DEA-C01 Data Store Management Practice Question
A data engineer needs to migrate an on-premises Apache Hadoop cluster to AWS. The cluster stores data in HDFS and runs MapReduce jobs. The company wants to minimize operational overhead and leverage serverless technologies where possible. Which AWS service should the data engineer use to replace HDFS storage?
⚠ Common exam trap
It's easy for candidates to confuse Amazon EMR (a compute service) with a storage service, assuming it replaces HDFS, when in fact EMR can use either HDFS or S3 for storage, and the question explicitly asks for the storage replacement.
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 correct replacement for HDFS because it provides highly durable, scalable, and serverless object storage that can be used as the primary storage layer for Amazon EMR. Unlike HDFS, S3 decouples storage from compute, eliminating the need to manage cluster storage and allowing jobs to run on ephemeral clusters, which minimizes operational overhead. S3 integrates with EMR via the EMR File System (EMRFS), enabling MapReduce jobs to read/write data directly from S3 as if it were HDFS.
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 EBS
Why it's wrong here
Amazon EBS provides block volumes attached to individual EC2 instances, giving no shared, distributed, HDFS-compatible namespace across a cluster. It is tempting because EBS offers durable, high-performance storage for single instances, which would be correct for instance-level disks rather than a multi-node distributed filesystem.
- ✗
Amazon EMR
Why it's wrong here
Amazon EMR is a managed cluster service, not serverless storage; it still provisions EC2 nodes and requires cluster lifecycle management, adding operational overhead. It is tempting because EMR runs Hadoop and MapReduce workloads natively, which would be correct when the requirement is managed compute rather than a serverless HDFS replacement.
- ✓
Amazon S3
Why this is correct
Amazon S3 provides durable, serverless object storage that replaces HDFS without cluster management, satisfying the minimise-operational-overhead constraint. MapReduce workloads migrate to Amazon EMR or Athena, while S3 becomes the decoupled storage layer, eliminating NameNode and DataNode administration entirely.
- ✗
Amazon Redshift
Why it's wrong here
Amazon Redshift is a columnar data warehouse for SQL analytics, not an HDFS-compatible filesystem, so MapReduce jobs cannot read from it as storage. It is tempting because Redshift stores large datasets and integrates with analytics tooling, which would be correct for structured warehousing rather than Hadoop storage replacement.
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 by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This DEA-C01 practice question is part of Courseiva's free Amazon Web Services certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the DEA-C01 exam.