The correct answer is to modify the job to use the 'postactions' option with a SQL statement that deletes duplicates before the final insert. This resolves the duplicate rows issue because AWS Glue’s Spark-based ETL jobs can retry tasks upon failure, and when writing to Amazon Redshift via the JDBC connector, the default behavior is to append data without any deduplication logic. On the AWS Certified Data Engineer Associate DEA-C01 exam, this scenario tests your understanding of Glue-to-Redshift ETL patterns and the importance of idempotent writes, often appearing as a trap where candidates mistakenly blame the source data or suggest changing the write mode to overwrite. The key insight is that static source data is not the problem—the duplication stems from Glue’s retry mechanism combined with Redshift’s append-only default. A useful memory tip: think of postactions as a “cleanup crew” that runs after the job writes, ensuring only unique rows survive.
DEA-C01 Data Ingestion and Transformation Practice Question
This DEA-C01 practice question tests your understanding of data ingestion and transformation. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
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?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue: "most likely"
Why it matters: Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
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.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
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.
Clue confirmation
The clue word "most likely" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
✗
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.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that 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.
Detailed technical explanation
How to think about this question
Under the hood, AWS Glue ETL jobs use Apache Spark, which can retry failed stages or tasks, and the Redshift JDBC connector writes data in batches using COPY or INSERT statements. If a task fails after partially writing data and is retried, the same rows may be written again, leading to duplicates. The 'postactions' option allows executing SQL statements after the write, such as a DELETE using a window function like `ROW_NUMBER()` to remove duplicates, ensuring idempotent writes without modifying the source.
KKey Concepts to Remember
Read the scenario before looking for a memorised answer.
Find the constraint that changes the correct option.
Eliminate answers that are true in general but not in this case.
TExam Day Tips
→Watch for words such as best, first, most likely and least administrative effort.
→Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A media company stores terabytes of video archives that are accessed once a year for audit purposes. Moving these objects to a cold storage tier (Azure Archive, S3 Glacier, or Google Nearline) costs a fraction of hot storage. Questions like this test whether you understand storage tiers, access frequency tradeoffs, and retrieval latency requirements.
What to study next
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Data Ingestion and Transformation — This question tests Data Ingestion and Transformation — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: 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.
What should I do if I get this DEA-C01 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Are there clue words in this question I should notice?
Yes — watch for: "most likely". Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Question Discussion
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