hardMultiple ChoiceObjective-mapped
MLA-C01 Practice Question: A company deploys a model using SageMaker…
A company deploys a model using SageMaker real-time endpoint with auto scaling. They observe that during a traffic spike, the endpoint quickly scales up to 10 instances, but after the spike, it takes a long time to scale down, leading to high costs. The scaling policy is based on a simple average CPU utilization threshold. Which adjustment would optimize the scaling down behavior?
⚠ Common exam trap
Candidates often confuse cooldown periods with step adjustments, thinking that larger scale-in steps will speed up the process, when in fact the cooldown period controls the timing of when scaling actions can occur.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Decrease the scale-in cooldown period to allow the endpoint to scale down faster when utilization drops.
Decreasing the scale-in cooldown period allows the endpoint to respond more quickly to sustained drops in CPU utilization. By default, SageMaker auto scaling uses cooldown periods to prevent rapid fluctuations; a long scale-in cooldown delays the termination of instances after utilization falls, keeping costs high. Reducing this cooldown lets the endpoint scale down faster when the spike subsides, directly addressing the problem.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Increase the scale-in cooldown period to prevent premature scale-down.
Why it's wrong here
Increasing cooldown would make scaling down even slower.
- ✓
Decrease the scale-in cooldown period to allow the endpoint to scale down faster when utilization drops.
Why this is correct
Reducing cooldown enables the Auto Scaling group to remove instances sooner.
- ✗
Use a step scaling policy with a larger step adjustment for scale-in.
Why it's wrong here
Step scaling can help but the cooldown period is the main issue.
- ✗
Change the scaling policy to use memory utilization instead of CPU.
Why it's wrong here
Changing metric may not address the cooldown delay.
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