- A
Increase the number of workers and the worker type to G.2X to handle the memory errors, and enable job retries.
Why wrong: This addresses memory but not the risk of partial data being visible to Athena.
- B
Replace the Glue job with an AWS Lambda function that processes the CSV files and writes Parquet to S3, and use S3 Event Notifications to trigger the function.
Why wrong: Lambda has timeout and memory limits, and still risks partial writes.
- C
Modify the Glue job to use job bookmarks for incremental processing and write the Parquet output to a temporary location, then use an S3 copy operation to move the data into the final partitioned location only after the job completes successfully.
Bookmarks prevent reprocessing; atomic move ensures Athena sees complete data.
- D
Use Athena partition projection to automatically discover partitions and set up a retry mechanism using AWS Step Functions.
Why wrong: Partition projection does not solve the atomicity of writes.
Quick Answer
The answer is to modify the Glue job to use job bookmarks for incremental processing and write the Parquet output to a temporary location, then use an S3 copy operation to move the data into the final partitioned location only after the job completes successfully. This approach solves the atomic writes failure by staging data in a temporary prefix, ensuring that Athena queries never see partial or incomplete data from a failed Glue job. On the AWS Certified Data Engineer Associate DEA-C01 exam, this scenario tests your understanding of job bookmarks for incremental processing and the staging pattern to guarantee data consistency in S3 data lakes. A common trap is assuming that simply scaling up resources prevents partial writes, but memory errors can still cause mid-run failures. Remember the memory tip: “Stage before you page” — stage data in a temp location, then move it atomically to the final partition to avoid querying incomplete data.
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 is responsible for a data pipeline that uses Amazon S3 as a data lake, AWS Glue for ETL, and Amazon Athena for ad-hoc queries. The pipeline ingests CSV files from an external partner via SFTP into an S3 bucket. The files are then processed by a Glue job that converts them to Parquet and writes to a separate S3 bucket partitioned by date. The Glue job runs daily and is triggered by a scheduled CloudWatch Events rule. Recently, the data engineer noticed that some days the Glue job fails because of memory errors, and on those days the Athena queries that rely on the data return incomplete results. The engineer needs to ensure that the pipeline is resilient and that Athena queries always see a complete view of the data, even if the Glue job fails mid-run. The engineer also needs to minimize re-processing of data. Which course of action should the engineer take?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue:
"always"Why it matters: Absolute qualifier. An answer using 'always' is only correct if there are genuinely no exceptions — absolute statements are often wrong in networking.
Clue:
"minimum / minimize"Why it matters: Asks for the least resource use — fewest addresses, smallest subnet, lowest overhead. Eliminate over-provisioned options even if they would technically work.
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 Glue job to use job bookmarks for incremental processing and write the Parquet output to a temporary location, then use an S3 copy operation to move the data into the final partitioned location only after the job completes successfully.
Option B is correct. Using Glue job bookmarks enables incremental processing and the ability to resume from the last successful checkpoint. Staging the data in a temporary location and moving it atomically ensures that Athena sees only complete data. Option A is wrong because increasing worker capacity does not prevent partial writes. Option C is wrong because using Lambda for conversion is less scalable and error-prone. Option D is wrong because partition projection does not solve the atomicity 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.
- ✗
Increase the number of workers and the worker type to G.2X to handle the memory errors, and enable job retries.
Why it's wrong here
This addresses memory but not the risk of partial data being visible to Athena.
- ✗
Replace the Glue job with an AWS Lambda function that processes the CSV files and writes Parquet to S3, and use S3 Event Notifications to trigger the function.
Why it's wrong here
Lambda has timeout and memory limits, and still risks partial writes.
- ✓
Modify the Glue job to use job bookmarks for incremental processing and write the Parquet output to a temporary location, then use an S3 copy operation to move the data into the final partitioned location only after the job completes successfully.
Why this is correct
Bookmarks prevent reprocessing; atomic move ensures Athena sees complete data.
Clue confirmation
The clue words "always", "minimum / minimize" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Use Athena partition projection to automatically discover partitions and set up a retry mechanism using AWS Step Functions.
Why it's wrong here
Partition projection does not solve the atomicity of writes.
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.
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Data Operations and Support — study guide chapter
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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: Modify the Glue job to use job bookmarks for incremental processing and write the Parquet output to a temporary location, then use an S3 copy operation to move the data into the final partitioned location only after the job completes successfully. — Option B is correct. Using Glue job bookmarks enables incremental processing and the ability to resume from the last successful checkpoint. Staging the data in a temporary location and moving it atomically ensures that Athena sees only complete data. Option A is wrong because increasing worker capacity does not prevent partial writes. Option C is wrong because using Lambda for conversion is less scalable and error-prone. Option D is wrong because partition projection does not solve the atomicity 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.
Are there clue words in this question I should notice?
Yes — watch for: "always", "minimum / minimize". Absolute qualifier. An answer using 'always' is only correct if there are genuinely no exceptions — absolute statements are often wrong in networking.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 20, 2026
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.
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