MLS-C01 Modeling Practice Question
A data scientist is using Amazon SageMaker to deploy a model for real-time inference. The model is a TensorFlow neural network. The scientist wants to use automatic scaling based on the number of incoming requests. Which service integration is required?
⚠ Common exam trap
It's easy for candidates to confuse SageMaker's built-in auto scaling with external services like ECS or Lambda, not realizing that SageMaker endpoints directly integrate with Application Auto Scaling for request-based scaling.
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
✓
Amazon SageMaker endpoint configured with Application Auto Scaling
Amazon SageMaker endpoints natively integrate with Application Auto Scaling to adjust the number of instances based on a target metric, such as the number of incoming requests per instance. This allows the TensorFlow model to scale automatically in response to traffic, without needing additional orchestration services.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Amazon ECS with service auto scaling
Why it's wrong here
ECS is not the standard way to deploy SageMaker models.
- ✓
Amazon SageMaker endpoint configured with Application Auto Scaling
Why this is correct
SageMaker integrates with Application Auto Scaling to scale endpoints based on demand.
- ✗
AWS Lambda with provisioned concurrency
Why it's wrong here
Lambda is not used to host SageMaker models directly.
- ✗
AWS Auto Scaling plans
Why it's wrong here
Auto Scaling plans are used for multiple resources but not specifically for SageMaker endpoints.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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