SAP-C02 Design for New Solutions Practice Question
A company is designing a real-time analytics pipeline for IoT data. They need to ingest millions of messages per second, process them with low latency, and store results in Amazon S3. Which combination of services should they 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
✓
Amazon Kinesis Data Streams, Amazon Kinesis Data Analytics, Amazon Kinesis Data Firehose
Kinesis Data Streams ingests high-throughput data, Kinesis Data Analytics processes it in real-time, and Kinesis Data Firehose delivers the results to S3. Option B uses SQS, which is not designed for millions of messages per second, and Lambda may throttle under high load. Option C uses Redshift, which is a data warehouse; while streaming data can be loaded into Redshift, it is not a real-time streaming destination and typically requires Firehose. Option D uses IoT Core for ingestion, but DynamoDB is not optimized for storing large analytical results; S3 would be more appropriate.
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 Kinesis Data Streams, Amazon Kinesis Data Analytics, Amazon Kinesis Data Firehose
Why this is correct
Correct. Amazon Kinesis Data Streams can ingest millions of messages per second, Kinesis Data Analytics performs real-time processing, and Kinesis Data Firehose delivers the processed data to S3 with low latency.
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Amazon SQS, AWS Lambda, Amazon S3
Why it's wrong here
Incorrect. Amazon SQS is a message queue, not designed for high-throughput streaming ingestion. AWS Lambda may throttle under high concurrency, and S3 alone is not a real-time processing layer.
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Amazon Kinesis Data Streams, Amazon Redshift, Amazon S3
Why it's wrong here
Incorrect. Amazon Redshift is a data warehouse, not a real-time streaming target. While you can stream to Redshift, it typically requires staging in S3 via Firehose, and the combination lacks real-time analytics.
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Amazon IoT Core, AWS Lambda, Amazon DynamoDB
Why it's wrong here
Incorrect. Amazon IoT Core is for IoT device management and ingestion, but DynamoDB is a NoSQL database, not suitable for storing large-scale analytical results that need to be in S3. Lambda may also throttle.
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 |
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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.