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.
Which TWO techniques are commonly used to handle missing values in a dataset for AI training?
L1 regularization
Why wrong: Regularization to prevent overfitting.
Deletion of rows with missing values
Simple but valid method.
One-hot encoding
Why wrong: Encoding categorical variables.
Min-max normalization
Why wrong: Scaling, not missing data handling.
Imputation with mean or median
Common imputation method.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Deletion of rows with missing values
Option B is correct because deleting rows with missing values is a straightforward technique to handle missing data, especially when the missingness is random and the dataset is large enough that removing a few rows does not significantly impact model performance. This approach avoids introducing bias from imputation methods but can lead to loss of valuable information if too many rows are removed.
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.
L1 regularization
Why it's wrong here
Regularization to prevent overfitting.
Deletion of rows with missing values
Why this is correct
Simple but valid method.
Related concept
Read the scenario before looking for a memorised answer.
One-hot encoding
Why it's wrong here
Encoding categorical variables.
Min-max normalization
Why it's wrong here
Scaling, not missing data handling.
Imputation with mean or median
Why this is correct
Common imputation method.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Salesforce often tests the distinction between data preprocessing techniques (like handling missing values) and model regularization or feature engineering, so candidates may confuse L1 regularization or one-hot encoding as methods for missing data when they serve entirely different purposes.
Detailed technical explanation
Under the hood, imputation with mean or median preserves the dataset size but can reduce variance and introduce bias, particularly if missing data is not completely at random (MCAR). In real-world scenarios, such as sensor data in IoT, deletion may be preferred when missing values are sparse, while imputation is common in survey data where missingness is structured. Advanced imputation methods like KNN or MICE can capture relationships between features but are more computationally intensive.
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: Deletion of rows with missing values — Option B is correct because deleting rows with missing values is a straightforward technique to handle missing data, especially when the missingness is random and the dataset is large enough that removing a few rows does not significantly impact model performance. This approach avoids introducing bias from imputation methods but can lead to loss of valuable information if too many rows are removed.
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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