AWS Certified Data Engineer Associate DEA-C01 Practice Question

A retail company ingests about 1 TB of semi-structured clickstream data into AWS every day. The data must be retained for 6 months and be available for ad-hoc SQL queries by analysts who run a few dozen queries per day that typically return less than 2 GB of results. The company needs the lowest-cost solution while keeping query latency under 10 seconds. Which data storage approach should a data engineer implement to meet these requirements?

Load the data into an Amazon Redshift RA3 cluster with concurrency scaling enabled.
Persist the data in Amazon RDS for PostgreSQL on gp3 storage and use Amazon Redshift federated queries for reporting.
Ingest the data into an Amazon DynamoDB table configured for on-demand capacity and query it with PartiQL.
Store the data as compressed Parquet files in Amazon S3 and run interactive queries with Amazon Athena.

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Answer Description

Storing the data in Amazon S3 and querying it with Amazon Athena is the most economical choice for infrequent, interactive analytics. S3 provides virtually unlimited, low-cost object storage, and Athena is a serverless query service that charges only for the amount of data scanned, so costs scale with actual analyst usage. Compressing the data into columnar Parquet files further reduces the number of bytes scanned, lowering both query cost and latency to well within the 10-second target.

Using an Amazon Redshift RA3 cluster or Amazon RDS PostgreSQL would require continuously running database instances, producing higher fixed costs for compute and storage. DynamoDB on-demand capacity is optimized for key-value access patterns; scanning multiple terabytes for SQL analytics would be expensive and could not meet the latency target.

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