DEA-C01 Data Operations and Support Practice Question
A data engineer runs an AWS Glue ETL job that reads from an S3 bucket containing JSON files. The job fails with an error indicating that some records are malformed. The engineer wants to skip the malformed records and continue processing. Which approach should the engineer take?
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
✓
Configure the Glue DynamicFrame to use the `withErrorThreshold` option to skip corrupt records.
The `withErrorThreshold` option on an AWS Glue DynamicFrame allows the ETL job to skip a specified number of corrupt or malformed records without failing. This is the most direct way to handle malformed JSON records in Glue. Option A is incorrect because pre-processing all files is inefficient and may not be feasible for large datasets. Option B is incorrect because converting to Parquet does not address the issue of malformed JSON within the existing files. Option C is incorrect because AWS Glue Schema Registry validates schema compliance, not individual record malformation; it would reject entire datasets that don't match the schema, not skip malformed records.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Pre-process the JSON files to correct the malformed records before Glue reads them.
Why it's wrong here
Pre-processing adds complexity and may not be feasible.
- ✗
Convert the JSON files to Parquet format and use Glue to read Parquet.
Why it's wrong here
Converting format does not fix malformed JSON.
- ✗
Use AWS Glue Schema Registry to reject invalid records.
Why it's wrong here
Schema Registry validates schemas, not individual records.
- ✓
Configure the Glue DynamicFrame to use the `withErrorThreshold` option to skip corrupt records.
Why this is correct
Glue can skip malformed records using error thresholds.
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.