CompTIA Cloud+ (CV0-004)OperationsMedium
A cloud administrator is configuring an auto-scaling group for a stateless web application. The application experiences predictable traffic spikes every weekday morning between 08:00 and 09:00 UTC and requires 10 instances during this period, but only 2 instances during off-peak hours. Which scaling configuration should be implemented to efficiently manage costs and performance?
- AStep scaling policy based on network ingress.
- BPredictive scaling policy integrated with machine learning.
- CScheduled scaling policy with minimum and desired capacities.
- DTarget tracking scaling policy based on CPU utilization.
Show answer & explanationAnswer & explanation
Correct answer: C. Scheduled scaling policy with minimum and desired capacities.
For predictable traffic spikes at specific times, a scheduled scaling policy is the most efficient. It allows the administrator to define exact scaling actions (e.g., increase to 10 instances at 07:55 UTC and decrease to 2 instances at 09:05 UTC) without relying on real-time metrics, thus preventing delays in scaling up and ensuring cost savings during off-peak hours.
Why the other options are wrong
- A. Step scaling also reacts to current load, and while it allows for more granular adjustments, it's not as efficient for predictable, time-based events.
- B. Predictive scaling uses historical data and machine learning, which is powerful but often more complex and potentially overkill for a simple, predictable daily pattern.
- D. Target tracking reacts to current load, which might be too slow for predictable spikes or lead to over-provisioning if not precisely tuned.
Scheduled Scaling
An auto-scaling policy that adjusts the number of instances based on a predefined schedule, ideal for predictable traffic patterns.
- Proactive scaling based on time.
- Ensures resources are available before peak load.
- Cost-effective by scaling down during off-peak times.
Memory trick: Schedule for Predictable Peaks, Save Cents on Off-Peaks.