SOA-C02 Cost and Performance Optimization Practice Question
A company runs a web application on EC2 instances behind an Application Load Balancer. The application experiences variable traffic patterns. What is the MOST cost-effective way to ensure the application scales based on demand?
⚠ Common exam trap
Many exam-takers choose scheduled scaling (Option B) thinking it covers all variable traffic, but the exam tests the distinction that scheduled scaling only works for predictable patterns, not truly variable demand, making target tracking the correct choice for cost-effective dynamic 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 EC2 Auto Scaling with a target tracking scaling policy based on average CPU utilization.
A target tracking scaling policy based on average CPU utilization automatically adjusts the number of EC2 instances to maintain a target metric (e.g., 50% CPU), scaling out during high demand and scaling in during low demand. This is the most cost-effective approach for variable traffic patterns as it eliminates over-provisioning and manual intervention, directly aligning capacity with real-time demand.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Provision a fixed number of EC2 instances that can handle peak load at all times.
Why it's wrong here
Provisioning a fixed fleet sized for peak demand means those EC2 instances sit idle whenever traffic drops, incurring continuous compute costs without delivering value. It also fails to accommodate growth beyond the initial estimate, risking performance degradation. This approach ignores the elasticity that defines AWS, forcing you to pay for maximum capacity while most of the time only a fraction is needed.
- ✗
Use EC2 Auto Scaling with a scheduled scaling policy that adds instances during business hours.
Why it's wrong here
Scheduled scaling only adjusts capacity at the times you preconfigure, so a sudden spike during lunchtime or an unexpected launch event outside business hours will go unanswered. It also keeps instances running during off-peak business hours even if CPU utilization is negligible, wasting money. The policy cannot react to real-time CloudWatch metrics, making it unsuitable for variable or unpredictable workloads.
- ✓
Use EC2 Auto Scaling with a target tracking scaling policy based on average CPU utilization.
Why this is correct
A target tracking scaling policy works by setting a target value for a metric—such as average CPU utilization at, say, 60%—and Auto Scaling continuously reads CloudWatch alarms to add or remove instances, keeping the metric near that target. This approach is reactive and automatic, handling sudden traffic surges by launching instances and terminating idle ones when demand falls. It is the most cost-effective and hands-off option for variable web workloads.
- ✗
Use EC2 Auto Scaling with a manual scaling plan that requires an administrator to adjust the desired capacity.
Why it's wrong here
Manual scaling relies on an administrator to monitor CloudWatch metrics and then manually change the Auto Scaling group's desired capacity through the console or CLI. The inherent latency of human reaction time means the fleet is either over-provisioned because the admin is cautious, or under-provisioned because the admin hasn't noticed the spike yet. It also requires 24/7 operational staff to be effective, which is rarely practical.
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Same concept, more angles
2 more ways this is tested on SOA-C02
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. A company runs a web application on EC2 instances behind an Application Load Balancer. The application experiences variable traffic patterns. The operations team notices that during low traffic periods, there are still a large number of running instances, leading to higher costs. What should the team do to reduce costs while maintaining performance?
medium- A.Replace the existing instances with larger instance types to handle peak load.
- ✓ B.Implement a target tracking scaling policy based on average CPU utilization.
- C.Manually scale down the number of instances during off-peak hours.
- D.Purchase Reserved Instances for the baseline capacity.
Why B: The problem is over-provisioning during low-traffic periods, so the solution must automatically reduce capacity when demand drops while preserving the ability to scale up. A target tracking scaling policy based on average CPU utilization continuously adjusts the desired instance count to maintain the target, scaling in during off-peak and out during peaks. This is the standard, hands-off cost-optimization approach for variable workloads.
Variation 2. A company runs a web application on EC2 instances behind an Application Load Balancer (ALB). The application experiences variable traffic with occasional spikes. The SysOps administrator wants to optimize costs while ensuring that the application can handle spikes without performance degradation. The current setup uses a fixed number of instances. Which action should the administrator take?
medium- A.Purchase Reserved Instances for the current number of instances to reduce hourly cost.
- ✓ B.Implement an Auto Scaling group with a target tracking scaling policy based on ALB request count per target.
- C.Replace on-demand instances with Spot Instances for all traffic.
- D.Increase the instance size to a compute-optimized type to handle spikes.
Why B: An Auto Scaling group with a target tracking policy based on ALB request count per target automatically scales instances in and out to match traffic, handling spikes while minimizing cost during low-traffic periods. This directly addresses variable traffic with occasional spikes.
JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This SOA-C02 practice question is part of Courseiva's free Amazon Web Services certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the SOA-C02 exam.