The correct answer is to review the Glue job's mapping transformation to ensure transaction_id is correctly mapped and not dropped. This resolves the null constraint violation because the error indicates that the column is being nullified during the ETL process, even though the source Parquet files contain valid values. The intermittent failure during peak Redshift concurrency is a red herring; the core issue is a mapping logic error in the DynamicFrame transformation, not a Redshift capacity problem. On the AWS Certified Data Engineer Associate DEA-C01 exam, this scenario tests your ability to distinguish between data integrity issues and infrastructure bottlenecks—a common trap is to blame Redshift concurrency when the root cause is a dropped column in the Glue mapping. Remember the memory tip: "Map it, don't drop it"—always verify that column mappings are complete before blaming the database.
DEA-C01 Data Operations and Support Practice Question
This DEA-C01 practice question tests your understanding of data operations and support. 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 at a financial services company manages an AWS Glue ETL pipeline that processes transaction data from Amazon S3 to Amazon Redshift for reporting. The pipeline runs every hour and uses a Glue job that reads Parquet files, performs transformations in Spark, and writes to Redshift using the JDBC connector. Recently, the job has been failing intermittently with the error: 'java.sql.BatchUpdateException: ERROR: null value in column "transaction_id" violates not-null constraint'. The data engineer has verified that the source Parquet files do contain non-null values for transaction_id. The job uses a DynamicFrame and applies a mapping to rename columns. The engineer also noticed that the failure occurs only during peak hours when there is high concurrency on Redshift. Which course of action should the engineer take to resolve this issue?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Review the Glue job's mapping transformation to ensure transaction_id is correctly mapped and not dropped.
Option C is correct. The error suggests that some rows are being written with null transaction_id. During high concurrency, Redshift might be rejecting the batch due to a transient issue, but the error is about null constraint. The most likely cause is that the mapping is incorrectly dropping or nullifying the column. Option A is wrong because increasing Glue's worker count does not address the null value issue. Option B is wrong because increasing Redshift WLM concurrency could exacerbate the problem. Option D is wrong because the source files are not the issue.
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.
✗
Add a filter in Glue to remove rows with null transaction_id.
Why it's wrong here
The source does not have nulls; filtering would lose data.
✗
Increase the Redshift WLM concurrency scaling to handle more queries.
Why it's wrong here
Does not fix the null value issue.
✓
Review the Glue job's mapping transformation to ensure transaction_id is correctly mapped and not dropped.
Why this is correct
The mapping may have a bug that sets transaction_id to null.
Related concept
Read the scenario before looking for a memorised answer.
✗
Increase the number of Glue workers to handle peak-hour load.
Why it's wrong here
The error is about null values, not capacity.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Many certification questions include familiar terms but test a specific constraint. Read the exact wording before choosing an answer that is generally true but wrong for this case.
Detailed technical explanation
How to think about this question
This question should be treated as a scenario, not a definition check. Identify the problem, the constraint and the best action. Then compare each option against those facts.
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.
Use explanations to understand the rule behind the answer.
TExam Day Tips
→Underline the problem statement mentally.
→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 DEA-C01 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
Data Operations and Support — This question tests Data Operations and Support — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Review the Glue job's mapping transformation to ensure transaction_id is correctly mapped and not dropped. — Option C is correct. The error suggests that some rows are being written with null transaction_id. During high concurrency, Redshift might be rejecting the batch due to a transient issue, but the error is about null constraint. The most likely cause is that the mapping is incorrectly dropping or nullifying the column. Option A is wrong because increasing Glue's worker count does not address the null value issue. Option B is wrong because increasing Redshift WLM concurrency could exacerbate the problem. Option D is wrong because the source files are not the issue.
What should I do if I get this DEA-C01 question wrong?
Identify which DEA-C01 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
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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