- A
Change the input format from Parquet to CSV to simplify parsing.
Why wrong: CSV is less efficient than Parquet for analytics; would increase data volume and slow processing.
- B
Coalesce the input data into a single large file before processing.
Why wrong: Single file reduces parallelism, likely increasing runtime.
- C
Use column pruning and predicate pushdown to read only necessary columns and filter data early.
Reduces the amount of data processed, improving performance and reducing costs.
- D
Increase the number of workers to maximum allowed.
Why wrong: More workers increase cost and may cause overhead; not optimal without need.
Question 1,543 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 company uses AWS Glue ETL jobs to transform data stored in Amazon S3. The job reads data in Parquet format, applies transformations, and writes the output back to S3 in Parquet format. The team wants to improve the job's performance and reduce costs. Which action is MOST effective?
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 column pruning and predicate pushdown to read only necessary columns and filter data early.
Option C is correct because column pruning and predicate pushdown reduce the amount of data read from S3 by Spark-based AWS Glue ETL jobs. By reading only the necessary columns and filtering rows early in the scan, I/O and memory usage decrease, directly improving performance and reducing costs.
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.
- ✗
Change the input format from Parquet to CSV to simplify parsing.
Why it's wrong here
CSV is less efficient than Parquet for analytics; would increase data volume and slow processing.
- ✗
Coalesce the input data into a single large file before processing.
Why it's wrong here
Single file reduces parallelism, likely increasing runtime.
- ✓
Use column pruning and predicate pushdown to read only necessary columns and filter data early.
Why this is correct
Reduces the amount of data processed, improving performance and reducing costs.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Increase the number of workers to maximum allowed.
Why it's wrong here
More workers increase cost and may cause overhead; not optimal without need.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse 'coalesce' (reducing partitions) with 'repartition' (increasing parallelism) and assume fewer files always improve performance, ignoring that Glue ETL benefits from parallel reads across many small files when using columnar formats.
Detailed technical explanation
How to think about this question
Parquet stores data in a columnar format with embedded statistics (min, max, null counts) per row group. AWS Glue uses Spark's Catalyst optimizer to push down filters and prune columns at the file scan level, skipping entire row groups that don't match predicates. This is especially effective on large datasets where only a subset of columns or rows are needed for transformations.
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 startup's cloud architect reviews their monthly bill and notices costs are higher than expected for a long-running batch job. Switching from on-demand instances to Reserved Instances — or using Spot/Preemptible VMs — can reduce compute costs by up to 72 %. Questions like this test whether you understand the tradeoffs between commitment, flexibility, and cost across cloud pricing models.
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 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 column pruning and predicate pushdown to read only necessary columns and filter data early. — Option C is correct because column pruning and predicate pushdown reduce the amount of data read from S3 by Spark-based AWS Glue ETL jobs. By reading only the necessary columns and filtering rows early in the scan, I/O and memory usage decrease, directly improving performance and reducing costs.
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.
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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