SAA-C03 Design High-Performing Architectures Practice Question
A web application uses an Amazon Aurora DB cluster for a read-heavy workload. The application team needs higher read throughput but cannot change the database schema. They want to avoid blocking writes and are willing to route read traffic separately. What is the most appropriate architecture change?
⚠ Common exam trap
It's easy for candidates to confuse Multi-AZ deployment (which provides failover only) with read replica scaling, or mistakenly believe that scaling storage or using a single instance can improve read throughput without schema changes.
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 Aurora read replicas and route SELECT queries to an Aurora reader endpoint.
Creating Aurora read replicas and routing SELECT queries to the Aurora reader endpoint is the most appropriate architecture change because Aurora's reader endpoint distributes read traffic across up to 15 low-latency read replicas, providing higher aggregate read throughput without blocking writes. This approach requires no schema changes and allows the application to separate read and write traffic, directly addressing the read-heavy workload requirement.
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 Aurora read replicas and route SELECT queries to an Aurora reader endpoint.
Why this is correct
Aurora Replicas are independent compute nodes attached to the same distributed storage volume as the writer, so they can serve SELECT traffic without competing for the writer's CPU or memory. The Aurora reader endpoint automatically load-balances connections across all healthy replicas, enabling the cluster to scale read throughput far beyond a single writer instance. Routing only SELECT queries to the reader endpoint preserves write consistency because all write operations continue through the writer endpoint, and replica lag is typically low enough for most use cases.
- ✗
Scale up the writer instance storage only; read capacity will automatically increase without using a reader endpoint.
Why it's wrong here
Increasing the writer instance's storage allocation does not add vCPUs or memory dedicated to processing read queries; in Aurora, storage is decoupled from compute, and the writer instance alone still handles every SELECT if no replica exists. Without a reader endpoint, all read requests contend with write traffic for the same instance's resources, so read capacity remains capped by that single node's limits. To actually expand read throughput, you must provision additional Aurora Replicas and point queries at the reader endpoint—storage scaling alone has no direct effect on read compute capacity.
- ✗
Move the Aurora cluster to Multi-AZ deployment mode only; read scaling is handled automatically without replicas.
Why it's wrong here
Aurora's storage layer is inherently replicated across three Availability Zones, so the term 'Multi-AZ' does not by itself confer additional read-serving compute capacity. The primary benefit of Multi-AZ (or Aurora's default storage resilience) is high availability and automatic failover, not elastic scaling of SELECT workload. Without creating Aurora Replicas, read requests still all land on the same writer instance, and the reader endpoint exists only after replicas are provisioned — so this approach leaves read performance unchanged.
- ✗
Replace the cluster with a single RDS instance because it offers consistent performance for both reads and writes.
Why it's wrong here
Replacing the Aurora cluster with a single RDS instance eliminates the ability to scale read traffic independently; every query, read or write, would hit the same database engine and force vertical scaling of one node. RDS Multi-AZ standbys in standard RDS are not usable for read traffic, so this change would actually reduce read throughput and remove Aurora's reader endpoint capabilities. The requirement demands scaling reads separately, which a single instance fundamentally cannot provide, making this option worse than maintaining an Aurora cluster with replicas.
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