DEA-C01 Data Ingestion and Transformation Practice Question
A data engineer needs to run a transformation that processes semi-structured JSON records already stored in Amazon S3 and write the results back to S3 in Parquet format. The team prefers a serverless, Apache Spark-based approach with minimal infrastructure management and wants to use the AWS Glue Data Catalog for metadata. Which approach should the engineer use?
⚠ Common exam trap
Many exam-takers confuse the role of an AWS Glue crawler, which only catalogs schemas, with an AWS Glue ETL job, which actually transforms and rewrites data.
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
✓
Create an AWS Glue ETL job using the Spark engine and write output with the Glue Parquet writer.
AWS Glue ETL jobs provide a serverless Apache Spark runtime that reads JSON from Amazon S3, applies transformations, and writes Parquet using the Glue Parquet writer. This combination satisfies the serverless and Spark-based preferences while integrating with the AWS Glue Data Catalog for table metadata. EMR adds cluster management overhead, and Lambda or a crawler cannot perform the required rewrite.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Run an AWS Glue crawler to convert the JSON files to Parquet automatically.
Why it's wrong here
AWS Glue crawlers infer schemas and populate the Data Catalog; they do not transform or rewrite data. A crawler pointed at JSON will register tables describing that JSON, not produce Parquet files. Conversion requires an actual ETL job, so the crawler alone cannot fulfill the transformation requirement.
- ✗
Use AWS Lambda with pandas to convert each JSON file to Parquet.
Why it's wrong here
AWS Lambda has a 15-minute execution limit and constrained memory and ephemeral storage, making it unsuitable for Spark-style transformations over large or numerous JSON files. pandas conversion also lacks native Glue Data Catalog integration and would require custom catalog updates, so it does not meet the serverless Spark and catalog requirements.
- ✗
Launch an Amazon EMR cluster with Spark and submit the job manually.
Why it's wrong here
Amazon EMR provides managed Spark but still requires provisioning, sizing, and lifecycle management of a cluster, which contradicts the stated preference for minimal infrastructure management. It is a valid Spark option, yet the serverless requirement points to AWS Glue, which handles capacity automatically and integrates with the Data Catalog without cluster administration.
- ✓
Create an AWS Glue ETL job using the Spark engine and write output with the Glue Parquet writer.
Why this is correct
AWS Glue ETL jobs run on a serverless Apache Spark environment, so the engineer avoids managing clusters while gaining Spark's transformation capabilities. Reading JSON from S3, transforming it, and writing Parquet through the Glue Parquet writer produces columnar output optimized for analytics, and the job integrates natively with the Glue Data Catalog for schema and table metadata.
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 and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Amazon Web Services exam blueprint
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.