- A
Configure an Amazon EMR cluster with Apache Spark for on-demand transformation.
Why wrong: EMR requires cluster management and is not fully serverless.
- B
Use an AWS Glue ETL job to convert CSV to Parquet.
Glue ETL jobs are serverless and can transform data formats.
- C
Use AWS Data Pipeline to schedule a periodic transformation.
Why wrong: Data Pipeline is not serverless and requires provisioning resources.
- D
Use Amazon Redshift Spectrum to convert files during query execution.
Why wrong: Redshift Spectrum queries data in place but does not convert file formats.
- E
Set up an S3 event notification to invoke an AWS Lambda function that triggers the Glue job.
Lambda can react to S3 events and start the Glue job automatically.
Question 1,126 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 engineering team is building a pipeline to transform CSV files uploaded to Amazon S3 into Parquet format using AWS Glue. The transformation must be serverless and handle files that arrive at irregular intervals. Which TWO actions should the team take? (Choose two.)
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 an AWS Glue ETL job to convert CSV to Parquet.
Option B is correct because AWS Glue ETL jobs provide a serverless Spark-based environment that can directly read CSV files from S3 and write them as Parquet, meeting the requirement for serverless transformation. Glue handles schema inference and conversion without managing any infrastructure, making it ideal for irregularly scheduled data.
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.
- ✗
Configure an Amazon EMR cluster with Apache Spark for on-demand transformation.
Why it's wrong here
EMR requires cluster management and is not fully serverless.
- ✓
Use an AWS Glue ETL job to convert CSV to Parquet.
Why this is correct
Glue ETL jobs are serverless and can transform data formats.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Use AWS Data Pipeline to schedule a periodic transformation.
Why it's wrong here
Data Pipeline is not serverless and requires provisioning resources.
- ✗
Use Amazon Redshift Spectrum to convert files during query execution.
Why it's wrong here
Redshift Spectrum queries data in place but does not convert file formats.
- ✓
Set up an S3 event notification to invoke an AWS Lambda function that triggers the Glue job.
Why this is correct
Lambda can react to S3 events and start the Glue job automatically.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates may confuse serverless with managed services like EMR or Data Pipeline, or assume Redshift Spectrum can transform data, when in fact it only queries external formats without writing back converted files.
Detailed technical explanation
How to think about this question
AWS Glue ETL jobs use Apache Spark under the hood, with a DynamicFrame abstraction that handles schema evolution and data type inference from CSV headers. When converting to Parquet, Glue can leverage columnar compression and predicate pushdown for downstream analytics, and the job can be triggered via S3 event notifications to Lambda, which starts the Glue job using the boto3 start_job_run API. This pattern ensures near-real-time processing without polling or scheduled intervals.
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.
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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 an AWS Glue ETL job to convert CSV to Parquet. — Option B is correct because AWS Glue ETL jobs provide a serverless Spark-based environment that can directly read CSV files from S3 and write them as Parquet, meeting the requirement for serverless transformation. Glue handles schema inference and conversion without managing any infrastructure, making it ideal for irregularly scheduled data.
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.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
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Last reviewed: Jul 4, 2026
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