AI0-001 AI Models and Data Engineering Practice Question
A dataset used for training a classification model contains 10% missing values in a feature that is known to be important. The data scientist decides to impute the missing values. Which imputation method is most robust if the data is not missing completely at random?
⚠ Common exam trap
The AI0-001 exam often tests the misconception that mean imputation is a safe default for missing data, but the trap here is that mean imputation assumes data is missing completely at random (MCAR), which is rarely true in real-world datasets, and it fails to account for the underlying missing data mechanism.
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
✓
Use multiple imputation to model missing values
Multiple imputation is the most robust method when data is not missing completely at random (NMAR) because it uses a statistical model to account for the relationships between the missing feature and other observed features, generating multiple plausible values and combining them to produce unbiased estimates and valid standard errors. This approach preserves the variability and structure of the data, unlike simpler methods that can introduce bias when missingness depends on unobserved data.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Delete all rows with missing values
Why it's wrong here
Deleting rows reduces sample size and can introduce bias.
- ✓
Use multiple imputation to model missing values
Why this is correct
Multiple imputation provides unbiased estimates under missing at random assumption.
- ✗
Replace missing values with the mean of the feature
Why it's wrong here
Mean imputation reduces variance and can bias estimates.
- ✗
Fill missing values with 0
Why it's wrong here
Filling with zero is not meaningful and distorts the feature distribution.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This AI0-001 practice question is part of Courseiva's free CompTIA 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 AI0-001 exam.