DEA-C01 Data Ingestion and Transformation Practice Question
A company is streaming IoT sensor data to Amazon Kinesis Data Streams. The data is JSON with a schema that changes occasionally. They want to load the data into Amazon S3 in Parquet format partitioned by date and sensor_id. Which approach is MOST cost-effective and operationally efficient?
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 with a schema from AWS Glue Data Catalog to convert to Parquet and enable dynamic partitioning by date and sensor_id.
Amazon Kinesis Data Firehose can directly convert incoming JSON data to Parquet using a schema from AWS Glue Data Catalog, and it supports dynamic partitioning by date and sensor_id without requiring custom code. This is the most cost-effective and operationally efficient approach as it is a fully managed service that handles buffering, conversion, and partitioning automatically. Option A (EMR) is overkill for this use case and adds operational complexity. Option B (Lambda) would require additional transformation logic and is less efficient for high-throughput streaming. Option C (custom KCL application on EC2) requires ongoing management and is not as simple as using Firehose.
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 EMR to read from Kinesis Data Streams and write to S3 in Parquet format.
Why it's wrong here
Using Amazon EMR to read from Kinesis Data Streams and write to S3 in Parquet is overkill for this use case and adds operational complexity; it is not as cost-effective or efficient as a fully managed service.
- ✗
Use a Lambda function to transform records to Parquet and write to S3.
Why it's wrong here
Using a Lambda function to transform records to Parquet and write to S3 would require additional transformation logic and is less efficient for high-throughput streaming compared to a managed service.
- ✗
Use a custom Kinesis Client Library application on EC2 to buffer and write Parquet files to S3.
Why it's wrong here
Using a custom Kinesis Client Library application on EC2 to buffer and write Parquet files to S3 requires ongoing management and is not as simple as using Kinesis Data Firehose.
- ✓
Use Amazon Kinesis Data Firehose with a schema from AWS Glue Data Catalog to convert to Parquet and enable dynamic partitioning by date and sensor_id.
Why this is correct
Amazon Kinesis Data Firehose can directly convert incoming JSON data to Parquet using a schema from AWS Glue Data Catalog, and supports dynamic partitioning by date and sensor_id without custom code. It is fully managed, making it the most cost-effective and operationally efficient.
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 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.