AWS Certified Solutions Architect - Associate
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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?
Other
Community
UAnonymous
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 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%.
Explanation:
Explanation
Correct Answer: B - Scheduled Scaling Policy
Why Option B is correct:
- Weekly predictable pattern: The batch jobs run weekly, which is a predictable schedule.
- 30-minute pre-provisioning requirement: The company needs capacity provisioned 30 minutes before jobs run, which can be precisely scheduled.
- Least operational overhead: Scheduled scaling requires minimal setup and maintenance compared to other options.
- No trend analysis needed: The company doesn't have resources for trend analysis, making scheduled scaling ideal.
- Manual process automation: Directly automates what engineers were doing manually.
Why other options are incorrect:
Option A (Dynamic Scaling):
- Reacts to CPU utilization after jobs start, not 30 minutes before
- Would cause scaling during job execution rather than pre-provisioning
- Could lead to performance issues as instances scale up while jobs are running
Option C (Predictive Scaling):
- Requires historical data and trend analysis
- The company explicitly stated they don't have resources for analyzing required capacity trends
- More complex setup and maintenance than scheduled scaling
- Predictive scaling is better for variable patterns, not fixed weekly schedules
Option D (EventBridge + Lambda):
- Overly complex solution for a simple weekly schedule
- Requires custom Lambda function development and maintenance
- Triggers based on CPU utilization (reactive, not proactive)
- Higher operational overhead than native Auto Scaling features
Key AWS Concepts:
- Scheduled Scaling: Best for predictable, recurring workloads with known timing
- Dynamic Scaling: Best for variable workloads that respond to real-time metrics
- Predictive Scaling: Best for workloads with historical patterns that can be forecasted
- Event-driven scaling: Best for custom scaling logic not covered by native Auto Scaling features
Since the requirement is specifically for weekly batch jobs that need capacity 30 minutes before execution, scheduled scaling provides the simplest, most reliable solution with the least operational overhead.
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