- A
AWS Glue
Why wrong: AWS Glue is primarily a batch ETL service.
- B
Amazon EMR
Why wrong: EMR is for big data processing using frameworks like Spark, not real-time stream transformation.
- C
Amazon Kinesis Data Analytics
Kinesis Data Analytics provides real-time stream processing capabilities.
- D
Amazon Athena
Why wrong: Athena is a query service for S3, not for real-time transformation.
Question 1,780 of 1,786
Real-Time Transformation from Kinesis Data Streams to S3 with Kinesis Data Analytics
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 company is ingesting streaming data from IoT devices into Amazon Kinesis Data Streams. The data must be transformed in real-time and then stored in Amazon S3. Which AWS service should be used to perform the transformation?
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
Amazon Kinesis Data Analytics (now part of Amazon Managed Service for Apache Flink) is the correct choice because it can consume streaming data from Kinesis Data Streams, apply real-time transformations using SQL or Apache Flink, and then output the transformed data to destinations like Amazon S3. This directly meets the requirement for real-time transformation of streaming IoT data before storage.
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.
- ✗
AWS Glue
Why it's wrong here
AWS Glue is primarily a batch ETL service.
- ✗
Amazon EMR
Why it's wrong here
EMR is for big data processing using frameworks like Spark, not real-time stream transformation.
- ✓
Amazon Kinesis Data Analytics
Why this is correct
Kinesis Data Analytics provides real-time stream processing capabilities.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Amazon Athena
Why it's wrong here
Athena is a query service for S3, not for real-time transformation.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse AWS Glue's batch ETL capabilities with real-time streaming, leading them to select Glue despite its lack of native support for live Kinesis Data Streams processing.
Detailed technical explanation
How to think about this question
Under the hood, Kinesis Data Analytics for Apache Flink uses a Flink runtime that maintains state and exactly-once processing semantics, enabling complex event processing and windowed aggregations on streaming data. A subtle behavior is that the service automatically scales the underlying Flink application parallelism based on the Kinesis shard count, but you must configure the Kinesis stream with enough shards to handle the incoming data rate. In a real-world IoT scenario, this allows you to filter out noise, aggregate sensor readings over 1-minute tumbling windows, and write only meaningful summaries to S3, reducing storage costs.
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: Amazon Kinesis Data Analytics — Amazon Kinesis Data Analytics (now part of Amazon Managed Service for Apache Flink) is the correct choice because it can consume streaming data from Kinesis Data Streams, apply real-time transformations using SQL or Apache Flink, and then output the transformed data to destinations like Amazon S3. This directly meets the requirement for real-time transformation of streaming IoT data before storage.
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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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 →
Same concept, more angles
1 more ways this is tested on DEA-C01
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. A company is streaming IoT data from thousands of devices into Amazon Kinesis Data Streams. The data must be transformed in real time before being stored in Amazon S3. Which service should be used to perform the transformation as the data streams through Kinesis?
medium- A.AWS Glue
- ✓ B.Amazon Kinesis Data Analytics for Apache Flink
- C.Amazon EMR
- D.AWS Lambda
Why B: Amazon Kinesis Data Analytics for Apache Flink is the correct choice because it is purpose-built for running Apache Flink applications that can perform real-time transformations, filtering, and enrichment on data streaming through Kinesis Data Streams before outputting the results to destinations like Amazon S3. It integrates natively with Kinesis Data Streams as a source and can write transformed data directly to S3 using a Flink sink, making it ideal for this streaming ETL use case.
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Last reviewed: Jul 4, 2026
This DEA-C01 practice question is part of Courseiva's free Amazon Web Services certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the DEA-C01 exam.
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