MLA-C01 Data Preparation for Machine Learning Practice Question
A data scientist is preparing a large dataset for training a machine learning model. The dataset contains missing values in several columns. Which approach is the MOST efficient for handling missing values in a large dataset using AWS services?
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 Amazon SageMaker Data Wrangler to impute missing values using built-in transforms.
Amazon SageMaker Data Wrangler provides a visual interface and built-in transforms for handling missing values efficiently at scale, without writing custom code. Glue ETL is more code-heavy, and imputation with pandas is not scalable for large datasets. Removing all rows with missing values is not always optimal and may not be efficient.
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 AWS Glue ETL to write a custom Python script that imputes missing values with the mean.
Why it's wrong here
Custom scripts require development effort and may not be the most efficient for large datasets.
- ✓
Use Amazon SageMaker Data Wrangler to impute missing values using built-in transforms.
Why this is correct
Data Wrangler provides efficient, scalable, and visual data preparation without custom code.
- ✗
Use pandas in a SageMaker notebook to impute missing values with the median.
Why it's wrong here
pandas is not designed for large-scale data processing and may run out of memory.
- ✗
Remove all rows with missing values from the dataset.
Why it's wrong here
Removing rows can lead to data loss and biased models, and is not always efficient.
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