DEA-C01 Data Store Management Practice Question
A data engineer is designing a real-time analytics pipeline that ingests clickstream data into Amazon Kinesis Data Streams. The data must be stored in Amazon S3 for later analysis with Amazon Athena. The engineer needs the data to be queryable with minimal latency and wants to avoid managing complex ETL jobs. Which solution should the engineer use?
⚠ Common exam trap
The trap here is overlooking that Kinesis Data Firehose can perform schema-based record format conversion to Parquet, which is often assumed to require a separate ETL tool.
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
✓
Use Amazon Kinesis Data Firehose to deliver data to Amazon S3 with record format conversion to Parquet using an AWS Glue table.
Amazon Kinesis Data Firehose can ingest streaming data and deliver it to Amazon S3 with automatic record format conversion to Parquet using an AWS Glue table. This provides near-real-time, queryable data in a columnar format without custom ETL code. Other options require writing and managing Lambda functions, Kinesis Data Analytics applications, or Glue streaming jobs, which add complexity and do not offer the same level of managed simplicity.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use Amazon Kinesis Data Analytics to run SQL queries on the stream and write results to Amazon S3.
Why it's wrong here
Kinesis Data Analytics is designed for real-time stream processing and can output to S3, but it requires writing and managing SQL or Flink applications. It does not automatically convert formats to Parquet or manage partitioning for Athena. The scenario explicitly asks to avoid complex ETL jobs, so this option introduces unnecessary development and operational effort.
- ✓
Use Amazon Kinesis Data Firehose to deliver data to Amazon S3 with record format conversion to Parquet using an AWS Glue table.
Why this is correct
Kinesis Data Firehose can deliver streaming data to Amazon S3 and perform record format conversion to Parquet using a schema from the AWS Glue Data Catalog. This eliminates the need for custom ETL jobs and makes the data immediately queryable by Athena with columnar performance. It provides near-real-time delivery and automatic partitioning, meeting the latency and simplicity requirements.
- ✗
Use AWS Lambda to read from Kinesis Data Streams and write JSON files to Amazon S3.
Why it's wrong here
Writing JSON files to S3 requires custom Lambda code to handle batching, error retry, and partitioning. JSON is not columnar, so Athena queries will scan more data and perform worse. This approach adds operational overhead and does not provide the minimal-latency, managed ETL experience that the scenario demands. It also lacks built-in format conversion.
- ✗
Use AWS Glue streaming ETL jobs to read from Kinesis Data Streams and write Parquet to Amazon S3.
Why it's wrong here
AWS Glue streaming ETL jobs can process Kinesis streams and write Parquet to S3, but they require developing and maintaining Glue scripts, managing job bookmarks, and tuning worker capacity. This is more complex than using a fully managed delivery service. The scenario emphasizes minimal latency and avoiding complex ETL, making this option less suitable than a managed Firehose solution.
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.