SAP-C02 Design for New Solutions Practice Question
A global e-commerce company is designing a new recommendation engine on AWS. The engine processes user behavior data (clicks, purchases) from multiple sources in real time and updates recommendations stored in Amazon DynamoDB. The data stream can reach 100,000 events per second. The solution must be highly available and process events with minimal latency (< 1 second). The company wants to use a managed streaming service and a real-time processing framework. Which solution should a Solutions Architect recommend?
⚠ Common exam trap
Watch out — candidates often confuse Kinesis Data Firehose's near-real-time delivery (60-second buffer) with true real-time streaming, or assume Lambda can handle high-throughput streaming workloads without considering concurrency and latency limitations.
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 Amazon Kinesis Data Streams to ingest events, process them in real time with Amazon Kinesis Data Analytics using SQL, and write results to DynamoDB.
Amazon Kinesis Data Streams can ingest up to 100,000 events per second with shard-level scaling, and Kinesis Data Analytics (SQL) provides sub-second processing latency for real-time transformations. Writing directly to DynamoDB from the analytics application meets the <1 second latency requirement while maintaining high availability through Kinesis's built-in replication across three Availability Zones.
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 events, process them in real time with Amazon Kinesis Data Analytics using SQL, and write results to DynamoDB.
Why this is correct
Kinesis Data Analytics provides low-latency real-time processing; integrates well with Kinesis Streams and DynamoDB.
- ✗
Use Amazon Kinesis Data Firehose to ingest events, transform data with Lambda, and deliver to DynamoDB.
Why it's wrong here
Firehose delivers data in batches with some latency (60 seconds minimum), not real-time processing.
- ✗
Use Amazon Kinesis Data Streams to ingest events, use AWS Lambda to process each event, and write results to DynamoDB.
Why it's wrong here
Lambda may struggle with 100,000 events per second and strict latency requirements; concurrency limits and cold starts could be issues.
- ✗
Use Amazon MSK (Managed Streaming for Apache Kafka) to ingest events, process them with Apache Spark Streaming on Amazon EMR, and write to DynamoDB.
Why it's wrong here
EMR adds operational overhead; Spark Streaming may not achieve sub-second latency.
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