DEA-C01 Data Ingestion and Transformation Practice Question
A company uses Amazon Kinesis Data Firehose to ingest application logs into an Amazon S3 bucket. The logs are in JSON format. The data engineering team wants to convert the logs from JSON to Parquet format before landing in S3. What is the most cost-effective way to achieve this?
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
✓
Configure the Firehose delivery stream to convert the data to Parquet using a schema from AWS Glue.
Kinesis Data Firehose can convert the input data format to Parquet using a schema from AWS Glue. Option A is incorrect because Athena queries raw data and would not help with ingestion. Option C is incorrect because Lambda can do this but would incur additional compute cost. Option D is incorrect because Glue ETL would add cost and latency.
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 Athena to query the JSON data and write results in Parquet format.
Why it's wrong here
Amazon Athena can be used to query and convert data, but it would require an additional query step after ingestion, incurring Athena costs and latency. It is not the most cost-effective for real-time conversion during ingestion.
- ✓
Configure the Firehose delivery stream to convert the data to Parquet using a schema from AWS Glue.
Why this is correct
Configure the Firehose delivery stream to convert the data to Parquet using a schema from AWS Glue. This is the most cost-effective because Firehose handles the conversion natively without additional compute services.
- ✗
Use an AWS Lambda function to transform each record to Parquet and send to Firehose.
Why it's wrong here
Using a Lambda function adds compute cost and complexity. While possible, it is less cost-effective than using Firehose's built-in conversion.
- ✗
Use an AWS Glue ETL job to run on a schedule and convert JSON to Parquet in S3.
Why it's wrong here
AWS Glue ETL jobs are excellent for batch processing and transforming data formats like JSON to Parquet, making this option tempting. However, it fails this scenario because the requirement is to convert the logs *before landing in S3*. A Glue ETL job operates on data *already landed* in S3, necessitating a separate, scheduled process to read, transform, and write the data back. This introduces a two-stage ingestion and transformation, which is not the most cost-effective for continuous streams requiring pre-landing conversion. Glue would be the correct choice for converting existing JSON data in S3 or for post-ingestion batch transformations.
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.