SAA-C03 Design High-Performing Architectures Practice Question
A trading platform ingests market data events at very high volume and must deliver them with the lowest possible latency to multiple independent consumer applications. Each consumer must read the full stream independently, and ordering must be preserved per instrument symbol. Which solution meets these requirements?
⚠ Common exam trap
The trap here is assuming that any fan-out mechanism preserves ordering, when standard SQS queues and many pub/sub designs offer no per-key ordering guarantee.
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
✓
Create an Amazon Kinesis Data Streams stream with a partition key of the instrument symbol, and have each consumer use the Kinesis Client Library.
Kinesis Data Streams is built for high-throughput, low-latency event ingestion where many applications read the same stream independently. Partitioning by instrument symbol guarantees that all events for a given symbol land on one shard and are read in order, and the Kinesis Client Library gives each consumer application its own checkpointed position without affecting the others.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Create an Amazon SQS FIFO queue with a message group ID of the instrument symbol, and have each consumer application use a separate queue.
Why it's wrong here
SQS FIFO preserves ordering only within a message group, but each consumer would need its own queue, which means duplicating every message on ingest and multiplying cost and complexity. FIFO queues also have lower throughput limits than Kinesis shards, and a queue is a point-to-point model that does not naturally support many independent readers of one stream.
- ✗
Use AWS Database Migration Service with a change data capture task to replicate events into an Amazon Aurora cluster that each consumer queries.
Why it's wrong here
DMS change data capture is designed for replicating database changes, not for streaming market data events at very high volume with low latency. Consumers would poll a relational database, adding query load and latency, and ordering per symbol would depend on application logic rather than the transport. This is far more complex and slower than a purpose-built streaming service.
- ✗
Create an Amazon SNS topic and subscribe each consumer application with an Amazon SQS standard queue endpoint.
Why it's wrong here
SNS fan-out delivers each message to every subscriber, which does satisfy independent consumption, but standard SQS queues make no ordering guarantee and may deliver duplicates. There is no partition key concept to keep all events for one instrument in order, so the ordering requirement per symbol cannot be met with this design.
- ✓
Create an Amazon Kinesis Data Streams stream with a partition key of the instrument symbol, and have each consumer use the Kinesis Client Library.
Why this is correct
Kinesis Data Streams retains records for up to 365 days and allows many consumers to read the same data independently, each tracking its own position. Using the instrument symbol as the partition key routes all events for a symbol to the same shard, which preserves per-symbol ordering. The Kinesis Client Library handles shard discovery, checkpointing, and load balancing across consumer instances.
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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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