CompTIA Cloud+ (CV0-004)OperationsMedium
A cloud engineer is designing an automated scaling solution for an e-commerce website that experiences predictable spikes in traffic during holiday seasons. To optimize costs and performance, the engineer wants to ensure that new instances are provisioned in anticipation of the traffic increase, rather than reacting to it. Which scaling strategy should be employed?
- ADynamic scaling based on CPU utilization.
- BPredictive scaling using machine learning.
- CScheduled scaling based on historical data.
- DManual scaling during peak hours.
Show answer & explanationAnswer & explanation
Correct answer: C. Scheduled scaling based on historical data.
Scheduled scaling allows instances to be provisioned or de-provisioned at specific times based on historical traffic patterns, effectively anticipating predictable spikes in demand and optimizing both performance and cost.
Why the other options are wrong
- A. Dynamic scaling reacts to current metrics, which would be reactive, not anticipatory, and could lead to performance issues during sudden spikes.
- B. Predictive scaling using machine learning is more complex and typically used for less predictable patterns; for 'predictable spikes,' scheduled scaling is a more direct and cost-effective solution.
- D. Manual scaling is inefficient and prone to human error, especially for recurring, predictable events.
Scheduled Scaling
An automated scaling strategy that adjusts resource capacity at predefined times based on known, recurring demand patterns.
- Ideal for predictable workloads (e.g., daily peaks, holiday seasons).
- Proactive, anticipating demand rather than reacting to it.
- Helps optimize costs by scaling up only when needed and down afterwards.
Memory trick: Scheduled scaling saves stress for seasonal spikes.