MLA-C01 · Question #170
A company uses an Amazon SageMaker AI ML model to make real-time inferences. The company has configured auto scaling for the Amazon EC2 instances that SageMaker AI uses for the inferences. During…
The correct answer is D. Increase the cooldown period after scale-out activities. Increasing the cooldown period after scale-out activities gives newly launched instances enough time to fully initialize and become ready to serve inference traffic before additional scaling actions occur, which prevents premature scaling decisions and optimizes performance…
Question
A company uses an Amazon SageMaker AI ML model to make real-time inferences. The company has configured auto scaling for the Amazon EC2 instances that SageMaker AI uses for the inferences. During times of peak usage, new instances launch before existing instances are fully ready. As a result, the model experiences inefficiencies and delays. Which solution will optimize the scaling process without affecting response times?
Options
- AChange to a multi-model endpoint configuration in SageMaker AI.
- BIntegrate Amazon API Gateway and AWS Lambda to manage invocations of the SageMaker AI
- CDecrease the cooldown period for scale-in activities. Increase the maximum number of instances.
- DIncrease the cooldown period after scale-out activities.
How the community answered
(27 responses)- A4% (1)
- B4% (1)
- C11% (3)
- D81% (22)
Explanation
Increasing the cooldown period after scale-out activities gives newly launched instances enough time to fully initialize and become ready to serve inference traffic before additional scaling actions occur, which prevents premature scaling decisions and optimizes performance without impacting
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