AI0-001 Implementing AI Solutions Practice Question
In the AI project lifecycle, which phase involves splitting the dataset into training, validation, and test sets while ensuring no data leakage?
⚠ Common exam trap
It's easy for candidates to confuse 'data acquisition' (collecting data) with 'data preparation' (cleaning and splitting), leading them to incorrectly choose option C when the question specifically asks about splitting and leakage prevention.
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
Splitting the dataset into training, validation, and test sets is a core data preparation step that must be performed before any model training begins. This phase ensures that data leakage is prevented by keeping the test set completely isolated until final evaluation, which is critical for obtaining an unbiased estimate of model performance. In the AI project lifecycle, data preparation encompasses cleaning, transforming, and partitioning the data, making option A the correct phase.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Data preparation
Why this is correct
Data preparation covers dataset splitting into training, validation and test subsets, and enforces leakage prevention by fitting transformations only on training data before applying them elsewhere. This satisfies the stem's requirement that the split occur without leakage, which later modelling phases cannot retroactively correct.
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Problem definition
Why it's wrong here
Problem definition establishes business objectives, success criteria, and scope before any data is touched, so no splitting occurs there. It is tempting because framing the task correctly shapes later data handling, and it is the correct answer when the question asks which phase precedes data collection entirely.
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Data acquisition
Why it's wrong here
Data acquisition covers collecting, ingesting, and labelling raw data; it precedes any partitioning and does not enforce train/validation/test separation or leakage prevention. It is tempting because acquisition is genuinely the correct phase when the question asks where source data is gathered and consolidated before preparation begins.
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Model evaluation
Why it's wrong here
Model evaluation scores a trained model against held-out data; the splits must already exist and be leakage-free before this phase runs. It is tempting because evaluation depends on the test set, but it is the correct answer when the question asks which phase measures performance metrics such as accuracy or F1.
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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.