AI0-001 Machine Learning and Deep Learning Practice Question
Which THREE of the following are techniques for handling missing data in machine learning?
⚠ Common exam trap
The CompTIA AI exam often tests the distinction between techniques that directly handle missing data versus those that are preprocessing or modeling steps that assume complete data, leading candidates to mistakenly select PCA or autoencoder reconstruction as missing data methods.
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 for handling missing data, often used when the missingness is random and the dataset is large enough that removing a few rows does not significantly impact model performance. This method avoids introducing bias from imputation 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.
- ✓
Deletion of rows with missing values
Why this is correct
Listwise deletion removes incomplete records; a basic approach.
- ✗
Autoencoder reconstruction
Why it's wrong here
Autoencoders can impute but are advanced and not standard for routine missing data.
- ✓
Mean imputation
Why this is correct
Replacing missing values with the column mean is a common simple imputation method.
- ✗
Principal Component Analysis
Why it's wrong here
PCA reduces dimensionality and does not handle missing values directly.
- ✓
Using a separate category for missing values
Why this is correct
Treating 'missing' as a distinct category is used for categorical data.
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