Which factors influence cloud capacity planning and autoscaling thresholds?

• A. Forecast demand, performance metrics, cost constraints, latency targets
• B. Only forecast demand
• C. Only cost constraints
• D. Only latency targets

Answer: (A)

Balancing capacity planning and autoscaling thresholds requires considering what you expect to happen, how the system behaves, and the business limits you must respect. Forecasting demand helps you anticipate the workload and size the baseline so you’re not caught off guard by spikes. Real-time performance metrics—like CPU, memory, I/O, queue depth, and especially latency and error rates—show whether current resources are keeping up and indicate when a scale action is needed. Cost constraints keep scaling within budget, preventing runaway expenses and guiding how aggressively you scale or how high you set limits. Latency targets ensure user experience stays acceptable, so scaling is triggered before response times breach service level expectations. Put together, these signals form robust autoscaling thresholds and capacity plans that respond to demand while maintaining performance and cost discipline. Focusing on any single factor alone can lead to under- or over-provisioning, or missed latency targets, which is why all four are considered.