Quick Answer
The answer is handling missing values, encoding categorical variables, and feature scaling. These three are considered common data preprocessing steps in machine learning because raw data almost always contains inconsistencies that algorithms cannot interpret directly. Handling missing values prevents biased or broken models by either imputing gaps with statistical measures like the mean or median, or removing incomplete records. Encoding categorical variables transforms non-numeric labels—such as product categories or country names—into numerical formats like one-hot or label encoding, which is essential since most machine learning models require numeric input. Feature scaling, through methods like standardization or normalization, ensures that variables with larger magnitudes do not dominate the learning process. On the CompTIA AI+ AI0-001 exam, this question tests your understanding of the data preparation phase, often appearing with distractors like “increasing dataset size” or “selecting a model architecture.” A common trap is forgetting that scaling is distinct from encoding. Remember the mnemonic “MES” for Missing values, Encoding, Scaling—three pillars that turn messy data into a model-ready foundation.
This AI0-001 practice question tests your understanding of ai models and data engineering. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
Which THREE are common data preprocessing steps in a machine learning pipeline? (Choose 3)
Hyperparameter tuning
Why wrong: Hyperparameter tuning is part of model optimization.
Encoding categorical variables
Categorical data must be converted to numeric.
Model evaluation
Why wrong: Model evaluation is after training.
Scaling numeric features
Scaling prevents features with larger ranges from dominating.
Handling missing values
Missing data must be addressed before training.
Answer choices
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.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
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.
Related concept
Read the scenario before looking for a memorised answer.
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.
Related concept
Read the scenario before looking for a memorised answer.
Handling missing values
Why this is correct
Missing data must be addressed before training.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
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.
Detailed technical explanation
Under the hood, scaling numeric features (e.g., using StandardScaler or MinMaxScaler) ensures that features with larger magnitudes do not dominate distance-based algorithms like k-nearest neighbors or gradient descent. Handling missing values might involve imputation (mean, median, or mode) or deletion, and the choice can significantly impact model bias and variance. In real-world scenarios, failing to encode categorical variables can cause algorithms like linear regression to treat categories as ordered, leading to incorrect coefficient interpretations.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
A practitioner preparing for the AI0-001 exam encounters this exact type of scenario on the job. The correct answer here is not the most general option — it is the best answer for the specific constraint described. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Real exam questions reward reading the full scenario before eliminating options, because the constraint defines which answer fits.
What to study next
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FAQ
AI Models and Data Engineering — This question tests AI Models and Data Engineering — Read the scenario before looking for a memorised answer..
The correct answer is: 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.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 30, 2026
This AI0-001 practice question is part of Courseiva's free CompTIA certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the AI0-001 exam.
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