The answer is model-based imputation, which uses relationships between other features like education and job role to predict missing annual income values. This approach is best for minimizing bias because it preserves the natural data distribution and avoids the distortion caused by simple mean or zero imputation, while also retaining sample size better than deletion. On the Salesforce AI Associate AI Associate exam, this question tests your understanding of bias mitigation in data preparation—a core concept in ethical AI. A common trap is choosing mean imputation, which can artificially compress variance and introduce systematic bias, especially for skewed fields like income. Remember the memory tip: “Model maps the missing, mean masks the bias.”
AI Associate Data for AI Practice Question
This AI Associate practice question tests your understanding of data for ai. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. 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 is preparing customer data for a predictive model. They notice that many records have missing values for the 'annual income' field. Which approach is best to handle this issue while minimizing bias?
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.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Use model-based imputation considering other features.
Model-based imputation (Option B) is best because it uses relationships between features (e.g., education, job role) to predict missing 'annual income' values, preserving data distribution and minimizing bias. This approach avoids the distortion caused by simple mean/zero imputation and retains sample size better than deletion.
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.
✗
Remove all records with missing values.
Why it's wrong here
Deleting records reduces sample size and may introduce bias if missingness is not random.
✓
Use model-based imputation considering other features.
Why this is correct
Model-based imputation leverages other features to predict missing values, preserving relationships and minimizing 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.
✗
Replace missing values with the mean.
Why it's wrong here
Mean imputation distorts the distribution and can weaken correlations.
✗
Set missing values to zero.
Why it's wrong here
Setting to zero introduces a large bias and is not appropriate for income data.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Salesforce often tests the misconception that mean imputation is a safe default, but the trap here is that it ignores feature dependencies and can artificially shrink variance, leading to overconfident model predictions and biased coefficients.
Detailed technical explanation
How to think about this question
Model-based imputation often uses techniques like k-nearest neighbors (KNN) or regression imputation, where a model trained on complete cases predicts missing values from other features. Under the hood, this leverages covariance structures in the data, making it superior to single-value imputation which assumes missingness is completely random (MCAR). In real-world scenarios, income is often missing not at random (MNAR), so using correlated features (e.g., occupation, education) reduces bias more effectively than simple methods.
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 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
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Data for AI — This question tests Data for AI — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Use model-based imputation considering other features. — Model-based imputation (Option B) is best because it uses relationships between features (e.g., education, job role) to predict missing 'annual income' values, preserving data distribution and minimizing bias. This approach avoids the distortion caused by simple mean/zero imputation and retains sample size better than deletion.
What should I do if I get this AI Associate question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Are there clue words in this question I should notice?
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.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. Which TWO techniques are commonly used to handle missing values in a dataset for AI training?
hard
A.L1 regularization
✓ B.Deletion of rows with missing values
C.One-hot encoding
D.Min-max normalization
✓ E.Imputation with mean or median
Why B: 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.
Last reviewed: Jun 30, 2026
Question Discussion
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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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