DEA-C01 Data Ingestion and Transformation Practice Question
A data engineer is building an AWS Glue job that reads semi-structured JSON from Amazon S3 and must flatten nested arrays into relational columns before writing to Amazon Redshift. The transformation logic is complex and the engineer wants to unit test it locally without provisioning a cluster. Which Glue capability should the engineer use to develop and test this transformation logic?
⚠ Common exam trap
The trap here is equating AWS Glue Studio notebooks or interactive sessions with local development, when only the installable Glue ETL library actually runs on the developer's machine.
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
✓
The AWS Glue ETL library (awsglue) run locally with a Python environment and sample datasets.
The AWS Glue ETL library can be installed locally so developers run the same transformation code against sample data without any managed cluster. This enables genuine unit tests of complex nested-array flattening. The visual editor, interactive sessions, and DataBrew all depend on AWS-hosted execution and therefore cannot satisfy the requirement for local testing before deployment.
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 Glue DataBrew recipe steps executed against a sample dataset.
Why it's wrong here
DataBrew provides visual data preparation recipes that run on the DataBrew service, not in a local Python test harness. It is aimed at analysts preparing data interactively rather than engineers writing and unit testing custom Spark transformations. It cannot execute the arbitrary complex flattening code described, and it still requires the managed service to run, so it fails the local-testing requirement.
- ✗
AWS Glue interactive sessions with an AWS Glue Studio notebook.
Why it's wrong here
Interactive sessions let you develop and debug Glue ETL code interactively, but they still run on Glue-managed infrastructure in the AWS account and incur session cost. They speed up iterative development yet do not provide a fully local environment for isolated unit tests. The scenario specifically asks for local testing without provisioning a cluster, which interactive sessions do not deliver.
- ✓
The AWS Glue ETL library (awsglue) run locally with a Python environment and sample datasets.
Why this is correct
The AWS Glue ETL library can be installed and run in a local Python environment, allowing the engineer to execute transformation functions against sample DataFrames without any Glue cluster. This supports true local unit testing of complex flattening logic. It matches the requirement to develop and test without provisioning managed infrastructure, and the same code can later run on Glue with minimal changes.
- ✗
AWS Glue Studio visual job editor with a custom transform node.
Why it's wrong here
The Glue Studio visual editor lets you build jobs graphically and add custom transform nodes, but it runs against the Glue service rather than providing a local unit-testing harness. It is useful for authoring but does not give the isolation needed to run assertions against sample data on a developer machine. It does not directly satisfy the requirement for local testing of complex logic.
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
Courseiva writes every DEA-C01 question from scratch — 1,321 in total, each with an explanation and a wrong-answer breakdown. None are copied from real exams or dumps. Learn why practice questions differ from exam dumps →
JA
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.