- A
Use AWS Glue streaming ETL to write to S3 in real-time.
Why wrong: Glue streaming does not natively handle late data with Firehose partitioning.
- B
Use Kinesis Data Analytics with a tumbling window to write to S3.
Why wrong: Data Analytics does not support custom partitioning or late data handling.
- C
Use Kinesis Data Firehose with a Lambda transformation to write to S3, and a separate Lambda consumer to reprocess late data from the stream.
Firehose handles real-time delivery with partitioning; Lambda can reprocess late records.
- D
Use Kinesis Data Firehose to deliver to S3, and use S3 Batch Operations to process late data.
Why wrong: Batch Operations are not suitable for streaming late data.
Question 507 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 engineer is designing a real-time analytics pipeline for clickstream data. The source is Amazon Kinesis Data Streams, and the data must be stored in Amazon S3 in partitioned Parquet format with near-real-time latency. The engineer must also handle late-arriving data (up to 1 hour). Which combination of services meets these requirements?
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 Kinesis Data Firehose with a Lambda transformation to write to S3, and a separate Lambda consumer to reprocess late data from the stream.
Option A is incorrect because AWS Glue streaming ETL, while capable of handling late data via Spark Structured Streaming's watermarking, is not ideal for near-real-time Parquet partitioning to S3 at high throughput; it is more suited for complex transformations with micro-batches. Option B is incorrect: Kinesis Data Analytics tumbling windows process data in memory and output to destinations like S3, but they lack built-in late data handling and Parquet conversion. Option C is correct: Kinesis Data Firehose delivers streaming data to S3 with near-real-time latency, and a Lambda transformation converts it to Parquet. To handle late-arriving data (up to 1 hour), a separate Lambda consumer reads from the Kinesis Data Streams shard iterator to reprocess records that arrive after the Firehose delivery window, ensuring no data loss. Option D is incorrect: S3 Batch Operations are designed for batch processing of existing objects, not for near-real-time handling of late-arriving streaming 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.
- ✗
Use AWS Glue streaming ETL to write to S3 in real-time.
Why it's wrong here
Glue streaming does not natively handle late data with Firehose partitioning.
- ✗
Use Kinesis Data Analytics with a tumbling window to write to S3.
Why it's wrong here
Data Analytics does not support custom partitioning or late data handling.
- ✓
Use Kinesis Data Firehose with a Lambda transformation to write to S3, and a separate Lambda consumer to reprocess late data from the stream.
Why this is correct
Firehose handles real-time delivery with partitioning; Lambda can reprocess late records.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Use Kinesis Data Firehose to deliver to S3, and use S3 Batch Operations to process late data.
Why it's wrong here
Batch Operations are not suitable for streaming late data.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates assume Kinesis Data Firehose alone can handle late-arriving data via its buffering settings, but Firehose does not reprocess records that arrive after its delivery window; a separate consumer is required to explicitly handle late data from the stream.
Detailed technical explanation
How to think about this question
Kinesis Data Firehose buffers incoming data for up to 900 seconds or 128 MB before delivering to S3, enabling near-real-time writes. The Lambda transformation function can convert records to Parquet using libraries like PyArrow, and the separate Lambda consumer uses the Kinesis Client Library (KCL) to process records with a shard iterator that can start from a specific timestamp, allowing reprocessing of late data within the 1-hour retention window. In practice, this pattern is used for clickstream analytics where late events (e.g., from mobile devices with poor connectivity) must be merged into existing partitions without overwriting.
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
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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 Kinesis Data Firehose with a Lambda transformation to write to S3, and a separate Lambda consumer to reprocess late data from the stream. — Option A is incorrect because AWS Glue streaming ETL, while capable of handling late data via Spark Structured Streaming's watermarking, is not ideal for near-real-time Parquet partitioning to S3 at high throughput; it is more suited for complex transformations with micro-batches. Option B is incorrect: Kinesis Data Analytics tumbling windows process data in memory and output to destinations like S3, but they lack built-in late data handling and Parquet conversion. Option C is correct: Kinesis Data Firehose delivers streaming data to S3 with near-real-time latency, and a Lambda transformation converts it to Parquet. To handle late-arriving data (up to 1 hour), a separate Lambda consumer reads from the Kinesis Data Streams shard iterator to reprocess records that arrive after the Firehose delivery window, ensuring no data loss. Option D is incorrect: S3 Batch Operations are designed for batch processing of existing objects, not for near-real-time handling of late-arriving streaming 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.
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Last reviewed: Jul 4, 2026
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