DEA-C01 Data Ingestion and Transformation Practice Question
A data engineer is building an AWS Glue ETL job that reads from an Amazon S3 bucket containing nested JSON files. The job must flatten the nested structure and write the output to Amazon Redshift. The engineer notices that some JSON records have missing fields and inconsistent schemas. Which AWS Glue feature should be used to handle these inconsistencies and ensure the job does not fail?
⚠ Common exam trap
The trap here is thinking that a predefined schema or manual catalog edits can handle runtime schema variations, but they do not provide the flexibility needed for inconsistent JSON.
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 a Glue DynamicFrame with the resolveChoice transform to handle schema inconsistencies.
AWS Glue DynamicFrames are specifically built to handle semi-structured data with evolving schemas. The resolveChoice transform lets you resolve ambiguous or conflicting schema types, such as choosing a specific data type or dropping nulls. This ensures the ETL job can process nested JSON with missing fields without failing, making it the correct choice for this scenario.
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 a Glue DataFrame with a predefined schema and set the job to ignore malformed records.
Why it's wrong here
A predefined schema may not accommodate missing fields or type variations, leading to job failures or data loss. Ignoring malformed records does not address schema inconsistencies and may silently drop valid data. This approach is rigid and not suited for evolving JSON schemas.
- ✗
Use Amazon Kinesis Data Firehose to transform the JSON before the Glue job reads it.
Why it's wrong here
Kinesis Data Firehose is for streaming data and does not integrate with batch Glue jobs reading from S3. It cannot flatten nested JSON or resolve schema inconsistencies for this batch scenario. Using it would add unnecessary complexity and is not the right tool for the job.
- ✗
Use AWS Glue crawlers to infer the schema and then manually edit the Data Catalog table to add missing columns.
Why it's wrong here
Crawlers can infer schema, but manually editing the Data Catalog does not handle runtime schema variations within the job. It also does not prevent job failures when encountering unexpected fields. This is a manual, error-prone process that does not scale with changing data.
- ✓
Use a Glue DynamicFrame with the resolveChoice transform to handle schema inconsistencies.
Why this is correct
DynamicFrame and resolveChoice are designed to handle schema variability in semi-structured data. They allow you to specify how to resolve ambiguous types or missing fields, such as casting to a specific type or dropping nulls. This prevents job failures due to inconsistent schemas and is the recommended approach for nested JSON with varying structures.
Visual reference
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.