DEA-C01 Data Ingestion and Transformation Practice Question
A company uses AWS Glue ETL jobs to transform data in Amazon S3. The data arrives in JSON format but needs to be converted to Parquet for efficient querying. Which AWS Glue feature should be used to infer the schema and generate transformation code?
⚠ Common exam trap
Many candidates confuse AWS Glue crawlers with Amazon Athena or S3 Select, assuming any query or analysis tool can infer schemas for ETL, but only crawlers are designed to automatically discover and catalog schemas for Glue ETL jobs.
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 crawlers
AWS Glue crawlers are the correct feature because they automatically connect to data stores (like S3), infer the schema of JSON data by sampling it, and populate the AWS Glue Data Catalog with table definitions. This catalog schema can then be used by AWS Glue ETL jobs to generate transformation code (e.g., converting JSON to Parquet) without manual schema definition.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Amazon S3 Select
Why it's wrong here
S3 Select filters and retrieves subsets of a single object using SQL, returning results to the caller; it cannot infer schemas across a dataset or emit ETL code. It is tempting for querying JSON in place, but format conversion and code generation require AWS Glue crawlers and Studio.
- ✗
Amazon Athena
Why it's wrong here
Athena queries data in S3 using SQL and defines tables through its own DDL or a Glue Data Catalog crawler; it does not generate ETL transformation code. It is tempting because it reads JSON and Parquet, but conversion between formats and script generation belong to AWS Glue Studio.
- ✗
Amazon Kinesis Data Analytics
Why it's wrong here
Kinesis Data Analytics runs SQL or Apache Flink over streaming data; it neither crawls S3 to infer schemas nor generates ETL scripts. It is tempting for real-time transformation, but the scenario needs batch schema inference and code generation, which AWS Glue crawlers and the visual editor provide.
- ✓
AWS Glue crawlers
Why this is correct
Crawlers populate the Data Catalog with schema information used by Glue ETL jobs.
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
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