- A
Use AWS Glue crawlers to create a schema in the Data Catalog and then use a standard Spark DataFrame for transformation.
Why wrong: Crawlers may not handle schema evolution well.
- B
Use AWS Glue DynamicFrames to read the CSV files and apply transformations using resolveChoice and applyMapping.
DynamicFrames support schema evolution.
- C
Use a Python shell job in Glue to manually parse each file and write to parquet.
Why wrong: Python shell is less efficient and not recommended for ETL.
- D
Use a Glue ETL job with a static schema defined in the script and ignore files that don't match.
Why wrong: This would cause data loss.
Question 328 of 1,786
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 healthcare company is ingesting patient data from a legacy system into an Amazon S3 data lake using AWS Glue. The legacy system produces CSV files with inconsistent schemas (columns may appear or disappear in different files). The data engineer needs to create a Glue ETL job that can handle schema evolution and transform the data into a standardized parquet format. The job should also be able to process new files as they arrive. Which approach should the data engineer use?
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
✓
Use AWS Glue DynamicFrames to read the CSV files and apply transformations using resolveChoice and applyMapping.
Option B is correct because AWS Glue DynamicFrames support schema evolution by allowing schema-on-read, and the `resolveChoice` and `applyMapping` transformations can handle inconsistent schemas across CSV files. Option A is wrong because crawlers only catalog schemas, not perform ETL transformations. Option C is wrong because Python shell jobs are not designed for large-scale ETL and lack native schema evolution handling. Option D is wrong because a static schema would reject files with missing or extra columns, failing to handle schema evolution.
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.
- ✗
Use AWS Glue crawlers to create a schema in the Data Catalog and then use a standard Spark DataFrame for transformation.
Why it's wrong here
Crawlers may not handle schema evolution well.
- ✓
Use AWS Glue DynamicFrames to read the CSV files and apply transformations using resolveChoice and applyMapping.
Why this is correct
DynamicFrames support schema evolution.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Use a Python shell job in Glue to manually parse each file and write to parquet.
Why it's wrong here
Python shell is less efficient and not recommended for ETL.
- ✗
Use a Glue ETL job with a static schema defined in the script and ignore files that don't match.
Why it's wrong here
This would cause data loss.
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.
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 Ingestion and Transformation — study guide chapter
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FAQ
Questions learners often ask
What does this DEA-C01 question test?
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: Use AWS Glue DynamicFrames to read the CSV files and apply transformations using resolveChoice and applyMapping. — Option B is correct because AWS Glue DynamicFrames support schema evolution by allowing schema-on-read, and the `resolveChoice` and `applyMapping` transformations can handle inconsistent schemas across CSV files. Option A is wrong because crawlers only catalog schemas, not perform ETL transformations. Option C is wrong because Python shell jobs are not designed for large-scale ETL and lack native schema evolution handling. Option D is wrong because a static schema would reject files with missing or extra columns, failing to handle schema evolution.
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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Last reviewed: Jun 20, 2026
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