DEA-C01 Data Ingestion and Transformation Practice Question
A company uses AWS Database Migration Service (DMS) to continuously replicate data from an on-premises Oracle database to Amazon S3 in Parquet format. The replication is used for near-real-time analytics. Recently, the DMS task started failing with an error indicating insufficient memory. The source database is large (2 TB). What should a data engineer do to resolve this issue while minimizing changes to the existing architecture?
⚠ Common exam trap
A common mix-up: candidates think splitting tasks or changing formats reduces memory usage, but the root cause is insufficient instance resources, and AWS DMS tasks require adequate instance sizing for large-scale CDC workloads.
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
✓
Increase the DMS replication instance size.
The error indicates the DMS replication instance is running out of memory during continuous replication of a 2 TB Oracle database to S3 in Parquet format. Increasing the replication instance size (Option D) directly addresses the memory constraint by providing more RAM and processing capacity, which is necessary for handling large volumes of Change Data Capture (CDC) data and Parquet conversion overhead. This solution requires minimal architectural changes, as it only involves modifying the instance class in the DMS task settings.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Change the target format to JSON to reduce memory usage.
Why it's wrong here
Parquet is columnar and DMS buffers row groups in memory during write; switching to JSON does not reduce that buffering and adds per-row overhead, so the out-of-memory failure persists. JSON suits schema-flexible or document targets, not the near-real-time analytical Parquet pipeline the stem requires.
- ✗
Split the DMS task into multiple smaller tasks.
Why it's wrong here
Splitting into multiple tasks partitions tables across task instances, but each task still buffers its own full-load and CDC memory, so the underlying per-task memory limit is not raised. It fits parallelising throughput across many small tables, not a single large source.
- ✗
Use Change Data Capture (CDC) only, without full load.
Why it's wrong here
CDC-only skips the initial full load, so existing 2 TB rows never reach S3 and analytics are incomplete. It suits ongoing incremental replication after a baseline load, not resolving a memory failure during full load plus CDC.
- ✓
Increase the DMS replication instance size.
Why this is correct
DMS memory exhaustion during large-table replication is resolved by scaling the replication instance, adding RAM and CPU. This satisfies the 2 TB source constraint while preserving the existing task, endpoints and Parquet target architecture unchanged.
Visual reference
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 |
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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.