DEA-C01 Data Ingestion and Transformation Practice Question
An e-commerce company ingests clickstream data from their website into Amazon S3. The data is in JSON format, and each file is about 10 MB. They need to transform the data into a columnar format for analytics and load it into Amazon Redshift nightly. The transformation should be cost-effective and require minimal operational overhead. Which approach meets these requirements?
⚠ Common exam trap
Test-takers frequently choose Amazon EMR or Lambda because they are familiar with Spark or serverless functions, but they overlook the operational overhead of EMR and the execution limits of Lambda for batch workloads, while Glue provides a balanced, managed solution for this specific use case.
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 AWS Glue ETL job to convert to Parquet and load into Redshift.
AWS Glue ETL is the correct choice because it is a serverless, managed service that can efficiently convert JSON to Parquet (a columnar format optimized for Redshift) and load the data into Redshift with minimal operational overhead. The nightly batch processing of 10 MB files is well-suited for Glue's pay-per-use pricing, making it cost-effective without requiring infrastructure management.
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 AWS Glue ETL job to convert to Parquet and load into Redshift.
Why this is correct
Serverless and minimal overhead.
- ✗
Use Amazon Redshift COPY command to load JSON directly.
Why it's wrong here
Does not transform to columnar format.
- ✗
Use Amazon EMR with Spark to transform and load data.
Why it's wrong here
Requires cluster management.
- ✗
Use AWS Lambda to transform each file and write to Redshift.
Why it's wrong here
Timeout and scalability issues.
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.