MLS-C01 Modeling Practice Question
A data scientist is training a classification model on a dataset with missing values in several features. The data scientist wants to use SageMaker to train the model. Which TWO approaches can the data scientist use to handle missing data within the SageMaker training pipeline? (Choose two.)
⚠ Common exam trap
Test-takers frequently assume all SageMaker built-in algorithms automatically handle missing values, but only XGBoost does; BlazingText and PCA require complete data, and Inference Pipeline is for serving, not training.
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
✓
Use the SageMaker built-in XGBoost algorithm, which can handle missing values by default.
The SageMaker built-in XGBoost algorithm has a built-in mechanism to handle missing values by default. It learns the best direction (left or right branch) to route missing values during training, so no explicit imputation is needed. This makes it a seamless choice for datasets with missing data within the SageMaker training pipeline.
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 the SageMaker built-in XGBoost algorithm, which can handle missing values by default.
Why this is correct
XGBoost has built-in support for missing values.
- ✗
Use the SageMaker BlazingText algorithm, which automatically imputes missing values.
Why it's wrong here
BlazingText is for text data and does not handle missing values.
- ✗
Use SageMaker Inference Pipeline to handle missing values at inference time.
Why it's wrong here
Inference pipelines are for deployment, not for training data preprocessing.
- ✓
Use SageMaker Processing to run a custom Python script that imputes missing values before training.
Why this is correct
SageMaker Processing allows custom preprocessing.
- ✗
Use SageMaker PCA algorithm, which automatically handles missing values.
Why it's wrong here
PCA does not handle missing values automatically.
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