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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 includes splitting and ensuring no leakage from future information.

  • Problem definition

    Why it's wrong here

    Problem definition identifies goals, not data splitting.

  • Data acquisition

    Why it's wrong here

    Data acquisition gathers raw data; splitting and leakage prevention happen during preparation.

  • Model evaluation

    Why it's wrong here

    Model evaluation uses already prepared splits; it does not set them up.

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Written by Johnson Ajibi, MSc IT Security

Senior Network & Security Engineer · founder of Courseiva

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