AI Associate Data for AI Practice Question
Which TWO techniques are commonly used to handle missing values in a dataset for AI training?
⚠ Common exam trap
Salesforce often tests the distinction between data preprocessing techniques (like handling missing values) and model regularization or feature engineering, so candidates may confuse L1 regularization or one-hot encoding as methods for missing data when they serve entirely different purposes.
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
✓
Deletion of rows with missing values
Deleting rows with missing values is a straightforward technique to handle missing data, especially when the missingness is random and the dataset is large enough that removing a few rows does not significantly impact model performance. This approach avoids introducing bias from imputation methods but can lead to loss of valuable information if too many rows are removed.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
L1 regularization
Why it's wrong here
Regularization to prevent overfitting.
- ✓
Deletion of rows with missing values
Why this is correct
Simple but valid method.
- ✗
One-hot encoding
Why it's wrong here
Encoding categorical variables.
- ✗
Min-max normalization
Why it's wrong here
Scaling, not missing data handling.
- ✓
Imputation with mean or median
Why this is correct
Common imputation method.
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