MLA-C02 practice question: Endpoint scaling and provisioning (1 of 2)
Deployment and Orchestration of ML and AI Workflows A free question from our original MLA-C02 practice exam. Try it, then open the answer to see why each option is right or wrong.
An esports league runs a match-prediction endpoint on a real-time SageMaker AI endpoint. Traffic jumps sharply during tournament weekends, and in the last tournament the endpoint slowed down for about 15 minutes before its instance count caught up.
The league wants the endpoint to add capacity before each weekend starts, and to keep scaling on the actual request load during the event. The scaling policy must track the request volume each instance handles.
Which TWO scaling elements should the team configure? (Choose two.)
Choose two answers.
AA. A target tracking policy on the SageMakerVariantInvocationsPerInstance metric, so that the instance count follows the number of requests each instance handles.
BB. A target tracking policy on the CPU utilization of the instances, since CPU always reflects the load of a model endpoint.
CC. A scheduled scaling action that raises the minimum instance count before each tournament weekend starts.
DD. Delete the endpoint each Friday and recreate it on Monday, so that every tournament starts with a fresh set of instances.
EE. Increase the size of each instance to the largest type available, so that no scaling is needed during the tournament.
Show the answer and explanation
Answers: A, C
A. Right: Correct. Target tracking on invocations per instance adds instances when each one handles more requests than the target. It keeps the endpoint near the chosen load during the event. Learn more: Define a scaling policy (opens in a new tab)
B. Wrong: CPU may not reflect the load of model inference, for example when the work runs on GPUs. The invocations-per-instance metric matches the request load the stem describes.
C. Right: Correct. A scheduled action raises capacity at a known time, one time or on a recurring schedule, so the endpoint is ready before traffic arrives. After it runs, the target tracking policy keeps adjusting capacity during the event. Learn more: Auto scaling policy overview (opens in a new tab)
D. Wrong: Recreating the endpoint each week takes time and risks downtime, and it does not scale on load during the event. The stem asks for scaling that follows the traffic.
E. Wrong: Oversized instances cost more all the time and still cannot match a traffic spike beyond their capacity. The stem asks for scaling by instance count.
Explanation
Target tracking on invocations per instance scales the endpoint on the request load each instance handles, and a scheduled action raises minimum capacity before a known event. CPU utilization may not reflect model load, and recreating or oversizing the endpoint does not scale on demand.
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