AI0-001 Implementing AI Solutions Practice Question
Which stage of the AI project lifecycle involves splitting data into training, validation, and test sets?
⚠ Common exam trap
The trap is choosing model evaluation because splitting sounds like an evaluation activity — but the split is performed during data preparation, and evaluation merely consumes the already-split test set.
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
✓
Data preparation
Data preparation is the stage where raw data is cleaned, transformed, and split into training, validation, and test sets. This split is a core part of preparing data for model training — the training set teaches the model, the validation set tunes hyperparameters, and the test set provides an unbiased final evaluation. It occurs before model training and evaluation, making data preparation the correct stage.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
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Model evaluation
Why it's wrong here
Model evaluation scores a trained model against held-out data; the split itself is created earlier, during data preparation. It is tempting because evaluation consumes the test set, so it would be the correct stage if the question asked where the test set is used to estimate generalisation performance.
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Data acquisition
Why it's wrong here
Data acquisition gathers and ingests raw data from sources; partitioning into training, validation and test sets occurs afterwards, during data preparation. It is tempting because acquisition precedes splitting and supplies the dataset, but it would be correct only if the question asked where data is collected and consolidated.
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Problem definition
Why it's wrong here
Problem definition scopes business objectives, success metrics and constraints before any data exists, so no splitting occurs there. It is tempting because it is the lifecycle's first stage and frames the project, yet partitioning into training, validation and test sets happens during data preparation, after acquisition.
- ✓
Data preparation
Why this is correct
Data preparation is the lifecycle stage where raw data is cleaned, transformed, and partitioned into training, validation, and test sets, satisfying the requirement to separate data before model training begins. This splitting prevents data leakage and enables unbiased evaluation, making it the stage that directly addresses the question's constraint.
About these practice questions
Courseiva writes every AI0-001 question from scratch — 962 in total, each with an explanation and a wrong-answer breakdown. None are copied from real exams or dumps. Learn why practice questions differ from exam dumps →
JA
Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official CompTIA exam blueprint
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.