DEA-C01 Data Ingestion and Transformation Practice Question
A data engineer needs to ingest data from an on-premises Oracle database into Amazon S3. The data volume is about 500 GB initially, with daily incremental updates of 10 GB. The pipeline must minimize operational overhead. Which AWS service should be used for the initial and incremental loads?
⚠ Common exam trap
Watch out — candidates often choose AWS Glue for its serverless nature, but Glue's incremental crawl only updates the Data Catalog, not the data itself, and it cannot capture row-level changes from a database without full reloads.
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
✓
AWS Database Migration Service (DMS) with change data capture (CDC) to Amazon S3.
AWS DMS with CDC is the correct choice because it supports continuous replication from Oracle to Amazon S3 with minimal overhead. It handles both the initial 500 GB full load and ongoing 10 GB daily increments via change data capture, without requiring custom code or complex pipeline management.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
AWS Database Migration Service (DMS) with change data capture (CDC) to Amazon S3.
Why this is correct
AWS DMS with change data capture performs the initial full load and then continuously replicates ongoing changes from Oracle to Amazon S3, so incremental updates need no custom scripting or scheduled jobs, satisfying the minimal-operational-overhead requirement.
- ✗
AWS Glue with a JDBC connection and incremental crawl.
Why it's wrong here
AWS Glue with a JDBC connection reads Oracle through Spark, requiring provisioned DPUs and custom incremental-detection logic, so operational overhead stays high. It is tempting because Glue catalogues and transforms JDBC sources, and would suit ongoing ETL where transformation, not bulk ingestion, is the goal.
- ✗
Amazon Kinesis Data Firehose with a custom producer.
Why it's wrong here
Kinesis Data Firehose ingests streaming records, not batch table extracts, so a custom producer cannot perform the initial 500 GB Oracle dump or detect row-level changes. It is tempting because Firehose delivers to S3 with no servers, and would be correct for continuous event streams rather than daily database increments.
- ✗
AWS Data Pipeline with a SQL activity and HiveCopyActivity.
Why it's wrong here
AWS Data Pipeline requires defining SQL and HiveCopy activities plus scheduling infrastructure, adding configuration overhead the scenario forbids. It is tempting because it historically moved on-premises relational data to S3, and would suit legacy scheduled JDBC copies where the engineer accepts managing pipeline definitions.
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.