SAA-C03 Design High-Performing Architectures Practice Question
A logistics company runs an Amazon RDS for MySQL database that supports a shipment tracking application. Read replicas are already in use, but the primary instance's CPU is saturated by a small number of long-running analytical queries that the reporting team runs directly against the primary. The company wants to offload these analytical queries and improve primary performance while keeping the application's transactional writes fast. (Choose two.)
⚠ Common exam trap
The trap here is assuming a Multi-AZ standby can serve read traffic, when it is only a failover target.
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
✓
Move the analytical workload to Amazon Redshift and load data from RDS using AWS Database Migration Service or zero-ETL integration.
The goal is workload isolation. A dedicated read replica for reporting moves analytical reads off the primary without changing the application, while a purpose-built warehouse such as Amazon Redshift removes heavy scans entirely and keeps the warehouse current through DMS or zero-ETL. Together they protect transactional write latency.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Enable Multi-AZ on the primary instance so the standby can serve the analytical queries.
Why it's wrong here
A Multi-AZ standby exists for automatic failover, not for serving read traffic. It cannot be queried while the primary is healthy, so it cannot offload analytics. Enabling Multi-AZ improves availability and durability, which are valuable, but it does nothing to reduce the primary's CPU saturation from reporting queries.
- ✗
Increase the primary instance size and add more Provisioned IOPS to the storage volume to absorb the analytical queries.
Why it's wrong here
Scaling up the primary adds capacity but keeps analytical and transactional workloads competing on the same instance. It raises cost and only postpones the problem, since long-running scans will still consume CPU and buffer pool resources. This does not isolate the workloads, which is what the scenario requires.
- ✓
Move the analytical workload to Amazon Redshift and load data from RDS using AWS Database Migration Service or zero-ETL integration.
Why this is correct
Redshift is a columnar data warehouse built for large analytical scans, so moving reporting there removes that load from the primary entirely. Loading via AWS DMS or RDS zero-ETL integration keeps the warehouse refreshed, and the primary no longer spends CPU on long analytical queries, which improves transactional write performance.
- ✗
Take frequent manual snapshots of the primary during reporting runs so the queries read from the snapshot.
Why it's wrong here
RDS snapshots are stored in Amazon S3 and are not directly queryable by clients; restoring a snapshot creates a new instance and takes time. Snapshots do not provide a live query target, so they cannot offload the running analytical queries or reduce primary CPU during the reporting window.
- ✓
Create a read replica dedicated to analytics and point the reporting queries to that replica's endpoint.
Why this is correct
A read replica receives asynchronous copies of the primary's data and can serve read-only analytical queries without consuming primary CPU. Directing reporting traffic to a dedicated replica endpoint isolates the analytical workload, freeing the primary to handle transactional writes with lower latency. This is the standard RDS pattern for separating read-heavy analytics from OLTP traffic.
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Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Amazon Web Services exam blueprint
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