- A
Amazon EMR running Spark Structured Streaming
Why wrong: EMR can do stateful streaming but requires cluster management, increasing operational overhead.
- B
Amazon Kinesis Data Firehose with Lambda transformation
Why wrong: Lambda has a 15-minute timeout and is stateless; not suitable for complex stateful operations.
- C
AWS Glue streaming ETL job
Why wrong: Glue streaming is based on Spark and has limited stateful support; less mature than Flink.
- D
Amazon Kinesis Data Analytics for Apache Flink
Flink supports stateful stream processing, exactly what is needed.
Question 1,717 of 1,786
DEA-C01 Data Ingestion and Transformation Practice Question
This DEA-C01 practice question tests your understanding of data ingestion and transformation. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. 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 streaming pipeline that ingests data from an Amazon Kinesis Data Stream (with 5 shards) into Amazon S3. The data must be transformed using a complex stateful operation that cannot be done in a Lambda function (limited to 15 minutes). The engineer needs a solution that can maintain state across multiple records. Which service should be used?
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 Analytics for Apache Flink
Amazon Kinesis Data Analytics for Apache Flink is the correct choice because it supports stateful stream processing with exactly-once semantics, can maintain state across multiple records, and has no 15-minute execution limit like AWS Lambda. It natively integrates with Kinesis Data Streams and can sink transformed data to S3, meeting all requirements for complex stateful operations.
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 EMR running Spark Structured Streaming
Why it's wrong here
EMR can do stateful streaming but requires cluster management, increasing operational overhead.
- ✗
Amazon Kinesis Data Firehose with Lambda transformation
Why it's wrong here
Lambda has a 15-minute timeout and is stateless; not suitable for complex stateful operations.
- ✗
AWS Glue streaming ETL job
Why it's wrong here
Glue streaming is based on Spark and has limited stateful support; less mature than Flink.
- ✓
Amazon Kinesis Data Analytics for Apache Flink
Why this is correct
Flink supports stateful stream processing, exactly what is needed.
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 confuse AWS Glue streaming ETL (which is Spark-based and better for batch-oriented transformations) with a true stateful streaming engine, or they assume Kinesis Data Firehose can handle stateful logic via Lambda, not realizing Lambda's stateless nature and timeout limit.
Detailed technical explanation
How to think about this question
Apache Flink, the engine behind Kinesis Data Analytics, uses a distributed snapshot mechanism (Checkpoints) to maintain operator state consistently across failures, enabling exactly-once processing even with stateful operations like windowed aggregations or pattern matching. Under the hood, Flink stores state in a configurable state backend (e.g., RocksDB or in-memory) and periodically snapshots it to durable storage (e.g., S3), allowing recovery without data loss. In a real-world scenario, this is critical for use cases like fraud detection where you need to track user session state across thousands of events without losing intermediate results.
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.
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: Amazon Kinesis Data Analytics for Apache Flink — Amazon Kinesis Data Analytics for Apache Flink is the correct choice because it supports stateful stream processing with exactly-once semantics, can maintain state across multiple records, and has no 15-minute execution limit like AWS Lambda. It natively integrates with Kinesis Data Streams and can sink transformed data to S3, meeting all requirements for complex stateful operations.
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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