DEA-C01 Data Operations and Support Practice Question
A data engineer needs to move data from an Amazon S3 bucket to an Amazon Redshift cluster on a daily schedule. The data is in CSV format and the target table already exists. Which AWS service should the engineer use to automate this task?
⚠ Common exam trap
DEA-C01 often tests the distinction between services that query data in place (Athena) versus those that move data (Glue). Candidates may pick Athena because it works with S3 and Redshift, but Athena does not load data into 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
✓
AWS Glue
AWS Glue is a fully managed extract, transform, and load (ETL) service that can schedule and run jobs to move data from S3 to Redshift. It provides built-in connectors for both S3 and Redshift, and you can define a crawler to infer the schema and a job to load the data into the existing Redshift table. Glue handles the scheduling, retries, and scaling, making it the ideal choice for this daily automated task.
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 Glue
Why this is correct
AWS Glue provides managed, serverless ETL with built-in schedulers and crawlers, so the engineer can automate the daily S3-to-Redshift load without provisioning servers. It reads CSV directly and writes to the existing Redshift table via JDBC, satisfying the daily schedule and existing-target constraints.
- ✗
Amazon Athena
Why it's wrong here
Amazon Athena queries data in place on S3 using SQL, returning results rather than writing rows into Redshift tables. It is tempting because it reads S3 CSV files, but it cannot perform the scheduled load into an existing table; AWS Glue's crawler and job do that.
- ✗
Amazon EMR
Why it's wrong here
Amazon EMR provisions Hadoop or Spark clusters for distributed processing, requiring cluster management and custom job code. It is tempting for large-scale transformation, but a scheduled CSV load into an existing Redshift table needs no processing cluster; AWS Glue handles the extract, transform and load directly.
- ✗
Amazon Kinesis Data Analytics
Why it's wrong here
Kinesis Data Analytics runs continuous SQL or Apache Flink over streaming data for real-time analytics. It is tempting because it processes data, but a daily batch CSV load into Redshift is not a streaming workload; AWS Glue's scheduled jobs perform the extract, transform and load.
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 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.