DVA-C02 Auto Scaling Cooldown Period Practice Question
A team uses AWS Elastic Beanstalk to deploy a web application. The application experiences intermittent high latency. The team notices that the environment's Auto Scaling group is not scaling out quickly enough. Which configuration change should the team make to improve scaling responsiveness?
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
✓
Decrease the Auto Scaling group's cooldown period
Decreasing the Auto Scaling group's cooldown period reduces the time that the group waits after a scaling activity before it can launch another instance, thereby improving scaling responsiveness. Option A is incorrect because a lighter health check path does not affect scaling speed—it only affects how quickly unhealthy instances are detected. Option B is incorrect; while detailed CloudWatch metrics provide more granular data, they do not directly reduce the cooldown period. Option C is incorrect because increasing instance size improves per-instance capacity but does not change how quickly the group scales out.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Modify the Elastic Load Balancer health check path to a lighter endpoint
Why it's wrong here
Modifying the Elastic Load Balancer (ELB) health check path to a lighter endpoint primarily affects how quickly the ELB determines the health status of individual instances within its target group. While a faster health check might remove unhealthy instances from rotation more rapidly or add newly launched instances to traffic distribution sooner, it does not directly influence the Auto Scaling group's decision-making process or the speed at which it launches new instances in response to a scaling policy breach. The scaling trigger and instance provisioning time remain unchanged.
- ✗
Enable detailed CloudWatch metrics for the Auto Scaling group
Why it's wrong here
Enabling detailed CloudWatch metrics for an Auto Scaling group provides more granular data points, typically at 1-minute intervals instead of the default 5-minute intervals. While this enhanced granularity offers improved visibility into performance and resource utilization, allowing for more precise monitoring and analysis, it does not inherently accelerate the Auto Scaling group's reaction time to a scaling event. The frequency at which scaling policies are evaluated and acted upon is independent of the metric collection interval.
- ✗
Increase the instance type to a larger size
Why it's wrong here
Increasing the instance type to a larger size provides more compute resources (CPU, memory, network) per individual instance. This action enhances the capacity of each instance, potentially allowing it to handle more load and reducing the overall number of instances required to meet demand. However, it does not decrease the actual time it takes for the Auto Scaling group to provision, launch, and initialize a *new* instance when a scaling event is triggered, which is a key factor in scaling speed.
- ✓
Decrease the Auto Scaling group's cooldown period
Why this is correct
Decreasing the Auto Scaling group's cooldown period directly impacts how quickly the group can initiate subsequent scaling activities after a previous one has completed. The cooldown period is a configurable setting designed to prevent rapid, oscillating scaling actions by pausing further scaling for a specified duration. A shorter cooldown allows the Auto Scaling group to respond to persistent or rapidly changing load conditions more promptly, enabling faster scale-out or scale-in operations.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This DVA-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 DVA-C02 exam.