DEA-C01 Data Ingestion and Transformation Practice Question
A data engineering team is building a pipeline to transform CSV files uploaded to Amazon S3 into Parquet format using AWS Glue. The transformation must be serverless and handle files that arrive at irregular intervals. Which TWO actions should the team take? (Choose two.)
⚠ Common exam trap
A common mix-up: candidates confuse serverless with managed services like EMR or Data Pipeline, or assume Redshift Spectrum can transform data, when in fact it only queries external formats without writing back converted files.
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 an AWS Glue ETL job to convert CSV to Parquet.
AWS Glue ETL jobs provide a serverless Spark-based environment that can directly read CSV files from S3 and write them as Parquet, meeting the requirement for serverless transformation. Glue handles schema inference and conversion without managing any infrastructure, making it ideal for irregularly scheduled data.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Configure an Amazon EMR cluster with Apache Spark for on-demand transformation.
Why it's wrong here
EMR requires cluster management and is not fully serverless.
- ✓
Use an AWS Glue ETL job to convert CSV to Parquet.
Why this is correct
Glue ETL jobs are serverless and can transform data formats.
- ✗
Use AWS Data Pipeline to schedule a periodic transformation.
Why it's wrong here
Data Pipeline is not serverless and requires provisioning resources.
- ✗
Use Amazon Redshift Spectrum to convert files during query execution.
Why it's wrong here
Redshift Spectrum queries data in place but does not convert file formats.
- ✓
Set up an S3 event notification to invoke an AWS Lambda function that triggers the Glue job.
Why this is correct
Lambda can react to S3 events and start the Glue job automatically.
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 |
Go deeper
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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.