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. Each device sends a JSON payload every second. The company needs to store the raw data for a month and then aggregate it into hourly summaries for long-term storage. The solution must be serverless and cost-effective. Which combination of AWS services should the company 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 to ingest data, Amazon Kinesis Data Analytics to aggregate in real-time, Amazon Kinesis Data Firehose to deliver aggregated data to S3, and an S3 Lifecycle policy to expire raw data after 30 days.
Kinesis Data Streams ingests real-time data, Kinesis Data Analytics performs real-time aggregation, and Kinesis Data Firehose delivers aggregated data to S3. An S3 Lifecycle policy can expire raw data after 30 days. Option A uses Lambda for aggregation, which is not ideal for streaming aggregations. Option C misses the real-time aggregation step. Option D uses SQS, which is not designed for real-time streaming, and DynamoDB is not suitable for raw data storage at high volumes.
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 to ingest data, AWS Lambda to transform and aggregate, Amazon S3 for storage.
Why it's wrong here
Lambda is not efficient for continuous streaming aggregation; it works per record or batch but not for sliding windows.
- ✓
Amazon Kinesis Data Streams to ingest data, Amazon Kinesis Data Analytics to aggregate in real-time, Amazon Kinesis Data Firehose to deliver aggregated data to S3, and an S3 Lifecycle policy to expire raw data after 30 days.
Why this is correct
This design uses serverless services for real-time ingestion, aggregation, and cost-effective storage.
- ✗
Amazon Kinesis Data Streams to ingest data, Amazon Kinesis Data Firehose to deliver to S3, and Amazon Athena to query raw data.
Why it's wrong here
Athena can query S3 but does not aggregate in real-time; also raw data stored for month in S3 is expensive.
- ✗
Amazon SQS to ingest data, AWS Lambda to process and aggregate, Amazon DynamoDB for raw data, S3 for summaries.
Why it's wrong here
SQS is not designed for high-throughput streaming; also DynamoDB for raw data is expensive.
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.