AWS Certified Solutions Architect - Associate
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A solutions architect observes that a nightly batch processing job is automatically scaled up for 1 hour before the desired Amazon EC2 capacity is reached. The peak capacity is the 'same every night' and the batch jobs always start at 1 AM. The solutions architect needs to find a cost-effective solution that will allow for the desired EC2 capacity to be reached quickly and allow the Auto Scaling group to scale down after the batch jobs are complete.
What should the solutions architect do to meet these requirements?
Other
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UAnonymous
A
Increase the minimum capacity for the Auto Scaling group.
B
Increase the maximum capacity for the Auto Scaling group.
C
Configure scheduled scaling to scale up to the desired compute level.
D
Change the scaling policy to add more EC2 instances during each scaling operation.
Explanation:
Explanation
Correct Answer: C - Configure scheduled scaling to scale up to the desired compute level.
Why this is the correct solution:
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Predictable Pattern: The batch jobs run nightly at 1 AM with the same peak capacity every night. This predictable pattern makes scheduled scaling ideal.
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Cost-Effective: Scheduled scaling ensures that the desired capacity is reached exactly when needed (at 1 AM), eliminating the 1-hour warm-up period where resources are scaling up gradually. This reduces costs by minimizing the time instances are running.
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Quick Capacity Achievement: With scheduled scaling, the Auto Scaling group can instantly scale to the desired capacity at the scheduled time, rather than waiting for scaling policies to gradually add instances.
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Automatic Scale Down: After the batch jobs complete, the Auto Scaling group can automatically scale down based on metrics or another scheduled action.
Why other options are incorrect:
- A. Increase the minimum capacity: This would keep instances running 24/7, which is not cost-effective for nightly batch jobs.
- B. Increase the maximum capacity: This doesn't solve the problem of slow scaling; it only increases the upper limit.
- D. Change the scaling policy: While this might reduce the warm-up time, it's less precise and efficient than scheduled scaling for predictable workloads.
Best Practice Implementation:
The solutions architect should:
- Create a scheduled action to scale up to the desired capacity at 1 AM
- Create another scheduled action to scale down after the batch jobs complete
- Optionally combine with metric-based scaling for unexpected load variations
This approach ensures optimal cost efficiency while meeting performance requirements.
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