- A
Min-max scaling
Why wrong: Min-max scaling is a normalization method for numerical features, not for encoding categorical variables.
- B
K-means clustering
Why wrong: K-means is a clustering algorithm, not an encoding technique.
- C
One-hot encoding
One-hot encoding converts each category into a binary vector, suitable for categorical variables.
- D
Principal component analysis (PCA)
Why wrong: PCA is a dimensionality reduction technique, not an encoding method.
- E
Label encoding
Label encoding assigns a unique integer to each category, appropriate for ordinal or high-cardinality categorical variables.
Question 567 of 1,000
AI0-001 AI Models and Data Engineering Practice Question
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.
A data scientist is preparing a dataset for a classification model. The dataset contains several categorical variables with high cardinality. Which TWO encoding methods are appropriate for converting these categorical variables into numerical features?
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
✓
One-hot encoding
One-hot encoding is appropriate for high-cardinality categorical variables because it creates binary columns for each category, allowing the model to treat each category as an independent feature without imposing an ordinal relationship. This is crucial for classification models that assume numerical inputs, as it prevents the model from misinterpreting arbitrary integer labels as having meaningful order or magnitude.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Min-max scaling
Why it's wrong here
Min-max scaling is a normalization method for numerical features, not for encoding categorical variables.
- ✗
K-means clustering
Why it's wrong here
K-means is a clustering algorithm, not an encoding technique.
- ✓
One-hot encoding
Why this is correct
One-hot encoding converts each category into a binary vector, suitable for categorical variables.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Principal component analysis (PCA)
Why it's wrong here
PCA is a dimensionality reduction technique, not an encoding method.
- ✓
Label encoding
Why this is correct
Label encoding assigns a unique integer to each category, appropriate for ordinal or high-cardinality categorical variables.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Candidates may incorrectly discard label encoding (option E) because they assume it always imposes an ordinal relationship, but in many classification contexts (e.g., tree-based models) label encoding can handle high-cardinality nominal variables without issue. The trap is to overgeneralize the rule against label encoding, even when the question lists it as a correct answer alongside one-hot encoding.
Detailed technical explanation
How to think about this question
One-hot encoding creates a sparse matrix where each category becomes a binary column, which can significantly increase dimensionality with high cardinality—a challenge often mitigated by techniques like feature hashing or target encoding. Label encoding, while simpler, assigns arbitrary integers (e.g., 0, 1, 2) that can imply ordinality, misleading tree-based models that split on numerical thresholds. In practice, for high-cardinality features like zip codes or product IDs, one-hot encoding may lead to the curse of dimensionality, so alternatives like frequency encoding or embedding layers are sometimes preferred.
KKey Concepts to Remember
- Read the scenario before looking for a memorised answer.
- Find the constraint that changes the correct option.
- Eliminate answers that are true in general but not in this case.
TExam Day Tips
- Watch for words such as best, first, most likely and least administrative effort.
- Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
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
Questions learners often ask
What does this AI0-001 question test?
AI Models and Data Engineering — This question tests AI Models and Data Engineering — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: One-hot encoding — One-hot encoding is appropriate for high-cardinality categorical variables because it creates binary columns for each category, allowing the model to treat each category as an independent feature without imposing an ordinal relationship. This is crucial for classification models that assume numerical inputs, as it prevents the model from misinterpreting arbitrary integer labels as having meaningful order or magnitude.
What should I do if I get this AI0-001 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Last reviewed: Jul 4, 2026
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