DEA-C01 Data Ingestion and Transformation Practice Question
A data engineer needs to transform JSON data from Amazon S3 into Parquet format using AWS Glue. The data contains nested fields. Which Glue feature should the engineer use to define the schema and handle the nested structure?
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 the 'Relationalize' transform in a Glue ETL script.
The 'Relationalize' transform in AWS Glue is specifically designed to convert nested JSON data into flat tables by extracting and flattening nested fields into separate relational tables. This allows handling complex nested structures effectively. Option A is incorrect because 'FindMatches' is used for deduplication, not for handling nested data. Option B is incorrect because 'DropFields' is used to remove fields from a dataset, not to transform nested structures. Option D is incorrect because 'Spigot' is used for writing sample data for testing or debugging, not for schema definition or handling nesting.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use the 'FindMatches' transform to identify duplicates.
Why it's wrong here
FindMatches is for deduplication, not schema definition.
- ✗
Use the 'DropFields' transform to remove nested fields.
Why it's wrong here
DropFields removes fields, does not handle nesting.
- ✓
Use the 'Relationalize' transform in a Glue ETL script.
Why this is correct
Relationalize flattens nested JSON into relational tables.
- ✗
Use the 'Spigot' transform to write sample data.
Why it's wrong here
Spigot is for sampling data.
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 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.