MLS-C01 Practice Question: Machine Learning Implementation and Operations
A company uses Amazon SageMaker to deploy a model for real-time inference. The model is a linear regression model that was trained using the SageMaker built-in Linear Learner algorithm. The endpoint is configured with an ml.m5.large instance. After deployment, the company notices that the endpoint returns incorrect predictions. The training data was normalized, but the inference requests send raw feature values without normalization. What should the company do to fix the issue?
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
✓
Create a SageMaker inference pipeline that includes a preprocessing step to normalize the input data before passing it to the model.
The model was trained on normalized data, so it expects normalized input at inference time. Raw feature values will produce incorrect predictions because the model's coefficients are based on normalized data. The correct solution is to create a SageMaker inference pipeline that includes a preprocessing step (e.g., using a scikit-learn container) to normalize the input data before passing it to the model container. Option A (retrain with raw data) would require retraining and might degrade performance if normalization was necessary for convergence. Option B (changing instance type) does not address the data mismatch. Option D (batch transform job) is for batch inference, not real-time inference via an endpoint.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Retrain the model using raw data without normalization.
Why it's wrong here
Wrong: While possible, this is not the best practice; it may reduce accuracy and requires retraining.
- ✗
Change the endpoint instance type to a GPU instance to handle the raw data.
Why it's wrong here
Wrong: Instance type does not affect the need for normalization.
- ✓
Create a SageMaker inference pipeline that includes a preprocessing step to normalize the input data before passing it to the model.
Why this is correct
Correct: This ensures real-time raw data is normalized before inference.
- ✗
Use a batch transform job to preprocess the data before sending it to the endpoint.
Why it's wrong here
Wrong: Batch transform is not real-time; it adds latency and complexity.
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