AI0-001 Machine Learning and Deep Learning Practice Question
A company is preparing a dataset for training a supervised machine learning model. The dataset contains missing values, outliers, and categorical features. Which two preprocessing steps are typically performed to prepare the data? (Choose two.)
⚠ Common exam trap
The AI0-001 exam often tests the distinction between mandatory preprocessing steps (like handling missing values and encoding categories) and optional optimization techniques (like normalization or feature selection), leading candidates to select scaling or PCA as default steps when they are not universally required.
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
✓
Impute missing values with the mean
Imputing missing values with the mean is a standard technique to handle incomplete data, ensuring the model can process all records without discarding potentially valuable information. Option C is correct because one-hot encoding converts categorical features into a binary vector representation, which is required by most machine learning algorithms that expect numerical input.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Normalize numerical features to a standard range
Why it's wrong here
Normalization is useful for distance-based models but not always required; it is not as universally necessary as imputation and encoding.
- ✓
Impute missing values with the mean
Why this is correct
Imputation handles missing data and is commonly done.
- ✓
Encode categorical variables using one-hot encoding
Why this is correct
One-hot encoding converts categorical data into numerical format required by most algorithms.
- ✗
Remove all features with low variance
Why it's wrong here
Low variance features may still be informative; removal is not a typical first step.
- ✗
Increase the number of features using PCA
Why it's wrong here
PCA reduces dimensionality, not increases features.
About these practice questions
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