AWS Certified Solutions Architect Professional SAP-C02 Practice Question
A financial services company runs a critical data analytics application on a fleet of EC2 instances within an Auto Scaling group. The workload exhibits a predictable pattern, with high demand during business hours (9 AM to 5 PM, Monday-Friday) and low demand overnight and on weekends. However, the application also experiences brief, unpredictable, and intense spikes in processing demand that can occur at any time. The current configuration uses a simple scaling policy that adds instances when the average CPU utilization exceeds 70%, but this has resulted in significant over-provisioning during off-peak hours and slow reaction times to the sudden spikes, impacting performance and cost. The company wants to implement a new scaling strategy to minimize costs while ensuring the application can handle both predictable and unpredictable load changes effectively.
Which scaling strategy should a solutions architect recommend to meet these requirements in the most cost-effective and performant way?
Replace the simple scaling policy with a single target tracking scaling policy based on the ASGAverageCPUUtilization metric. Set a low target value (e.g., 50%) to maintain a permanent buffer of spare capacity to absorb all spikes.
Implement a combination of scaling policies. Use scheduled scaling to set the minimum and desired capacity to handle the baseline business-hour load. Concurrently, use a step scaling policy based on CPU utilization to rapidly add capacity in tiered increments in response to unpredictable spikes.
Implement a predictive scaling policy in 'forecast and scale' mode. Use the application's historical CloudWatch metrics to forecast the daily patterns and automatically provision capacity in advance of both the regular business-hour load and the unpredictable spikes.
Create a custom solution using an Amazon CloudWatch alarm that triggers an AWS Lambda function. The function will call the UpdateAutoScalingGroup API to manually adjust the desired capacity based on custom logic when CPU utilization is high.
The correct answer is to implement a combination of scheduled scaling and step scaling policies. Scheduled scaling is the most cost-effective way to handle predictable traffic patterns, such as scaling up for business hours and scaling down during nights and weekends. This directly addresses the over-provisioning issue during off-peak times. For the brief, intense, and unpredictable spikes, a step scaling policy provides more granular and aggressive control than a target tracking policy. It allows for configuring different scaling responses based on the magnitude of the alarm breach, ensuring that a sudden, intense spike triggers a correspondingly large and rapid increase in capacity, which is crucial for performance.
Using only a target tracking policy is a plausible but less optimal solution. While it adapts to load changes, it may not react aggressively enough to the described 'intense spikes' compared to a well-configured step policy, and it is less efficient at managing the known business-hour schedule than proactive scheduled scaling.
Using predictive scaling is incorrect because it relies on forecasting from historical data for cyclical patterns. It is not designed to handle sudden, unpredictable spikes in demand.
Implementing a custom solution with a Lambda function to modify the Auto Scaling group adds unnecessary operational overhead and complexity. AWS Auto Scaling provides native policies that can achieve the desired outcome more simply and robustly.
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AWS Certified Solutions Architect Professional SAP-C02
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