- A
Amazon Kinesis Data Streams with AWS Lambda for transformation and Amazon S3.
Why wrong: Requires custom exactly-once logic; Lambda has no native Parquet conversion.
- B
Amazon Simple Queue Service (SQS) with AWS Lambda for transformation and Amazon S3.
Why wrong: SQS does not natively convert to Parquet or partition data.
- C
AWS Glue streaming jobs consuming from Amazon Kinesis Data Streams and writing to Amazon S3.
Why wrong: Glue streaming jobs are more complex and do not directly integrate with IoT Core.
- D
Amazon Kinesis Data Firehose with data transformation via AWS Lambda, delivering to Amazon S3.
Firehose supports Parquet conversion and partitioning; Lambda handles transformation.
Question 1,517 of 1,786
DEA-C01 Data Ingestion and Transformation Practice Question
This DEA-C01 practice question tests your understanding of data ingestion and transformation. This is a configuration task: choose the command set that satisfies every stated requirement. Small differences — like 'secret' vs 'password' or 'transport input ssh' vs 'all' — change whether the answer is correct. 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 is designing a data ingestion pipeline for IoT sensor data. The data arrives as JSON via AWS IoT Core, and must be stored in Amazon S3 in partitioned Parquet format. The pipeline must handle late-arriving data (up to 1 hour) and ensure exactly-once processing. Which combination of services should the engineer use?
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
✓
Amazon Kinesis Data Firehose with data transformation via AWS Lambda, delivering to Amazon S3.
Amazon Kinesis Data Firehose is the correct choice because it can directly ingest streaming data from AWS IoT Core, use a built-in AWS Lambda function to transform JSON to Parquet, and deliver the data to Amazon S3 with automatic partitioning. It also supports buffering and retry logic to handle late-arriving data (up to 1 hour) and provides exactly-once delivery to S3 when configured with the appropriate error handling and idempotent transformations.
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.
- ✗
Amazon Kinesis Data Streams with AWS Lambda for transformation and Amazon S3.
Why it's wrong here
Requires custom exactly-once logic; Lambda has no native Parquet conversion.
- ✗
Amazon Simple Queue Service (SQS) with AWS Lambda for transformation and Amazon S3.
Why it's wrong here
SQS does not natively convert to Parquet or partition data.
- ✗
AWS Glue streaming jobs consuming from Amazon Kinesis Data Streams and writing to Amazon S3.
Why it's wrong here
Glue streaming jobs are more complex and do not directly integrate with IoT Core.
- ✓
Amazon Kinesis Data Firehose with data transformation via AWS Lambda, delivering to Amazon S3.
Why this is correct
Firehose supports Parquet conversion and partitioning; Lambda handles transformation.
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 often choose Kinesis Data Streams with Lambda (Option A) because they think it offers more control, but they overlook that Firehose provides a managed, exactly-once, partitioned Parquet delivery pipeline with built-in late-arriving data handling, which is the exact requirement in the question.
Detailed technical explanation
How to think about this question
Kinesis Data Firehose uses a buffer interval (up to 900 seconds) and buffer size (up to 128 MB) to accumulate records before writing to S3, which allows it to handle late-arriving data within the buffer window. The Lambda transformation function can convert JSON to Parquet using libraries like PyArrow, and Firehose automatically partitions data in S3 using keys like year/month/day/hour. Exactly-once processing is achieved by enabling 'ErrorOutputPrefix' and configuring the Lambda function to be idempotent, combined with Firehose's internal retry mechanism that avoids duplicate writes to the final S3 prefix.
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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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: Amazon Kinesis Data Firehose with data transformation via AWS Lambda, delivering to Amazon S3. — Amazon Kinesis Data Firehose is the correct choice because it can directly ingest streaming data from AWS IoT Core, use a built-in AWS Lambda function to transform JSON to Parquet, and deliver the data to Amazon S3 with automatic partitioning. It also supports buffering and retry logic to handle late-arriving data (up to 1 hour) and provides exactly-once delivery to S3 when configured with the appropriate error handling and idempotent transformations.
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: Jun 11, 2026
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