DEA-C01 Data Ingestion and Transformation Practice Question
A data engineer is tasked with transforming JSON data from an S3 bucket into Parquet format for efficient querying. The transformation should run on a schedule every hour. Which AWS service is best suited for this task?
⚠ Common exam trap
A common mix-up: candidates confuse Athena's ability to query Parquet with the ability to transform data into Parquet, but Athena is a query engine, not an ETL service, and cannot perform scheduled data format conversions.
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
✓
AWS Glue
AWS Glue is the best choice because it is a fully managed ETL service designed specifically for transforming and cataloging data at scale. It can natively read JSON from S3, convert it to Parquet, and run on a scheduled hourly basis using a Glue job with a trigger, without requiring server management or custom infrastructure.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
AWS Lambda
Why it's wrong here
Lambda caps execution at 15 minutes and lacks native Spark or distributed processing for large JSON-to-Parquet conversions. It is tempting because Lambda is serverless and event-driven, and it would be the right choice for small, short-lived per-object transformations triggered by S3 events.
- ✗
Amazon Athena
Why it's wrong here
Athena queries data in place via SQL and writes results, but it is not a transformation scheduler for producing Parquet datasets hourly. It is tempting because Athena reads S3 and supports CTAS output, and it would be correct for ad-hoc analytical querying rather than scheduled ETL.
- ✓
AWS Glue
Why this is correct
AWS Glue satisfies the hourly schedule constraint through time-based triggers, and its ETL engine converts JSON to Parquet using built-in transforms that infer schemas automatically. Crawlers catalogue the S3 source, while the Parquet output is written directly to S3 for efficient downstream querying by Athena or Redshift Spectrum.
- ✗
Amazon EMR
Why it's wrong here
While Amazon EMR can transform JSON to Parquet using Spark or Hive, it introduces significant overhead for a simple scheduled hourly job, as it requires provisioning and managing a cluster of EC2 instances, which is unnecessary for this lightweight, recurring task. The temptation arises because EMR is a powerful tool for large-scale, complex data processing pipelines, such as running multi-stage ETL jobs on petabytes of data, where its distributed computing capabilities would be the correct choice.
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
Related to this question
About these practice questions
One of 1,321 original DEA-C01 practice questions on Courseiva, each with a full explanation and wrong-answer analysis — not exam dumps or protected exam content. Learn why practice questions differ from exam dumps →
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.