Two Actions to Improve Auto Scaling Responsiveness
A company runs a web application on EC2 instances behind an Application Load Balancer. The application experiences a sudden spike in traffic. The operations team notices that the Auto Scaling group is not scaling out quickly enough. Which TWO actions should the team take to improve the scaling responsiveness?
Quick Answer
The answer is to decrease the cooldown period and implement a buffer-based scaling policy like target tracking. Decreasing the cooldown period allows the Auto Scaling group to launch new instances more quickly after a scaling activity completes, directly addressing the need to improve auto scaling responsiveness during sudden traffic spikes. Target tracking is more responsive than step scaling because it proactively adjusts capacity based on a predefined target metric, such as average CPU utilization, rather than reacting to discrete threshold breaches. On the AWS Certified Solutions Architect Professional SAP-C02 exam, this scenario tests your understanding of how cooldown timers and policy types affect scaling velocity, often appearing as a trap where candidates mistakenly choose increasing instance size or minimum size. A common memory tip is to remember that responsiveness is about speed, not size—think “cool down to speed up” and “track the target, don’t step through it.”
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
✓
Switch from a step scaling policy to a target tracking scaling policy based on CPU utilization.
To improve scaling responsiveness, the team should use a target tracking scaling policy based on CPU utilization (Option B) because target tracking continuously adjusts the desired capacity to meet the target value, providing faster and more precise scaling than step scaling. Decreasing the cooldown period (Option D) allows new instances to start contributing to metrics sooner, enabling the Auto Scaling group to scale out more quickly during spikes. Option A (increasing instance size) does not improve the speed of scaling out; it only increases capacity per instance but not responsiveness. Option C (increasing health check grace period) delays the detection of unhealthy instances, slowing down scaling. Option E (decreasing minimum number) reduces baseline capacity and does not help with scaling out speed.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Increase the instance size to handle more traffic per instance.
Why it's wrong here
Larger instances do not improve scaling speed; they only affect capacity per instance.
- ✓
Switch from a step scaling policy to a target tracking scaling policy based on CPU utilization.
Why this is correct
Target tracking provides more responsive scaling than step scaling.
- ✗
Increase the health check grace period for the Auto Scaling group.
Why it's wrong here
A longer grace period delays scaling decisions.
- ✓
Decrease the cooldown period for the Auto Scaling group.
Why this is correct
A shorter cooldown allows the group to scale out more quickly after a scaling activity.
- ✗
Decrease the minimum number of instances in the Auto Scaling group.
Why it's wrong here
Lower minimum means fewer instances initially, which could worsen responsiveness.
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1 more way this is tested on SAP-C02
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Variation 1. A company runs a web application on EC2 instances behind an Application Load Balancer (ALB). The application experiences periodic spikes in traffic. The operations team wants to ensure that the application can handle the spikes without manual intervention. What is the MOST cost-effective solution?
easy- A.Use a scheduled scaling policy to add instances during predicted peak hours.
- ✓ B.Create a target tracking scaling policy using the ALB RequestCountPerTarget metric.
- C.Manually add instances when traffic spikes are expected.
- D.Use a simple scaling policy based on CPU utilization.
Why B: A target tracking scaling policy automatically adjusts capacity based on a specific metric, and using the ALB RequestCountPerTarget metric is appropriate for handling traffic spikes in a cost-effective manner. This policy maintains the desired metric value by adding or removing instances as needed, without manual intervention. Option A is incorrect because a scheduled scaling policy assumes predictable peaks, but the traffic spikes are periodic and may not follow a fixed schedule. Option C is incorrect because manual intervention is not cost-effective and defeats the purpose of automation. Option D is incorrect because a simple scaling policy based on CPU utilization may not react quickly enough to sudden traffic spikes and could be less cost-effective than target tracking.
JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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