- A
Use Amazon EMR with Spark
Why wrong: EMR adds cluster management overhead and cost.
- B
Create a Glue DataBrew recipe and schedule the job using a cron expression
DataBrew supports scheduling directly.
- C
Create an AWS Lambda function triggered by S3 events
Why wrong: Lambda has time limits and is not designed for interactive data preparation.
- D
Use AWS Glue ETL with PySpark
Why wrong: Glue ETL is more expensive and complex for simple transformations.
Question 947 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 DataBrew to clean and transform data for analytics. The source data is in Parquet format in Amazon S3. The transformation includes filtering rows and adding calculated columns. What is the MOST cost-effective way to run these transformations on a schedule?
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
✓
Create a Glue DataBrew recipe and schedule the job using a cron expression
Option B is correct because AWS Glue DataBrew is purpose-built for visual data preparation, and scheduling a DataBrew recipe job with a cron expression directly meets the requirement to run filtering and column calculations on Parquet data in S3 without writing code. This is the most cost-effective approach as it avoids provisioning or managing compute resources beyond the serverless DataBrew job runs.
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 Amazon EMR with Spark
Why it's wrong here
EMR adds cluster management overhead and cost.
- ✓
Create a Glue DataBrew recipe and schedule the job using a cron expression
Why this is correct
DataBrew supports scheduling directly.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Create an AWS Lambda function triggered by S3 events
Why it's wrong here
Lambda has time limits and is not designed for interactive data preparation.
- ✗
Use AWS Glue ETL with PySpark
Why it's wrong here
Glue ETL is more expensive and complex for simple transformations.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates may over-engineer the solution by choosing Glue ETL or EMR, assuming that Parquet processing requires custom Spark code, when DataBrew's visual recipes can handle filtering and calculated columns without any code and at lower cost.
Detailed technical explanation
How to think about this question
DataBrew recipes are built on top of Glue Spark under the hood, automatically optimizing the execution plan for Parquet columnar storage by leveraging predicate pushdown and projection pushdown to minimize data scanned. When scheduling with cron, DataBrew uses AWS Glue workflows to trigger serverless Spark jobs, and costs are based on DPU-hours consumed only during job execution, with no idle cluster costs.
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.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
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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: Create a Glue DataBrew recipe and schedule the job using a cron expression — Option B is correct because AWS Glue DataBrew is purpose-built for visual data preparation, and scheduling a DataBrew recipe job with a cron expression directly meets the requirement to run filtering and column calculations on Parquet data in S3 without writing code. This is the most cost-effective approach as it avoids provisioning or managing compute resources beyond the serverless DataBrew job runs.
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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