This AI Associate practice question tests your understanding of data for ai. 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.
During data transformation, a data scientist applies one-hot encoding to a categorical feature with 50 unique values. The resulting dataset has 50 new columns. What is a potential drawback of this transformation?
Reduction in training time
Why wrong: More features increase training time.
Increased interpretability of the model
Why wrong: One-hot can make models less interpretable with many columns.
High cardinality leading to sparse data and overfitting
High cardinality creates many sparse columns, risking overfitting.
Loss of ordinal information in categories
Why wrong: Ordinal info is lost, but that's not the main drawback here.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
High cardinality leading to sparse data and overfitting
One-hot encoding a categorical feature with 50 unique values creates 50 binary columns, each representing one category. This high cardinality leads to a very sparse matrix (most entries are 0), which can cause the model to overfit by learning noise from rare categories, especially when the dataset is not large enough to support such dimensionality.
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.
Reduction in training time
Why it's wrong here
More features increase training time.
Increased interpretability of the model
Why it's wrong here
One-hot can make models less interpretable with many columns.
High cardinality leading to sparse data and overfitting
Why this is correct
High cardinality creates many sparse columns, risking overfitting.
Related concept
Read the scenario before looking for a memorised answer.
Loss of ordinal information in categories
Why it's wrong here
Ordinal info is lost, but that's not the main drawback here.
Common exam traps
Salesforce often tests the misconception that one-hot encoding always improves model performance by preserving all information, when in fact high cardinality introduces sparsity and overfitting risks that can degrade model accuracy.
Detailed technical explanation
Under the hood, one-hot encoding creates a binary vector for each category, resulting in a sparse matrix where each row has exactly one '1' and 49 '0's. This sparsity can cause the curse of dimensionality, where distance metrics become less meaningful and models like linear regression or tree-based models may overfit to rare categories. In real-world scenarios, such as encoding a 'zip code' feature with 50 values, this can lead to poor generalization on unseen data unless regularization or dimensionality reduction (e.g., feature hashing) is applied.
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: High cardinality leading to sparse data and overfitting — One-hot encoding a categorical feature with 50 unique values creates 50 binary columns, each representing one category. This high cardinality leads to a very sparse matrix (most entries are 0), which can cause the model to overfit by learning noise from rare categories, especially when the dataset is not large enough to support such dimensionality.
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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