AWS Certified Data Engineer Associate DEA-C01 Practice Question

An e-commerce company ingests about 800 GB of product images and related JSON metadata each day. The data must be stored with 11 nines durability, read by Spark jobs on Amazon EMR, and later queried using Amazon Athena. The solution should scale automatically, require minimal administration, and cut storage costs because the images are seldom accessed after the first few days. Which AWS storage option best meets these requirements?

Store the images and metadata in an Amazon S3 bucket and apply an S3 Lifecycle rule that transitions objects to S3 Glacier Instant Retrieval after 30 days.
Save the images as binary attributes in an Amazon DynamoDB table and scan the table from Amazon EMR.
Mount an Amazon EFS One Zone-IA file system on the EMR cluster and place the images and metadata there.
Load the images and metadata into an Amazon Redshift RA3 cluster and query the data with Redshift Spectrum.

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Answer Description

Amazon S3 is an object store that delivers 11 nines of durability, scales without user intervention, and is the native storage layer for both Amazon EMR and Amazon Athena. Because objects are infrequently accessed after upload, an S3 Lifecycle rule can transition them to a lower-cost storage class such as S3 Glacier Instant Retrieval to reduce cost while still allowing occasional access.

Amazon EFS provides POSIX file storage but does not integrate directly with Athena and is generally more expensive for large, rarely accessed datasets. Loading binary images into Amazon Redshift is inefficient and costly because Redshift is optimized for structured, columnar data, not large unstructured files. DynamoDB cannot store multi-megabyte images because each item is limited to 400 KB and would still require another service for Athena queries. Therefore, storing the data in Amazon S3 with an appropriate Lifecycle policy is the most suitable choice.

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