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AI Models and Data EngineeringmediumMultiple SelectObjective-mapped

AI0-001 AI Models and Data Engineering Practice Question

Which THREE are common causes of data leakage in machine learning pipelines?

⚠ Common exam trap

CompTIA often tests the distinction between valid data splitting practices and actual leakage causes, so candidates may incorrectly select time-based splitting (Option A) as a leakage cause when it is actually a proper technique for sequential data.

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

Using future information to predict the present

Using future information to predict the present is a classic form of data leakage. In time series or sequential data, if a model is trained on features that include values from a later time point, it gains access to information that would not be available at prediction time, leading to overly optimistic performance metrics and poor generalization.

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

  • Using time-based splitting for sequential data

    Why it's wrong here

    Time-based splitting is designed to prevent leakage by respecting temporal order.

  • Using future information to predict the present

    Why this is correct

    Using data that would not be available at prediction time is a direct form of leakage.

  • Using cross-validation on the entire dataset

    Why it's wrong here

    Cross-validation is a proper validation technique and does not cause leakage when done correctly.

  • Applying normalization before splitting data into train and test sets

    Why this is correct

    Normalizing before splitting uses statistics from the whole dataset, leaking test information into training.

  • Including features that are directly derived from the target variable

    Why this is correct

    If a feature is created using the target, the model sees information it should not have.

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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.