DEA-C01 Data Store Management Practice Question
A company is migrating an on-premises Hadoop cluster to AWS. The cluster processes large files in CSV format using Apache Spark. Which data store should be used as the primary storage for the data lake to optimize cost and performance?
⚠ Common exam trap
DEA-C01 often tests the misconception that HDFS or EBS is suitable for a data lake, but they are not cost-effective or scalable for long-term storage. Candidates might choose EMRFS with HDFS due to familiarity with Hadoop.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Amazon S3
Amazon S3 is the optimal primary storage for a data lake on AWS because it offers high durability, scalability, and cost-effectiveness. It integrates seamlessly with Apache Spark on Amazon EMR, allowing direct access to data without moving it. S3 also supports various file formats and decouples storage from compute, enabling independent scaling.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Amazon EMR File System (EMRFS) backed by HDFS
Why it's wrong here
EMRFS backed by HDFS keeps data on the cluster's local disks, so storage scales with compute and vanishes when the cluster terminates; it cannot serve as a durable, decoupled data lake. It suits transient scratch space for a running Spark job, not persistent primary storage.
- ✗
Amazon RDS for MySQL
Why it's wrong here
RDS for MySQL is a relational OLTP database with row-oriented storage and limited capacity, unsuited to large CSV files processed by Spark. A relational database would be correct for transactional application data, not a data lake.
- ✗
Amazon EBS volumes attached to the EMR cluster
Why it's wrong here
EBS volumes are block storage tied to a single Availability Zone and cannot be mounted concurrently by multiple EMR nodes, so they cannot serve as a shared data lake. They suit single-instance scratch or database workloads, not multi-node Spark storage.
- ✓
Amazon S3
Why this is correct
Amazon S3 provides durable, virtually unlimited object storage with separate compute and storage scaling, so Spark reads CSV files directly and cost stays low. HDFS on Amazon EMR couples storage to cluster lifetime, raising cost and complicating elasticity.
Quick reference
AWS S3 Storage Class Comparison
| Storage Class | Min Duration | Retrieval | Use Case |
|---|---|---|---|
| S3 Standard | None | Immediate | Frequently accessed data |
| S3 Standard-IA | 30 days | Immediate | Infrequent access, rapid retrieval |
| S3 One Zone-IA | 30 days | Immediate | Non-critical infrequent data |
| S3 Intelligent-Tiering | None | Immediate–hours | Unknown or changing access patterns |
| S3 Glacier Instant | 90 days | Milliseconds | Archive with instant retrieval |
| S3 Glacier Flexible | 90 days | Minutes–hours | Archive, flexible retrieval |
| S3 Glacier Deep Archive | 180 days | Hours | Long-term compliance archive |
Go deeper
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Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Amazon Web Services exam blueprint
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