Question 391 of 754
AI0-001 AI Models and Data Engineering Practice Question
Which THREE are common data preprocessing steps in a machine learning pipeline? (Choose 3)
⚠ Common exam trap
CompTIA often tests the distinction between preprocessing steps (data cleaning, transformation) and later pipeline stages (model tuning, evaluation), so candidates mistakenly select hyperparameter tuning or model evaluation as preprocessing steps.
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
✓
Encoding categorical variables
Encoding categorical variables is a common data preprocessing step because machine learning algorithms require numerical input. Techniques like one-hot encoding or label encoding convert categorical data (e.g., colors, countries) into numeric format, enabling the model to process them correctly. Without this step, the model would misinterpret categorical labels as ordinal or meaningless numeric values.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Hyperparameter tuning
Why it's wrong here
Hyperparameter tuning is part of model optimization.
- ✓
Encoding categorical variables
Why this is correct
Categorical data must be converted to numeric.
- ✗
Model evaluation
Why it's wrong here
Model evaluation is after training.
- ✓
Scaling numeric features
Why this is correct
Scaling prevents features with larger ranges from dominating.
- ✓
Handling missing values
Why this is correct
Missing data must be addressed before training.
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Last reviewed: Jun 30, 2026
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