SAP-C02 Design for New Solutions Practice Question
A company is designing a data processing pipeline for real-time analytics. The pipeline ingests data from IoT devices that send JSON messages via MQTT to AWS IoT Core. The messages must be processed in real-time to detect anomalies and the results must be stored in Amazon S3 for later analysis. The company currently uses a Lambda function to process each message, but as the number of devices grows, the Lambda function is being throttled due to concurrency limits. The company needs a solution that scales to handle thousands of devices per second without losing messages. The processed data must be available in S3 within 1 minute of ingestion. Which architecture 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
✓
Ingest the messages into Amazon Kinesis Data Streams with multiple shards, and use a Lambda function to process records from the stream. Increase the Lambda concurrency limit.
The correct architecture is to ingest messages into Amazon Kinesis Data Streams with multiple shards to handle high throughput. Using a Lambda function to process records from the stream allows real-time anomaly detection. To address concurrency limits, the Lambda concurrency limit can be increased by requesting a limit increase from AWS Support, and you can use reserved concurrency to ensure processing capacity. This solution scales elastically and can meet the 1-minute latency requirement. Option D is incorrect because Kinesis Data Analytics cannot read directly from S3 or from Kinesis Data Firehose; it requires a Kinesis data stream as its source. The architecture in D would not provide real-time processing as it relies on periodic S3 queries.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Send the IoT messages to an Amazon SQS queue and have Lambda poll the queue in batches to reduce the number of concurrent invocations.
Why it's wrong here
This still uses Lambda and may face concurrency issues if the batch size is not sufficient. Moreover, SQS polling adds latency and does not solve the fundamental concurrency problem as Lambda still invokes per batch.
- ✗
Store the raw messages in an S3 bucket and use S3 Select to query the data for anomalies periodically.
Why it's wrong here
S3 Select is for querying objects at rest, not for real-time streaming. Periodic queries cannot achieve the 1-minute latency requirement and are not suitable for real-time anomaly detection.
- ✓
Ingest the messages into Amazon Kinesis Data Streams with multiple shards, and use a Lambda function to process records from the stream. Increase the Lambda concurrency limit.
Why this is correct
This is correct. Kinesis Data Streams can scale to thousands of shards to handle high throughput. Lambda can process records in real-time, and increasing concurrency limits (by requesting a limit increase or using reserved concurrency) addresses the throttling issue. The processed data can be written to S3 via the Lambda function.
- ✗
Use AWS IoT Core rules to route messages to Amazon Kinesis Data Firehose, which writes raw data to S3. Then use Amazon Kinesis Data Analytics to read from S3 and perform real-time anomaly detection, writing results back to S3 via Firehose.
Why it's wrong here
This is incorrect because Kinesis Data Analytics cannot read from S3 in real-time; it requires a Kinesis data stream as a source. The architecture would not provide real-time processing.
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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Same concept, more angles
3 more ways 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 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?
medium- ✓ A.Amazon Kinesis Data Streams, Amazon Kinesis Data Analytics, Amazon Kinesis Data Firehose
- B.Amazon SQS, AWS Lambda, Amazon S3
- C.Amazon Kinesis Data Streams, Amazon Redshift, Amazon S3
- D.Amazon IoT Core, AWS Lambda, Amazon DynamoDB
Why A: 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.
Variation 2. 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?
hard- A.Amazon Kinesis Data Streams to ingest data, AWS Lambda to transform and aggregate, Amazon S3 for storage.
- ✓ B.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.
- C.Amazon Kinesis Data Streams to ingest data, Amazon Kinesis Data Firehose to deliver to S3, and Amazon Athena to query raw data.
- D.Amazon SQS to ingest data, AWS Lambda to process and aggregate, Amazon DynamoDB for raw data, S3 for summaries.
Why B: 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.
Variation 3. 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.)
medium- A.AWS Lambda
- ✓ B.Amazon Kinesis Data Streams
- ✓ C.Amazon Kinesis Data Analytics
- D.Amazon S3
- E.Amazon SQS
Why B: Amazon Kinesis Data Streams is correct because it is designed to ingest and store high-velocity data streams from thousands of IoT devices with low latency, providing durable, ordered data that can be consumed in real time. Amazon Kinesis Data Analytics is correct because it can process streaming data from Kinesis Data Streams using SQL or Apache Flink to perform real-time analytics without needing to store the data first, meeting the low-latency processing requirement.
JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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