- A
Convert the JSON files to CSV before processing with Glue.
Why wrong: CSV is less efficient; Parquet is better.
- B
Enable 'Group small files' in the Glue job or use a DynamicFrame with coalesce.
Grouping reduces the number of tasks and improves performance.
- C
Use an AWS Lambda function to pre-process the files.
Why wrong: Lambda adds complexity and cost.
- D
Increase the number of DPUs to the maximum.
Why wrong: Small files cause overhead regardless of DPUs.
Question 907 of 1,786
DEA-C01 Data Ingestion and Transformation Practice Question
This DEA-C01 practice question tests your understanding of data ingestion and transformation. Match the stated requirement to the specific cloud service, access model, or configuration option — many options are valid in isolation but not for this scenario. 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 needs to transform JSON data from Amazon S3 into Parquet format using AWS Glue. The source files are in a bucket with thousands of small files. What is the best practice to optimize the Glue job performance?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue:
"best"Why it matters: Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.
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
✓
Enable 'Group small files' in the Glue job or use a DynamicFrame with coalesce.
Option B is correct because enabling 'Group small files' in AWS Glue automatically coalesces thousands of small input files into larger partitions, reducing the number of tasks and minimizing overhead from task scheduling and S3 list operations. This is the recommended best practice for handling small files in Glue ETL jobs, as it optimizes read performance without requiring manual coalesce or repartitioning.
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.
- ✗
Convert the JSON files to CSV before processing with Glue.
Why it's wrong here
CSV is less efficient; Parquet is better.
- ✓
Enable 'Group small files' in the Glue job or use a DynamicFrame with coalesce.
Why this is correct
Grouping reduces the number of tasks and improves performance.
Clue confirmation
The clue word "best" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Use an AWS Lambda function to pre-process the files.
Why it's wrong here
Lambda adds complexity and cost.
- ✗
Increase the number of DPUs to the maximum.
Why it's wrong here
Small files cause overhead regardless of DPUs.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates assume more DPUs always improve performance, but for small files the bottleneck is metadata overhead, not compute capacity, so increasing DPUs without addressing file grouping leads to wasted resources and no speedup.
Detailed technical explanation
How to think about this question
Under the hood, 'Group small files' works by setting the 'groupSize' parameter (default 1 MB) in the Spark DataFrame reader, which instructs Glue to combine small files into larger splits before creating tasks. This reduces the number of partitions and avoids the 'small files problem' where metadata operations (e.g., S3 list and head requests) dominate execution time. In real-world scenarios with thousands of 10 KB JSON files, this optimization can reduce job runtime by 10x or more compared to default behavior.
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.
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 exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
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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: Enable 'Group small files' in the Glue job or use a DynamicFrame with coalesce. — Option B is correct because enabling 'Group small files' in AWS Glue automatically coalesces thousands of small input files into larger partitions, reducing the number of tasks and minimizing overhead from task scheduling and S3 list operations. This is the recommended best practice for handling small files in Glue ETL jobs, as it optimizes read performance without requiring manual coalesce or repartitioning.
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: "best". Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Last reviewed: Jul 4, 2026
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