adds complexity without significant benefit over QuickSight.

No community discussion yet for this question.