Collect metrics to S3 on a schedule, catalog with a Glue crawler, query with Athena, and visualize in QuickSight
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.)
Community Votes
100% of anonymous learners picked answer ACE. Votes are pick records left by other test-takers — they are not the verified answer.
Community Insight
The architecture is a three-stage pipeline and each stage has one AWS service that fits it: EventBridge plus Lambda plus S3 for scheduled collection and durable storage (A), a Glue crawler plus Athena views for cataloging and SQL access (C), and QuickSight datasets plus an analysis for visualization (E). Options B and D substitute DynamoDB with a stream, which pairs with option D's crawler reading a DynamoDB stream; DynamoDB is not an analytics store for this scale of metric history, and a crawler targets S3 data rather than a stream. Using S3 keeps the data in the service designed for large-scale analytical querying.
Auditing and visualizing workload metrics at scale needs a collection layer, a catalog and query layer, and a visualization layer. An EventBridge schedule invokes a Lambda function that calls the API and stores the metric data in Amazon S3, which is the durable, scalable store. An AWS Glue crawler catalogs that S3 data and Amazon Athena views make it queryable, and Amazon QuickSight datasets built on those Athena views render the metrics as a dashboard.
Storing the metric data in a DynamoDB table with a stream enabled (B) — DynamoDB is an operational key-value store, not an analytics repository for large volumes of time-series metric history, and the crawler-plus-views step in the correct answer is built around S3 data. Connecting an AWS Glue crawler to a DynamoDB stream to catalog the data (D) — a Glue crawler crawls S3 and other data stores, it does not subscribe to a DynamoDB stream, so this integration does not exist as described. QuickSight datasets built on the Athena views (E) are the correct visualization step, so the error in the other combinations is confined to the storage and cataloging choices.
Community Discussion (4 comments)
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Expert Analysis
Why the Answer Is Correct
The requirement is to audit, analyze, and visualize workload metrics from an application API at scale, which maps onto three stages. For collection, an EventBridge schedule invokes a Lambda function that calls the API to retrieve the metrics and stores the data in Amazon S3, giving automated, durable, and scalable storage that is well suited to analytical query volumes (A). For cataloging and querying, an AWS Glue crawler catalogs the metric data in the S3 bucket and Amazon Athena views are created over the cataloged data, providing SQL access without moving the data (C). For visualization, Amazon QuickSight datasets are created from the Athena views and a QuickSight analysis renders the metric data as a dashboard, giving the required visual representation (E). A, C, and E are the correct combination.Why the Other Options Are Wrong
B replaces the S3 storage layer with a DynamoDB table that has a stream enabled. DynamoDB is an operational key-value database optimized for request-driven workloads, not an analytical store for large volumes of metric history, so it is a poor fit for the at-scale analysis the requirement describes, and the remaining cataloging steps are built around S3. D goes further and connects an AWS Glue crawler to a DynamoDB stream to catalog the metric data. A Glue crawler crawls S3 buckets and supported data stores; it does not subscribe to a DynamoDB stream, so this integration is not something the crawler supports and the cataloging step would not function. The visualization and collection steps in those options are sound, but the storage and cataloging choices are not. A, C, and E are correct.Community Comment Notes
Community voted A,C,E unanimously. trungtd laid out the three stages explicitly: EventBridge schedule with Lambda and S3 for data collection and storage, a Glue crawler with Athena for cataloging and querying, and QuickSight for visualization, which is the same decomposition used in the correct answer. jamesf explained that the EventBridge schedule with Lambda storing to S3 provides a scalable and automated way to collect the metrics. getadroit cited the AWS Management Tools blog on analyzing CloudWatch Internet Monitor measurement logs with Athena and QuickSight, which follows this exact pipeline. No alternative received support.Official Reference
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