DEA-C01 Data Ingestion and Transformation Practice Question
A data engineer needs to transform JSON data into Parquet format using AWS Glue. The input data has nested fields. Which Glue feature should be used to flatten the nested structure?
⚠ Common exam trap
Candidates often confuse the Map transform (which can flatten JSON with custom code) with a built-in flattening feature, but AWS Glue's Relationalize is the dedicated, no-code solution for this specific task, and the exam expects you to know the exact purpose of each transform.
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
✓
Relationalize transform
The Relationalize transform is the correct choice because it is specifically designed to flatten nested JSON structures (such as arrays and structs) into a set of related tables (DataFrames) that can be written as Parquet. This transform recursively extracts nested fields, creating separate DataFrames for each level of nesting, which is essential for converting complex JSON into a flat, columnar Parquet format suitable for analytics.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Relationalize transform
Why this is correct
Relationalize is a Glue transform that converts nested, semi-structured data into a set of flat relational tables, unnesting arrays and structs into separate tables linked by join keys. It directly addresses flattening nested JSON before writing Parquet.
- ✗
DropNullFields transform
Why it's wrong here
DropNullFields removes records containing null fields; it does not restructure nested schemas. Flattening requires Relationalize, which converts nested JSON into separate related tables. DropNullFields is tempting when cleaning sparse data, where dropping null rows genuinely improves output quality, but it leaves nesting intact.
- ✗
FindMatches transform
Why it's wrong here
FindMatches is a machine learning transform that identifies duplicate records for deduplication, not schema restructuring. Flattening nested JSON needs Relationalize. FindMatches is tempting because it also processes nested data, but it outputs match groupings, not flattened columns, and would be correct for deduplicating customer records.
- ✗
Map transform
Why it's wrong here
The Map transform restructures keys and values within a DynamicFrame but does not flatten nested arrays or structs into top-level columns. It is tempting because it manipulates schema shape, yet Relationalize is the Glue feature purpose-built for unnesting nested JSON before writing Parquet.
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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.