SAP-C02 Design for New Solutions Practice Question
A company is migrating a monolithic application to microservices on Amazon ECS with Fargate. The application has variable traffic patterns, with high traffic during business hours and low traffic at night. They want to optimize costs while maintaining performance. Which scaling strategy should they implement?
⚠ Common exam trap
Test-takers frequently choose scheduled scaling alone (Option C) thinking it directly handles variable traffic, but they miss that it cannot react to unexpected spikes or lulls within the scheduled window, whereas target tracking with a schedule provides both proactive and reactive scaling.
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 target tracking scaling with a schedule to increase minimum capacity during business hours.
Combining target tracking scaling with a scheduled action allows the application to dynamically adjust capacity based on actual demand while ensuring a higher baseline during peak business hours. This hybrid approach optimizes costs by scaling down at night and maintains performance by preventing cold starts or lag during traffic spikes, which is ideal for variable patterns on ECS Fargate.
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 target tracking scaling with a schedule to increase minimum capacity during business hours.
Why this is correct
This combination handles both patterns.
- ✗
Use step scaling policies based on memory utilization.
Why it's wrong here
Step scaling policies based on memory utilisation fail here because the variable traffic pattern requires a scaling mechanism that responds to request throughput, not memory pressure; memory may remain stable under high traffic if the application is CPU-bound, causing under-provisioning. It is tempting because step scaling is a common AWS cost-optimisation tool for steady-state workloads where memory is the bottleneck, and would be correct for a memory-intensive application with predictable utilisation thresholds.
- ✗
Use scheduled scaling to increase capacity during business hours.
Why it's wrong here
Scheduled scaling cannot handle unexpected spikes.
- ✗
Use simple scaling policies based on CPU utilization.
Why it's wrong here
Simple scaling is less responsive.
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