AWS Certified Solutions Architect Associate · Free Practice Question Hard
Question 43
A company produces batch data that comes from different databases. The company also produces live stream data from network sensors and application APIs. The company needs to consolidate all the data into one place for business analytics. The company needs to process the incoming data and then stage the data in different Amazon S3 buckets. Teams will later run one-time queries and import the data into a business intelligence tool to show key performance indicators (KPIs). Which combination of steps will meet these requirements with the LEAST operational overhead? (Choose two.)
- A Use an AWS Glue extract, transform, and load (ETL) job to convert the data into JSON format. Load the data into multiple Amazon OpenSearch Service (Amazon Elasticsearch Service) clusters.
- B Use blueprints in AWS Lake Formation to identify the data that can be ingested into a data lake. Use AWS Glue to crawl the source, extract the data, and load the data into Amazon S3 in Apache Parquet format.
- C Use Amazon Athena for one-time queries. Use Amazon QuickSight to create dashboards for KPIs.
- D Use Amazon Kinesis Data Analytics for one-time queries. Use Amazon QuickSight to create dashboards for KPIs.
- E Create custom AWS Lambda functions to move the individual records from the databases to an Amazon Redshift cluster.
Reveal correct answers
Correct answers: C, E
Explanation
To meet the company's requirements with the least operational overhead, the following steps should be taken: 1. Use Amazon Athena for one-time queries and Amazon QuickSight to create dashboards for KPIs. This combination allows for efficient querying and visualization of data without the need for extensive infrastructure management. 2. Use blueprints in AWS Lake Formation to identify the data that can be ingested into a data lake. Use AWS Glue to crawl the source, extract the data, and load the data into Amazon S3 in Apache Parquet format. This approach simplifies the data ingestion and transformation process, ensuring that the data is efficiently staged in Amazon S3 for further analysis.Discussion
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