MLS-C01 Practice Question: Machine Learning Implementation and Operations
An ML engineer is deploying a model to a SageMaker endpoint for real-time inference. The model requires a custom inference script that preprocesses input data and postprocesses predictions. Which SageMaker feature should be used to implement this custom logic?
⚠ Common exam trap
Watch out — candidates often confuse SageMaker Processing jobs (batch) with real-time inference preprocessing, or assume built-in algorithms can be customized via inference scripts, when in fact only custom containers or scripts provide that flexibility.
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 model with a custom inference script that includes pre- and post-processing functions
SageMaker allows you to bring your own container or use a pre-built container with a custom inference script that defines `input_fn`, `predict_fn`, `output_fn`, and `model_fn` functions. These functions handle preprocessing of input data, model prediction, and postprocessing of predictions, enabling custom logic for real-time inference endpoints without requiring separate infrastructure.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use SageMaker Ground Truth to transform inference requests
Why it's wrong here
Ground Truth is for data labeling, not inference.
- ✗
Use SageMaker Processing jobs to preprocess data before inference
Why it's wrong here
Processing jobs are for batch preprocessing, not real-time.
- ✗
Use a built-in SageMaker algorithm with the default inference code
Why it's wrong here
Built-in algorithms do not support custom preprocessing/postprocessing.
- ✓
Create a SageMaker model with a custom inference script that includes pre- and post-processing functions
Why this is correct
Custom inference scripts allow full control over request handling.
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