SAA-C03 Design High-Performing Architectures Practice Question
A solutions architect is designing a high-performance architecture for a real-time analytics application. The application ingests a continuous stream of data from thousands of IoT devices. The data must be processed in near real-time, and the results must be stored in a durable, scalable data store for later analysis. The architect needs to choose AWS services that can handle the ingestion and processing of the stream. (Choose two.)
⚠ Common exam trap
The trap here is assuming that any queue or ETL service can handle real-time streaming; SQS FIFO is for message decoupling with ordering, and Glue is for batch ETL, not continuous stream processing.
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 the data.
Amazon Kinesis Data Streams provides a scalable, durable ingestion layer for real-time streaming data, capable of handling thousands of IoT devices. AWS Lambda can be integrated as a consumer to process records in near real-time, automatically scaling with the stream. Together, they form a serverless pipeline that ingests and processes data with low latency, and can write results to a durable store like S3 or DynamoDB.
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 the data.
Why this is correct
Amazon Kinesis Data Streams is designed for real-time streaming data ingestion at scale. It can handle thousands of data sources and provides durable, ordered storage of records for up to 7 days. It integrates with AWS Lambda, Kinesis Data Analytics, and other services for processing. This makes it ideal for ingesting the continuous stream from IoT devices, ensuring high throughput and low latency for the analytics application.
- ✗
Use AWS Glue to process the stream in real time.
Why it's wrong here
AWS Glue is a serverless ETL service primarily used for batch processing and data cataloging. It can process data in micro-batches, but it is not designed for near real-time streaming with sub-second latency. AWS Glue jobs typically run on a schedule or trigger, and the minimum batch interval may not meet real-time requirements. For real-time stream processing, AWS Lambda or Kinesis Data Analytics is more appropriate.
- ✗
Use Amazon Redshift to ingest the data directly from the devices.
Why it's wrong here
Amazon Redshift is a data warehouse optimized for analytical queries on large datasets, not for ingesting high-velocity streaming data from thousands of devices. It does not natively support streaming ingestion; you typically load data in batches from S3 or via Kinesis Data Firehose. Using Redshift for direct ingestion would require custom code and may not handle the throughput or provide real-time processing capabilities.
- ✗
Use Amazon SQS FIFO queues to ingest the data.
Why it's wrong here
Amazon SQS FIFO queues provide exactly-once processing and preserve order, but they are not designed for high-throughput streaming ingestion from thousands of devices. SQS has a limit on the number of in-flight messages and may not scale as seamlessly for continuous IoT streams. While SQS can be used for decoupling, Kinesis Data Streams is more appropriate for real-time analytics with multiple consumers and ordered processing at scale.
- ✓
Use AWS Lambda to process the stream in real time.
Why this is correct
AWS Lambda can be configured as a consumer of Kinesis Data Streams, processing records in near real-time. It automatically scales with the number of shards and handles the infrastructure. Lambda functions can transform, enrich, and analyze the data as it arrives, and then write results to a durable store like Amazon S3 or DynamoDB. This serverless approach reduces operational overhead while providing the needed 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 |
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Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Amazon Web Services exam blueprint
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