AI0-001 AI Models and Data Engineering Practice Question
Which TWO strategies are effective for handling missing values in a dataset when the missingness is not random (MNAR)?
⚠ Common exam trap
CompTIA often tests the misconception that mean imputation or KNN imputation are safe defaults for any missing data pattern, but the trap here is that MNAR requires methods that explicitly model the missingness mechanism, which simple imputation techniques fail to do.
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
✓
Multiple imputation using chained equations
Multiple imputation using chained equations (MICE) is effective for MNAR because it models each variable with missing values as a function of other variables, iteratively generating plausible values that preserve the relationships and uncertainty in the data. This approach can account for the systematic pattern of missingness by incorporating auxiliary variables that are correlated with both the missing values and the missingness mechanism, making it robust even 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.
- ✓
Multiple imputation using chained equations
Why this is correct
Multiple imputation can handle MNAR if the imputation model incorporates variables that predict missingness.
- ✓
Treat missing as a separate category (e.g., for categorical features)
Why this is correct
Treating missing as its own category allows the model to capture potential non-random patterns.
- ✗
Listwise deletion
Why it's wrong here
Listwise deletion discards all rows with missing data, which can introduce bias under MNAR.
- ✗
KNN imputation
Why it's wrong here
KNN imputation assumes MAR (Missing at Random) and may be inappropriate for MNAR.
- ✗
Mean imputation
Why it's wrong here
Mean imputation reduces variance and can bias estimates, especially under MNAR.
About these practice questions
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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.