Capacity Planning & Scaling Flashcards
7 cards from real SRE practice questions. Tap to flip, then mark Knew It or Still Learning — missed cards come back until you master them.
Read the first 7 Capacity Planning & Scaling flashcards as text
Which metric is most useful for predicting when a database will run out of disk space?
Answer: Linear regression on historical growth rate
Linear regression on historical growth trends provides a time-based forecast, allowing teams to predict when capacity will be exhausted.
An SRE observes that CPU utilization spikes to 95% every Monday morning for 30 minutes. What is the best scaling strategy?
Answer: Configure scheduled auto-scaling to add capacity before Monday morning
Scheduled auto-scaling proactively adds resources before a known predictable load pattern, avoiding over-provisioning at other times.
What does 'headroom' refer to in capacity planning?
Answer: Reserved capacity above current usage to absorb unexpected demand
Headroom is the buffer of spare capacity maintained above current utilization to handle unexpected traffic bursts without degrading service.
A service's p99 latency degrades significantly when CPU utilization exceeds 70%. What should the SRE set as the scaling threshold?
Answer: 70% or below
Scaling should trigger at or before the 70% threshold to prevent latency degradation, ensuring performance SLOs are maintained.
Which load testing approach best simulates realistic production traffic for capacity planning?
Answer: Replay of recorded production traffic traces
Replaying recorded production traffic captures real-world request distributions, user patterns, and payload sizes for accurate capacity modeling.
What is the primary risk of setting auto-scaling cooldown periods that are too short?
Answer: Thrashing, where the system repeatedly scales up and down
Short cooldown periods cause scaling thrashing, where instances are repeatedly added and removed in rapid succession, destabilizing the system.
In the context of capacity planning, what is 'demand forecasting'?
Answer: Predicting future resource needs based on business and traffic growth trends
Demand forecasting uses historical trends, business growth projections, and seasonality to predict future infrastructure resource requirements.