SAP-C02 Design for New Solutions Practice Question
A company is designing a real-time analytics platform that ingests data from thousands of IoT devices. The platform must process and store high-velocity data with low latency. Which TWO AWS services should be used together to meet these requirements? (Choose TWO.)
⚠ Common exam trap
Test-takers frequently confuse Amazon Kinesis Data Streams with Amazon SQS or Amazon S3 for streaming ingestion, but SQS lacks ordered, replayable streams and S3 introduces latency, while Kinesis Data Streams is purpose-built for high-velocity, low-latency data ingestion and analytics.
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 Streams
Amazon Kinesis Data Streams (B) is correct because it is purpose-built to ingest high-velocity, real-time streaming data from thousands of producers such as IoT devices, providing low-latency, durable, and scalable stream capture with shards and configurable retention. Amazon Kinesis Data Analytics (C) is correct because it runs continuous SQL or Apache Flink queries directly on streaming data from Kinesis Data Streams, enabling real-time processing and analytics with sub-second latency without managing servers. Together, Kinesis Data Streams handles ingestion and Kinesis Data Analytics handles real-time processing, which matches the platform's low-latency, high-velocity requirements. AWS Lambda (A) is compute for event-driven functions but is not a streaming ingestion or stream-processing engine by itself, so it does not fulfill the ingestion-plus-real-time-analytics pairing. Amazon S3 (D) is object storage designed for durable batch storage, not low-latency stream ingestion or real-time processing. Amazon SQS (E) is a message queue for decoupling applications, not a high-throughput streaming data platform for real-time analytics.
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 Lambda
Why it's wrong here
Lambda is request-driven compute for event processing, not a durable high-velocity ingestion buffer; thousands of IoT devices need a stream store that retains and replays data. Lambda would be correct as the transformation layer consuming from Kinesis, not as the ingestion endpoint itself.
- ✓
Amazon Kinesis Data Streams
Why this is correct
Kinesis Data Streams ingests high-velocity device telemetry durably and with low latency, sharding throughput across many producers and retaining records for replay. It satisfies the ingestion half of the requirement, feeding downstream consumers such as Kinesis Data Analytics or Lambda for real-time processing.
- ✓
Amazon Kinesis Data Analytics
Why this is correct
Kinesis Data Analytics runs continuous SQL or Apache Flink queries directly over streaming data, producing insights within seconds. It satisfies the low-latency processing requirement by analysing the stream in flight rather than batching, complementing Kinesis Data Streams ingestion for the real-time analytics platform.
- ✗
Amazon S3
Why it's wrong here
S3 is for storage, not for real-time stream processing.
- ✗
Amazon SQS
Why it's wrong here
Amazon SQS is a decoupling queue, not a high-velocity ingestion or analytics engine; it cannot ingest thousands of IoT device streams or perform low-latency processing. It suits buffering messages between application tiers. The platform needs an ingestion service plus a stream-processing service working together.
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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