Which combination of steps will meet these requirements?

Explanations:

This option involves configuring an Amazon EventBridge schedule to periodically invoke an AWS Lambda function that retrieves workload metrics from the API. Storing the metrics in an Amazon S3 bucket allows for cost-effective storage and easy access for further processing and analysis.

While this option retrieves workload metrics via an AWS Lambda function triggered by EventBridge and stores them in DynamoDB, using DynamoDB for this purpose is not optimal for auditing and analyzing data at scale. DynamoDB is better suited for quick lookups and transactional workloads rather than large-scale data analytics.

Creating an AWS Glue crawler to catalog the data stored in S3 enables effective data management and integration with AWS services like Amazon Athena. Athena can then be used to run SQL queries on the data, making it suitable for analysis and auditing.

Connecting a Glue crawler to the DynamoDB stream is unnecessary since the workload metrics data is more efficiently stored in S3. Moreover, using streams primarily supports real-time processing rather than the batch processing needed for auditing and analyzing workload metrics.

By creating Amazon QuickSight datasets from the Amazon Athena views, users can visualize the workload metrics effectively. QuickSight provides robust tools for creating dashboards and analysis from SQL query results, making it ideal for reporting and visualization.

This option suggests creating a CloudWatch dashboard with custom widgets invoking Lambda functions to query Athena views. While it is possible, it is more indirect and less efficient than using QuickSight for visualization, which is specifically designed for this purpose.