DEA-C01 Data Ingestion and Transformation Practice Question
A company is migrating a legacy on-premises ETL pipeline to AWS. The pipeline processes daily batch files from an FTP server. The data must be transformed using complex business logic before being loaded into Amazon Redshift. Which THREE AWS services should be used for this migration?
⚠ Common exam trap
The trap is confusing batch ETL with streaming (Kinesis) or picking Athena as a transformation service; candidates may also forget Transfer Family for FTP ingestion and choose only Glue and Redshift.
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 Redshift
AWS Transfer Family (E) is correct because it provides a fully managed FTP/FTPS/SFTP endpoint that can land the legacy daily batch files directly into Amazon S3, replacing the on-premises FTP server without changing the source's protocol. AWS Glue (C) is correct because it is a serverless ETL service whose Spark-based jobs and Glue Data Catalog can implement the complex business logic transformations on the batch data before loading. Amazon Redshift (B) is correct because it is the target data warehouse where the transformed data is loaded for analytics, and Glue can write to it via the Redshift JDBC/ODBC connection or the Redshift data source. Amazon Athena (A) is not appropriate because it is an interactive query service over S3, not an ETL engine for applying complex transformation logic. Amazon Kinesis Data Streams (D) is not appropriate because it is designed for real-time streaming ingestion, whereas this pipeline processes daily batch files.
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 Athena
Why it's wrong here
Athena queries data already in Amazon S3 using SQL; it cannot extract files from FTP or perform complex multi-step transformations before loading Redshift. It is tempting for S3 analytics, but the pipeline needs AWS Glue for ETL and transformation logic.
- ✓
Amazon Redshift
Why this is correct
Amazon Redshift serves as the target data warehouse, satisfying the requirement to load transformed data. Its massively parallel processing architecture handles analytical queries over the daily batch volumes, and native integration with AWS Glue and Amazon S3 enables efficient bulk loads via COPY commands after transformation completes.
- ✓
AWS Glue
Why this is correct
AWS Glue provides the serverless Spark engine that applies the complex business logic transformations before loading. It satisfies the stem's transformation requirement, reading batch files and writing refined output into Amazon Redshift without managing infrastructure.
- ✗
Amazon Kinesis Data Streams
Why it's wrong here
Kinesis Data Streams ingests real-time streaming records, not scheduled batch files from FTP. It is tempting as an AWS ingestion service, but daily batch landing requires Amazon S3 with AWS Glue or Data Pipeline, while Kinesis suits continuous low-latency event streams.
- ✓
AWS Transfer Family
Why this is correct
AWS Transfer Family provides a fully managed SFTP, FTPS and FTP endpoint backed by Amazon S3, replacing the on-premises FTP server without managing EC2 instances. It satisfies the stem's requirement to ingest daily batch files from an FTP source, landing them in S3 for subsequent transformation and loading into Amazon Redshift.
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
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.