AI0-001 AI Models and Data Engineering Practice Question
Exhibit
{
"data_pipeline": {
"input": "raw_sales.csv",
"steps": [
{"type": "drop_columns", "columns": ["customer_id", "transaction_id"]},
{"type": "impute_missing", "strategy": "mean", "columns": ["age", "income"]},
{"type": "encode_categorical", "method": "onehot", "columns": ["product_category"]},
{"type": "normalize", "method": "minmax", "columns": ["age", "income"]}
],
"output": "processed_sales.parquet"
}
}Refer to the exhibit. A data scientist reviews the pipeline and notes that the model performance degraded. Which change to the pipeline would most likely improve model performance?
⚠ Common exam trap
The AI0-001 exam often tests the candidate's understanding that mean imputation is not always optimal, especially when outliers are present, and that median imputation is a simple yet effective robust alternative.
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
✓
Change the impute strategy from mean to median for the 'income' column.
The 'income' column likely contains outliers that skew the mean, making mean imputation sensitive to extreme values. Using median imputation is more robust to outliers, preserving the central tendency of the data and reducing bias introduced during preprocessing. This directly addresses the model performance degradation caused by distorted feature distributions.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Change the impute strategy from mean to median for the 'income' column.
Why this is correct
Income often has outliers; median is less affected by extremes.
- ✗
Remove the normalization step entirely.
Why it's wrong here
Normalization helps many algorithms; removing it can degrade performance.
- ✗
Drop the 'product_category' column instead of one-hot encoding.
Why it's wrong here
Dropping a potentially important feature loses information.
- ✗
Change the encoding method from onehot to label encoding.
Why it's wrong here
Label encoding on nominal categories creates incorrect ordinal assumptions.
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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.