- A
Add a filter in Glue to remove rows with null transaction_id.
Why wrong: Adding a filter to remove rows with null transaction_id would not resolve the root cause; the nulls are introduced by the mapping transformation, and filtering them out could also remove valid data that only appears null due to the mapping error.
- B
Increase the Redshift WLM concurrency scaling to handle more queries.
Why wrong: Increasing Redshift WLM concurrency scaling helps with query performance under high load, but the error is a null constraint violation, not a performance issue. Concurrency scaling does not fix the data transformation error.
- C
Review the Glue job's mapping transformation to ensure transaction_id is correctly mapped and not dropped.
The error shows that transaction_id is being written as null to Redshift despite source files having non-null values. Reviewing and correcting the Glue job's mapping transformation to ensure transaction_id is correctly mapped and not dropped will resolve the issue.
- D
Increase the number of Glue workers to handle peak-hour load.
Why wrong: Increasing the number of Glue workers can improve job performance but does not address the null value problem; the issue lies in the data transformation logic, not resource constraints.
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 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
Review the Glue job's mapping transformation to ensure transaction_id is correctly mapped and not dropped.
Option C is correct. The error indicates that transaction_id is being nullified or dropped during the Glue job's mapping transformation. Even though source files have non-null values, the mapping could be incorrectly mapping or omitting the column, causing nulls to be written to Redshift. The failure during peak hours is coincidental; the root cause is the mapping logic. Option A is incorrect because filtering nulls would not fix the mapping error and could discard valid data. Option B is incorrect because increasing Redshift WLM concurrency scaling does not address the null constraint violation. Option D is incorrect because more Glue workers do not fix the transformation issue; the problem is data quality, not capacity.
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
Adding a filter to remove rows with null transaction_id would not resolve the root cause; the nulls are introduced by the mapping transformation, and filtering them out could also remove valid data that only appears null due to the mapping error.
- ✗
Increase the Redshift WLM concurrency scaling to handle more queries.
Why it's wrong here
Increasing Redshift WLM concurrency scaling helps with query performance under high load, but the error is a null constraint violation, not a performance issue. Concurrency scaling does not fix the data transformation error.
- ✓
Review the Glue job's mapping transformation to ensure transaction_id is correctly mapped and not dropped.
Why this is correct
The error shows that transaction_id is being written as null to Redshift despite source files having non-null values. Reviewing and correcting the Glue job's mapping transformation to ensure transaction_id is correctly mapped and not dropped will resolve the issue.
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
Increasing the number of Glue workers can improve job performance but does not address the null value problem; the issue lies in the data transformation logic, not resource constraints.
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.
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 |
What to study next
Got this wrong? Here's your next step.
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FAQ
Questions learners often ask
What does this DEA-C01 question test?
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 indicates that transaction_id is being nullified or dropped during the Glue job's mapping transformation. Even though source files have non-null values, the mapping could be incorrectly mapping or omitting the column, causing nulls to be written to Redshift. The failure during peak hours is coincidental; the root cause is the mapping logic. Option A is incorrect because filtering nulls would not fix the mapping error and could discard valid data. Option B is incorrect because increasing Redshift WLM concurrency scaling does not address the null constraint violation. Option D is incorrect because more Glue workers do not fix the transformation issue; the problem is data quality, not capacity.
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.
About these practice questions
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Last reviewed: Jun 20, 2026
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