Question 1,188 of 1,660
SAP-C02 Design for New Solutions Practice Question
A company is designing a new application on AWS that processes real-time IoT sensor data from thousands of devices. The data must be ingested, processed, and stored for analysis. The company wants to use a serverless architecture to reduce operational overhead. The processing includes filtering, aggregation, and transformation. Which solution should a Solutions Architect recommend?
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 AWS IoT Core to ingest data, use IoT rules to route data to Kinesis Data Analytics for real-time processing, and store results in S3.
AWS IoT Core is purpose-built for ingesting data from IoT devices, its rules engine can route data to Kinesis Data Analytics for real-time processing using SQL, and results can be stored in S3 for analysis. Option A is incorrect because Kinesis Data Firehose delivers data in batches, not real-time processing as required. Option B is incorrect because while Lambda can process, it may have concurrency limits and is less optimal for high-throughput streaming compared to Kinesis Data Analytics. Option C is incorrect because SQS is not designed for real-time streaming ingestion from IoT devices; it's a message queue, not a streaming service.
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 Streams to ingest data, use Kinesis Data Firehose to deliver data to S3, and use Athena for queries.
Why it's wrong here
Firehose delivers data in batches (60 secs), not real-time processing; Athena is for ad-hoc queries.
- ✗
Use Amazon Kinesis Data Streams to ingest data, trigger a Lambda function for processing, and store results in DynamoDB.
Why it's wrong here
Lambda can process from Kinesis but may not handle high throughput efficiently; also no filtering/aggregation built-in.
- ✗
Use Amazon SQS to ingest sensor data, trigger a Lambda function for processing, and store results in DynamoDB.
Why it's wrong here
SQS is not designed for real-time streaming ingestion; it's a message queue.
- ✓
Use AWS IoT Core to ingest data, use IoT rules to route data to Kinesis Data Analytics for real-time processing, and store results in S3.
Why this is correct
IoT Core is designed for IoT; Kinesis Data Analytics provides real-time SQL processing; S3 is cost-effective storage.
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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Same concept, more angles
1 more way this is tested on SAP-C02
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. A company is designing a new application that will process real-time streaming data from thousands of IoT devices. The data must be ingested, processed with low latency, and stored in Amazon S3 for analytics. Which combination of AWS services should the company use to meet these requirements?
medium- A.Amazon SQS, AWS Lambda, Amazon S3
- B.Amazon Kinesis Data Firehose, Amazon Redshift, Amazon S3
- C.Amazon MQ, AWS Lambda, Amazon RDS
- ✓ D.Amazon Kinesis Data Streams, AWS Lambda, Amazon S3
Why D: Amazon Kinesis Data Streams ingests real-time streaming data from thousands of IoT devices with low latency, and AWS Lambda can process each record as it arrives via event source mapping. The processed data is then stored in Amazon S3 for analytics, meeting all requirements for ingestion, low-latency processing, and durable storage.
Last reviewed: Jun 20, 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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