MLA-C01 Data Preparation for Machine Learning Practice Question

A data scientist is preparing a dataset stored in Amazon S3 for a SageMaker training job. The dataset contains missing values in several columns. The scientist wants to impute missing values with the mean of each column. Which SageMaker built-in algorithm or processing method should be used to perform this imputation efficiently?

⚠ Common exam trap

The trap here is assuming that built-in algorithms like XGBoost handle missing values, but the question specifically asks for mean imputation, which requires a dedicated preprocessing transform.

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 'Impute' transform in Data Wrangler.

Mean imputation is a common preprocessing step for numerical features. In SageMaker Data Wrangler, the 'Impute' transform provides a user-friendly way to replace missing values with the column mean, median, or mode. This transform can be applied to multiple columns and is part of the data preparation flow. It ensures that the dataset is complete before feeding it into a training algorithm, which is essential for algorithms that cannot handle missing values natively.

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 'PCA' algorithm to fill missing values.
Why it's wrong here
Principal Component Analysis (PCA) is a dimensionality reduction technique, not an imputation method. It cannot handle missing values directly and would require complete data. Using PCA for imputation is not a standard practice and would not provide the mean substitution needed. Therefore, it is incorrect for this scenario.
✗
Use the SageMaker 'Linear Learner' algorithm to predict missing values.
Why it's wrong here
Linear Learner is a supervised learning algorithm for classification or regression. While it could theoretically be used to predict missing values in a column using other columns as features, that is a complex and indirect approach. The scenario specifically asks for mean imputation, which is a simple statistical method, so Linear Learner is overkill and not the intended solution.
✓
Use the SageMaker 'Impute' transform in Data Wrangler.
Why this is correct
SageMaker Data Wrangler offers an 'Impute' transform that allows replacing missing values with various strategies, including mean, median, or mode. This is a straightforward and efficient way to handle missing numerical data within the Data Wrangler interface, which can then be exported to a processing job or pipeline. It directly fulfills the requirement of mean imputation.
✗
Use the SageMaker 'XGBoost' algorithm to predict missing values.
Why it's wrong here
XGBoost is a powerful gradient boosting algorithm for supervised tasks. It can handle missing values internally during training, but it does not perform mean imputation as a preprocessing step. The requirement is to impute missing values with the mean before training, so using XGBoost for this purpose is not appropriate.

Quick reference

AWS S3 Storage Class Comparison

Storage Class	Min Duration	Retrieval	Use Case
S3 Standard	None	Immediate	Frequently accessed data
S3 Standard-IA	30 days	Immediate	Infrequent access, rapid retrieval
S3 One Zone-IA	30 days	Immediate	Non-critical infrequent data
S3 Intelligent-Tiering	None	Immediate–hours	Unknown or changing access patterns
S3 Glacier Instant	90 days	Milliseconds	Archive with instant retrieval
S3 Glacier Flexible	90 days	Minutes–hours	Archive, flexible retrieval
S3 Glacier Deep Archive	180 days	Hours	Long-term compliance archive

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MLA-C01 Data Preparation for Machine Learning Practice Question

Why each option matters

Option-by-option breakdown

AWS S3 Storage Class Comparison

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