DEA-C01 Data Ingestion and Transformation Practice Question
A data engineer is troubleshooting an AWS Glue ETL job that reads from Amazon S3 and writes to Amazon Redshift. The job runs successfully but writes duplicate rows into Redshift. The source data is static and does not contain duplicates. Which configuration change is most likely to resolve this issue?
⚠ Common exam trap
Candidates often assume duplicate rows come from the source data or a misconfiguration in the write mode, but the real cause is the default append behavior combined with Spark task retries, and the solution is to use post-write deduplication rather than changing the write mode or source processing.
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
✓
Modify the job to use the 'postactions' option with a SQL statement that deletes duplicates before final insert.
The job runs successfully but writes duplicate rows because AWS Glue's Spark-based ETL jobs can retry tasks on failure, and when writing to Redshift using the JDBC connector, the default behavior is to append data without deduplication. Using the 'postactions' option with a SQL DELETE statement that removes duplicates before the final INSERT ensures that only unique rows remain, resolving the duplication without altering the source data.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Enable the 'upsert' feature in the Redshift connection by setting 'update' to true.
Why it's wrong here
Upsert requires a unique key; if not properly configured, duplicates can still occur.
- ✓
Modify the job to use the 'postactions' option with a SQL statement that deletes duplicates before final insert.
Why this is correct
Using postactions to perform a MERGE or delete duplicates after staging can ensure idempotent writes.
- ✗
Use partition pruning on the S3 source to reduce the number of files read.
Why it's wrong here
Partition pruning reduces data scanned but does not prevent duplicate writes.
- ✗
Increase the number of DPUs (Data Processing Units) allocated to the job.
Why it's wrong here
More DPUs improve performance but do not prevent duplicates.
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 |
Go deeper
Related to this question
About these practice questions
Courseiva writes every DEA-C01 question from scratch — 1,711 in total, each with an explanation and a wrong-answer breakdown. None are copied from real exams or dumps. Learn why practice questions differ from exam dumps →
JA
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.