AI0-001 AI Models and Data Engineering Practice Question
A data scientist is preparing a dataset for training a classification model. The dataset has a column with missing values in 5% of rows. Which action should the data engineer take to minimize bias?
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 median of the column
Imputing with the median preserves the distribution without significantly reducing sample size, minimizing bias. Removing rows reduces sample size, constant 999 introduces artificial outlier, and native handling may not be available.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Impute missing values with the median of the column
Why this is correct
Median imputation preserves the central tendency without being affected by outliers, suitable for low missing rate.
- ✗
Remove all rows with missing values
Why it's wrong here
Removing rows reduces sample size and may introduce bias if missingness is not random.
- ✗
Replace missing values with a constant such as 999
Why it's wrong here
A constant like 999 creates an artificial outlier that can skew the model.
- ✗
Use a model that can handle missing values natively
Why it's wrong here
While some models handle missing values, they often do so via internal imputation or tree-based splits, which may be less transparent.
About these practice questions
This AI0-001 question is part of Courseiva's 754-question bank — original exam-style content with full explanations and wrong-answer analysis, never real exam questions or exam dumps. Learn why practice questions differ from exam dumps →
JA
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.