MLA-C01 ML Model Development Practice Question
A machine learning engineer is preparing a dataset for training a SageMaker model. The dataset contains missing values in several numerical features. The engineer wants to handle these missing values during the training pipeline. Which two methods are valid ways to handle missing values in SageMaker? (Choose two.)
⚠ Common exam trap
Many exam-takers confuse monitoring, tuning, or bias detection services with data preprocessing capabilities.
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 SageMaker Data Wrangler to impute missing values with the mean or median.
SageMaker Data Wrangler offers built-in transformations to impute missing values, and the built-in XGBoost algorithm natively handles missing values by learning default directions. Both are valid methods for dealing with missing data in a SageMaker training pipeline. The other services are not designed for data preprocessing.
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 Data Wrangler to impute missing values with the mean or median.
Why this is correct
SageMaker Data Wrangler provides built-in transformations for handling missing values, including imputation with mean, median, or mode. It allows you to visually inspect and apply these transformations as part of a data preparation flow, which can then be exported to a pipeline. This is a valid and recommended approach for handling missing values before training.
- ✗
Use SageMaker Clarify to replace missing values with the mode of each column.
Why it's wrong here
SageMaker Clarify is used for bias detection and explainability, not for data imputation. It analyzes model predictions and data for bias, but does not modify the dataset. Replacing missing values is a preprocessing step that Clarify does not perform.
- ✗
Use SageMaker Debugger to automatically fill missing values during training.
Why it's wrong here
SageMaker Debugger is a tool for monitoring and debugging training jobs, such as detecting vanishing gradients or overfitting. It does not provide data preprocessing capabilities like missing value imputation. Its purpose is to collect and analyze tensors during training, not to modify the input data.
- ✓
Enable the SageMaker built-in XGBoost algorithm's default handling of missing values.
Why this is correct
The SageMaker built-in XGBoost algorithm automatically handles missing values by learning a default direction for missing data during tree construction. This means you can train directly on data with missing values without explicit imputation. This is a valid method, as XGBoost's implementation is designed to handle missing values internally.
- ✗
Configure SageMaker Automatic Model Tuning to impute missing values via hyperparameter search.
Why it's wrong here
SageMaker Automatic Model Tuning searches for optimal hyperparameters but does not perform data imputation. It tunes model hyperparameters like learning rate or tree depth, not data preprocessing steps. Missing values must be handled before training or by the algorithm itself, not through hyperparameter tuning.
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Last reviewed September 2026 · checked against the official Amazon Web Services exam blueprint
This MLA-C01 practice question is part of Courseiva's free Amazon Web Services certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the MLA-C01 exam.