AWS Certified Solutions Architect – Associate (SAA-C02) — Question 702

A transaction processing company has weekly scripted batch jobs that run on Amazon EC2 instances. The EC2 instances are in an Auto Scaling group. The number of transactions can vary, but the baseline CPU utilization that is noted on each run is at least 60%. The company needs to provision the capacity 30 minutes before the jobs run.
Currently, engineers complete this task by manually modifying the Auto Scaling group parameters. The company does not have the resources to analyze the required capacity trends for the Auto Scaling group counts. The company needs an automated way to modify the Auto Scaling group's desired capacity.
Which solution will meet these requirements with the LEAST operational overhead?

Answer options

• A. Create a dynamic scaling policy for the Auto Scaling group. Configure the policy to scale based on the CPU utilization metric. Set the target value for the metric to 60%.
• B. Create a scheduled scaling policy for the Auto Scaling group. Set the appropriate desired capacity, minimum capacity, and maximum capacity. Set the recurrence to weekly. Set the start time to 30 minutes before the batch jobs run.
• C. Create a predictive scaling policy for the Auto Scaling group. Configure the policy to scale based on forecast. Set the scaling metric to CPU utilization. Set the target value for the metric to 60%. In the policy, set the instances to pre-launch 30 minutes before the jobs run.
• D. Create an Amazon EventBridge (Amazon CloudWatch Events) event to invoke an AWS Lambda function when the CPU utilization metric value for the Auto Scaling group reaches 60%. Configure the Lambda function to increase the Auto Scaling group's desired capacity and maximum capacity by 20%.

Correct answer: C

Explanation

Predictive scaling uses machine learning to analyze historical load patterns and automatically forecast future capacity needs, which eliminates the need for manual trend analysis while allowing instances to be pre-launched 30 minutes before the batch jobs. Scheduled scaling is incorrect because it requires manual analysis to determine and hardcode the exact capacity numbers, which the company does not have the resources to do. Dynamic scaling and EventBridge-triggered Lambda functions are reactive solutions that cannot scale up resources proactively 30 minutes before the load begins.