DEA-C01 Data Ingestion and Transformation Practice Question
A data engineering team is ingesting streaming data from IoT devices using AWS IoT Core and needs to process the data in near real-time with minimal code. Which AWS service should they use to transform the data before storing it in Amazon S3?
⚠ Common exam trap
Many exam-takers confuse AWS Glue's streaming ETL capability (which still requires writing Scala or Python code and managing checkpointing) with the 'minimal code' requirement, or they mistakenly think Amazon Athena can transform data before it lands in S3, when in fact Athena only queries data already stored.
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 Kinesis Data Analytics
Amazon Kinesis Data Analytics (now part of Amazon Managed Service for Apache Flink) is the correct choice because it allows you to transform streaming data in near real-time using SQL or Apache Flink with minimal code. It can directly consume data from AWS IoT Core via Kinesis Data Streams or Amazon MSK, apply transformations like filtering, aggregation, or enrichment, and then output the processed data to Amazon S3 without requiring custom application servers.
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 Kinesis Data Analytics
Why this is correct
Kinesis Data Analytics can run SQL queries on streaming data from IoT Core in near real-time.
- ✗
AWS Glue
Why it's wrong here
Glue is a batch ETL service, not designed for near real-time streaming.
- ✗
Amazon Redshift
Why it's wrong here
Redshift is a data warehouse, not a real-time transformation service.
- ✗
Amazon Athena
Why it's wrong here
Athena is for querying data in S3, not for real-time processing.
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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JA
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.