- A
Use Amazon Kinesis Data Firehose to buffer and deliver data to S3, then use AWS Glue to convert to Parquet.
Firehose can ingest high throughput, buffer, and deliver to S3; Glue can run scheduled conversions.
- B
Use AWS Lambda to process each file as it arrives and write to S3.
Why wrong: Lambda has a concurrency limit and may not handle millions of files per hour cost-effectively.
- C
Use AWS Direct Connect to establish a dedicated network for file uploads.
Why wrong: Direct Connect is for network connectivity, not for data ingestion at the application layer.
- D
Use Amazon EMR to process the files as they arrive in S3.
Why wrong: EMR requires cluster management and may be more expensive than serverless options.
Question 588 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 designing a data ingestion pipeline that will receive millions of small JSON files per hour from external partners via API. The files should be stored in Amazon S3 and then transformed into Parquet for querying. Which approach is MOST cost-effective and scalable?
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 Amazon Kinesis Data Firehose to buffer and deliver data to S3, then use AWS Glue to convert to Parquet.
Amazon Kinesis Data Firehose is the most cost-effective and scalable approach because it can buffer millions of small JSON files per hour, automatically batch them, and deliver them to S3 without requiring any server management. After delivery, AWS Glue can efficiently convert the JSON data to Parquet format for optimized querying, leveraging its serverless, pay-per-use model that scales with data volume.
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 Kinesis Data Firehose to buffer and deliver data to S3, then use AWS Glue to convert to Parquet.
Why this is correct
Firehose can ingest high throughput, buffer, and deliver to S3; Glue can run scheduled conversions.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Use AWS Lambda to process each file as it arrives and write to S3.
Why it's wrong here
Lambda has a concurrency limit and may not handle millions of files per hour cost-effectively.
- ✗
Use AWS Direct Connect to establish a dedicated network for file uploads.
Why it's wrong here
Direct Connect is for network connectivity, not for data ingestion at the application layer.
- ✗
Use Amazon EMR to process the files as they arrive in S3.
Why it's wrong here
EMR requires cluster management and may be more expensive than serverless options.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often choose AWS Lambda for its simplicity, overlooking its concurrency limits, timeout constraints, and cost inefficiency when handling high-frequency, small-file ingestion at scale.
Detailed technical explanation
How to think about this question
Kinesis Data Firehose uses a buffer interval (default 60 seconds) and buffer size (default 5 MB) to aggregate incoming records before writing to S3, which reduces the number of S3 PUT requests and lowers costs. AWS Glue can convert JSON to Parquet using a serverless ETL job that leverages columnar storage and compression (e.g., Snappy), improving query performance in Amazon Athena or Redshift Spectrum by reducing I/O and storage footprint.
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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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 Amazon Kinesis Data Firehose to buffer and deliver data to S3, then use AWS Glue to convert to Parquet. — Amazon Kinesis Data Firehose is the most cost-effective and scalable approach because it can buffer millions of small JSON files per hour, automatically batch them, and deliver them to S3 without requiring any server management. After delivery, AWS Glue can efficiently convert the JSON data to Parquet format for optimized querying, leveraging its serverless, pay-per-use model that scales with data volume.
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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