AWS Certified Solutions Architect – Associate (SAA-C02) — Question 603

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.)

Answer options

• A. Use Amazon Athena for one-time queries. Use Amazon QuickSight to create dashboards for KPIs.
• B. Use Amazon Kinesis Data Analytics for one-time queries. Use Amazon QuickSight to create dashboards for KPIs.
• C. Create custom AWS Lambda functions to move the individual records from the databases to an Amazon Redshift cluster.
• D. 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.
• E. 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.

Correct answer: A, E

Explanation

Using AWS Lake Formation blueprints and AWS Glue crawlers to ingest and transform data into Apache Parquet format on Amazon S3 provides a serverless, low-overhead method to build a data lake. Amazon Athena allows teams to run ad-hoc SQL queries directly on the S3 data without managing infrastructure, which integrates seamlessly with Amazon QuickSight for KPI visualization. Other options, such as managing OpenSearch clusters, writing custom Lambda code, or using Kinesis Data Analytics for ad-hoc queries, introduce significantly higher operational complexity.