SAP-C02 Design for New Solutions Practice Question
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?
⚠ Common exam trap
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.
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.
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.
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.
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 |
Go deeper
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