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 of steps will meet these requirements? (Choose three.)
To meet the requirements of auditing, analyzing, and visualizing workload metrics from the application, the company can follow these steps: First, configure an Amazon EventBridge schedule to invoke an AWS Lambda function that calls the API to retrieve workload metrics. Store the workload metric data in an Amazon S3 bucket. This ensures the metrics are collected and stored effectively. Next, create an AWS Glue crawler to catalog the workload metric data in the Amazon S3 bucket, and create views in Amazon Athena for the cataloged data. This allows for efficient data querying and analysis. Finally, create Amazon QuickSight datasets from the Amazon Athena views and create a QuickSight analysis to visualize the workload metric data as a dashboard. This provides the necessary visualization tools to detect issues at scale.
ACE https://aws.amazon.com/blogs/mt/analyzing-amazon-cloudwatch-internet-monitor-measurement-logs-using-amazon-athena-amazon-quicksight/
Data Collection and Storage: EventBridge Schedule + Lambda + S3 Data Cataloging and Querying: Glue Crawler + Athena Data Visualization: QuickSight
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