AI0-001 AI Models and Data Engineering Practice Question
A data scientist is preparing a dataset for a supervised learning model. The dataset contains missing values in 15% of the rows for a numeric feature. Which preprocessing technique should be applied to minimize bias?
⚠ Common exam trap
CompTIA often tests the misconception that removing rows with missing values is always safe, but the trap here is that candidates overlook how even 15% missingness can bias the model if the missingness is not random, making imputation a more robust preprocessing step.
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
✓
Impute missing values with the mean of the feature.
Imputing missing values with the mean of the feature is the correct approach because it preserves the overall distribution of the numeric feature and avoids introducing systematic bias when only 15% of rows are affected. Removing rows would reduce sample size and potentially introduce selection bias, while encoding missing values as a separate category is inappropriate for numeric features in regression or classification tasks. Mean imputation is a standard, low-bias technique for MCAR (Missing Completely At Random) data in supervised learning pipelines.
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 rows with missing values.
Why it's wrong here
Removing rows may introduce bias if the missing data is not missing completely at random, and reduces sample size.
- ✓
Impute missing values with the mean of the feature.
Why this is correct
Mean imputation preserves the dataset size and is appropriate for numeric features when missingness is random.
- ✗
Encode the missing values as a separate category.
Why it's wrong here
Encoding missing values as a separate category is typically used for categorical features, not numeric.
- ✗
Use a model that handles missing values natively.
Why it's wrong here
Not all models handle missing values natively, and those that do may still produce biased results depending on the pattern of missingness.
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.