MLS-C01 Modeling Practice Question
A machine learning engineer is deploying a model to an Amazon SageMaker endpoint for real-time inference. The model requires a preprocessing step that involves tokenizing text and converting it to a numerical format. To minimize latency, where should the preprocessing logic be implemented?
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
✓
Inside the SageMaker inference container using the inference.py script
To minimize latency, it's best to include the preprocessing logic inside the inference container that serves the model. This avoids additional network calls to separate preprocessing 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.
- ✓
Inside the SageMaker inference container using the inference.py script
Why this is correct
Including preprocessing in the container reduces latency by processing data locally.
- ✗
Using Amazon SageMaker batch transform
Why it's wrong here
Batch transform is for asynchronous processing, not real-time inference.
- ✗
As a separate AWS Lambda function called before the endpoint
Why it's wrong here
Adding a Lambda function introduces extra network latency.
- ✗
On the client side before sending the request
Why it's wrong here
Client-side preprocessing may not be possible if the client is not trusted or cannot run the preprocessing code.
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