- A
Use Amazon Kinesis Data Firehose to ingest data and AWS Glue for processing.
Why wrong: Firehose does not provide exactly-once semantics.
- B
Use Amazon EMR with Spark Streaming to process data from Kinesis Data Streams.
Why wrong: EMR can do it but is more complex than needed.
- C
Use AWS Lambda to process records from Kinesis Data Streams and store in S3.
Why wrong: Lambda is not suited for stateful windowed aggregations.
- D
Use Amazon Kinesis Data Analytics for Apache Flink to process data from Kinesis Data Streams and output to S3.
Flink provides exactly-once processing and handles late data.
Question 1,436 of 1,746
Windowed Aggregations with Kinesis Data Analytics for Apache Flink
This SAP-C02 practice question tests your understanding of design for new solutions. 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 designing a new real-time analytics platform that processes streaming data from IoT devices. The data must be ingested, processed with windowed aggregations, and stored in Amazon S3 for long-term analytics. The solution must handle late-arriving data and provide exactly-once processing semantics. Which combination of AWS services should the architect 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
✓
Use Amazon Kinesis Data Analytics for Apache Flink to process data from Kinesis Data Streams and output to S3.
Option D is correct because Amazon Kinesis Data Analytics for Apache Flink provides built-in support for windowed aggregations, exactly-once processing semantics, and handling late-arriving data via allowed lateness and watermarking. It can output processed results directly to Amazon S3 using a Flink sink, meeting all requirements for a real-time analytics platform.
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 ingest data and AWS Glue for processing.
Why it's wrong here
Firehose does not provide exactly-once semantics.
- ✗
Use Amazon EMR with Spark Streaming to process data from Kinesis Data Streams.
Why it's wrong here
EMR can do it but is more complex than needed.
- ✗
Use AWS Lambda to process records from Kinesis Data Streams and store in S3.
Why it's wrong here
Lambda is not suited for stateful windowed aggregations.
- ✓
Use Amazon Kinesis Data Analytics for Apache Flink to process data from Kinesis Data Streams and output to S3.
Why this is correct
Flink provides exactly-once processing and handles late data.
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 AWS Lambda or Kinesis Data Firehose for simplicity, overlooking the need for stateful windowed aggregations and exactly-once processing, which are not natively supported by those services.
Detailed technical explanation
How to think about this question
Kinesis Data Analytics for Apache Flink uses Flink's checkpointing mechanism to achieve exactly-once semantics by periodically saving the state of operators and offsets to a durable store (e.g., Amazon S3 or DynamoDB). For late-arriving data, Flink's event time processing with watermarking allows the system to define a threshold (e.g., 5 minutes) for how late events can be before they are considered late, and then handle them via side outputs or allowed lateness. In a real-world scenario, IoT devices with intermittent connectivity may send data out of order; Flink's managed service handles this without custom code for retries or deduplication.
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 SAP-C02 question test?
Design for New Solutions — This question tests Design for New Solutions — 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 Analytics for Apache Flink to process data from Kinesis Data Streams and output to S3. — Option D is correct because Amazon Kinesis Data Analytics for Apache Flink provides built-in support for windowed aggregations, exactly-once processing semantics, and handling late-arriving data via allowed lateness and watermarking. It can output processed results directly to Amazon S3 using a Flink sink, meeting all requirements for a real-time analytics platform.
What should I do if I get this SAP-C02 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
This SAP-C02 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 SAP-C02 exam.
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