MLA-C01 Practice Question: Refer to the exhibit

Exhibit

{
    "PolicyARN": "arn:aws:autoscaling:us-east-1:123456789012:scalingPolicy:policy-1",
    "PolicyName": "SageMakerEndpointScalingPolicy",
    "PolicyType": "TargetTrackingScaling",
    "TargetTrackingScalingPolicyConfiguration": {
        "TargetValue": 70.0,
        "PredefinedMetricSpecification": {
            "PredefinedMetricType": "SageMakerVariantInvocationsPerInstance"
        },
        "ScaleInCooldown": 600,
        "ScaleOutCooldown": 200
    }
}

Refer to the exhibit. A team observes that their SageMaker endpoint scales out quickly when load increases, but scales in very slowly when load decreases, causing over-provisioning. What is the most likely cause?

⚠ Common exam trap

A common mix-up: candidates confuse cooldown periods with scaling thresholds, assuming that slow scale-in is caused by a high TargetValue or wrong metric, rather than recognizing that cooldown timers directly control the delay between scaling actions.

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

✓

ScaleInCooldown is too high

A high ScaleInCooldown value causes the SageMaker endpoint to wait too long before initiating a scale-in event after load decreases. This delay prevents the endpoint from releasing resources promptly, leading to over-provisioning. In contrast, the scaling out behavior is unaffected by this cooldown, which explains why the endpoint scales out quickly but scales in slowly.

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

✗
TargetValue is too high
Why it's wrong here
A high target value may cause under-provisioning, not slow scale-in.
✗
ScaleOutCooldown is too low
Why it's wrong here
A low ScaleOutCooldown makes scale-out faster, not slower scale-in.
✓
ScaleInCooldown is too high
Why this is correct
A high ScaleInCooldown delays scale-in responses.
✗
Wrong predefined metric selected
Why it's wrong here
SageMakerVariantInvocationsPerInstance is a common metric for scaling.

Go deeper

MLA-C01 Practice Question: Refer to the exhibit

Exhibit

Why each option matters

Option-by-option breakdown

Related to this question