This AI Associate practice question tests your understanding of data for ai. Compare every option against the stated constraints before choosing — the best answer satisfies all requirements, not just the most obvious one. 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 data transformation is most appropriate for converting categorical variables into numerical format for a machine learning model?
Normalization.
Why wrong: Normalization scales numerical values to a range.
One-hot encoding.
One-hot encoding creates binary columns for each category, making them usable in models.
Principal component analysis.
Why wrong: PCA is a dimensionality reduction technique, not a transformation for categorical data.
Standardization.
Why wrong: Standardization is for numerical features, not categorical.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
One-hot encoding.
One-hot encoding is the correct transformation because it converts categorical variables into a binary vector representation, where each category becomes a separate column with a 1 or 0. This allows machine learning models to interpret categorical data without implying any ordinal relationship, which is essential for algorithms that rely on numerical distances or linear algebra.
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.
Normalization.
Why it's wrong here
Normalization scales numerical values to a range.
One-hot encoding.
Why this is correct
One-hot encoding creates binary columns for each category, making them usable in models.
Related concept
Read the scenario before looking for a memorised answer.
Principal component analysis.
Why it's wrong here
PCA is a dimensionality reduction technique, not a transformation for categorical data.
Standardization.
Why it's wrong here
Standardization is for numerical features, not categorical.
Common exam traps
Salesforce often tests the distinction between data preprocessing techniques (normalization, standardization) and encoding methods, trapping candidates who confuse scaling with categorical conversion.
Detailed technical explanation
Under the hood, one-hot encoding creates a sparse matrix where each row has exactly one '1' and the rest '0's, which can significantly increase the feature space for high-cardinality categories. In real-world scenarios, this can lead to the 'curse of dimensionality' or memory issues, so techniques like hashing or target encoding are sometimes used as alternatives. Additionally, some tree-based models can handle label encoding directly, but one-hot encoding remains the safest default for linear models and neural networks.
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 AI Associate 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
Data for AI — This question tests Data for AI — Read the scenario before looking for a memorised answer..
The correct answer is: One-hot encoding. — One-hot encoding is the correct transformation because it converts categorical variables into a binary vector representation, where each category becomes a separate column with a 1 or 0. This allows machine learning models to interpret categorical data without implying any ordinal relationship, which is essential for algorithms that rely on numerical distances or linear algebra.
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 AI Associate practice question is part of Courseiva's free Salesforce 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 AI Associate exam.
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