Incremental Data Migration from On-Prem to S3 with DMS and Glue
A team wants to move data from an on-premises Oracle database to Amazon S3 for analytics. The pipeline must run daily and handle incremental updates. Which THREE services should they use together? (Choose three.)
Quick Answer
This is another full-pipeline 'choose three' question, and the three services line up with what a daily, incrementally-updating migration from an on-premises database actually requires: a way to capture changes from the source, a place to land them, and a way to transform them for analytics. AWS Database Migration Service is the natural choice for pulling data out of the on-premises Oracle database and handling incremental updates, since DMS supports ongoing replication that captures changes continuously rather than requiring a full re-extraction of the source data on every run. Amazon S3 serves as the destination, providing scalable, durable, and cost-effective object storage that's the standard landing zone for data destined for analytics workloads, and it integrates directly with both DMS's replication output and Glue's processing input. AWS Glue rounds out the pipeline as the transformation layer, taking the raw incremental data that DMS has replicated into S3 and applying whatever ETL logic is needed to prepare it for analytics, all as a serverless service that doesn't require the team to manage transformation infrastructure themselves. Together, these three services map cleanly onto the classic extract-load-transform pattern for a continuously-updating on-premises source: DMS extracts and loads incrementally, S3 stores durably, and Glue transforms for downstream use. Whenever a scenario describes moving data on a recurring schedule from an on-premises relational database into an S3-based analytics pipeline with incremental updates, expect the correct combination of services to include a CDC-capable migration service, S3 as the storage layer, and Glue for transformation.
⚠ Common exam trap
A common mix-up: candidates confuse Amazon Athena as a data ingestion service because it can query S3 data, but it is purely a query engine and cannot move or replicate data from an on-premises database.
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 destination for storing the data because it provides a scalable, durable, and cost-effective object storage solution ideal for analytics workloads. The pipeline requires daily incremental updates, and S3 integrates seamlessly with AWS DMS for continuous replication and AWS Glue for ETL processing, making it the central storage layer for the analytics pipeline.
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 SageMaker
Why it's wrong here
SageMaker is for building and training ML models, not for data ingestion.
- ✓
Amazon S3
Why this is correct
S3 is the target data lake storage.
- ✗
Amazon Athena
Why it's wrong here
Athena queries data but does not move it.
- ✓
AWS Database Migration Service (DMS)
Why this is correct
DMS supports ongoing replication from Oracle to S3.
- ✓
AWS Glue
Why this is correct
Glue can transform data after it lands in S3.
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
Related to this question
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Same concept, more angles
1 more way this is tested on MLS-C01
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. Which TWO AWS services can be used to move data from an on-premises database to Amazon S3 on a recurring schedule without writing custom code? (Choose 2.)
medium- ✓ A.AWS Glue
- B.AWS Snowball Edge
- ✓ C.AWS Database Migration Service (AWS DMS)
- D.Amazon Athena
- E.Amazon Kinesis Data Firehose
Why A: AWS Glue is correct because it provides a fully managed ETL service that can run crawlers and jobs on a recurring schedule to extract data from on-premises databases (via JDBC connections) and write it to Amazon S3 without requiring any custom code. AWS DMS is correct because it supports continuous replication or scheduled tasks to migrate data from on-premises databases to S3 as a target, using built-in transformation capabilities and no custom scripting.
JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This MLS-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 MLS-C01 exam.