AI0-001 Implementing AI Solutions Practice Question
A data scientist is preparing a dataset for a classification model. The dataset has missing values in several features and features with very different scales. Which two data preparation steps should be applied?
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
✓
Cleaning and normalization
Cleaning handles missing values (e.g., imputation), and normalization scales features to a similar range, which is important for many ML algorithms.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Cleaning and normalization
Why this is correct
Correct: cleaning addresses missing values, normalization addresses scale differences.
- ✗
Outlier removal and binning
Why it's wrong here
Outlier removal and binning are not the primary steps for missing values and scaling.
- ✗
Feature selection and dimensionality reduction
Why it's wrong here
These steps may be applied later but do not directly address missing values or scale.
- ✗
Data augmentation and one-hot encoding
Why it's wrong here
Data augmentation creates synthetic data; one-hot encoding is for categorical variables, not scale or missing values.
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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.