Quick Answer
The correct answer is to impute missing values using mean or median. This is the best practice because it preserves the dataset size and statistical properties, allowing the AI model to learn from all available features without introducing bias from data removal—a critical consideration when handling missing data imputation for numerical features like customer churn predictors. On the Salesforce AI Associate exam, this concept tests your understanding of data preprocessing trade-offs: mean imputation works well for normally distributed data, while median is robust to outliers, and both avoid the information loss of dropping rows. A common trap is assuming deletion is safer, but imputation maintains sample size for algorithms like logistic regression or gradient boosting. Memory tip: “Mean for normal, median for skewed—keep your data, don’t get deleted.”
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.
A company wants to train an AI model to predict customer churn using historical data that contains many missing values. What is the best practice for handling missing data?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue: "best"
Why it matters: Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.
Use only features without missing values.
Why wrong: Discarding features may lose important information.
Ignore missing values as they do not affect AI training.
Why wrong: Ignoring missing values can lead to errors.
Impute missing values using mean or median.
Imputation preserves data and reduces bias.
Remove all records with missing values.
Why wrong: Removing records reduces dataset size and may introduce bias.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Impute missing values using mean or median.
Option C is correct because imputing missing values using mean or median is a standard practice that preserves the dataset size and statistical properties, allowing the AI model to learn from all available features without introducing bias from data removal. This approach is particularly effective for numerical features in customer churn prediction, where missing values are often random and imputation maintains the distribution for algorithms like logistic regression or gradient boosting.
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.
Use only features without missing values.
Why it's wrong here
Discarding features may lose important information.
Ignore missing values as they do not affect AI training.
Why it's wrong here
Ignoring missing values can lead to errors.
Impute missing values using mean or median.
Why this is correct
Imputation preserves data and reduces bias.
Clue confirmation
The clue word "best" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
Remove all records with missing values.
Why it's wrong here
Removing records reduces dataset size and may introduce bias.
Common exam traps
Salesforce often tests the misconception that removing missing data is safe, but the trap here is that candidates overlook how data removal can shrink the dataset and introduce bias, while imputation is a more balanced and widely accepted practice in AI workflows.
Detailed technical explanation
Under the hood, imputation with mean or median assumes the data is missing completely at random (MCAR) and preserves the overall mean, which is critical for models like linear regression that rely on covariance structures. In practice, more sophisticated methods like multiple imputation or model-based imputation (e.g., using k-NN or MICE) can capture uncertainty, but mean/median imputation is a robust baseline for many real-world churn datasets with moderate missingness.
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: Impute missing values using mean or median. — Option C is correct because imputing missing values using mean or median is a standard practice that preserves the dataset size and statistical properties, allowing the AI model to learn from all available features without introducing bias from data removal. This approach is particularly effective for numerical features in customer churn prediction, where missing values are often random and imputation maintains the distribution for algorithms like logistic regression or gradient boosting.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Yes — watch for: "best". Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 30, 2026
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