- A
Using time-based splitting for sequential data
Why wrong: Time-based splitting is designed to prevent leakage by respecting temporal order.
- B
Using future information to predict the present
Using data that would not be available at prediction time is a direct form of leakage.
- C
Using cross-validation on the entire dataset
Why wrong: Cross-validation is a proper validation technique and does not cause leakage when done correctly.
- D
Applying normalization before splitting data into train and test sets
Normalizing before splitting uses statistics from the whole dataset, leaking test information into training.
- E
Including features that are directly derived from the target variable
If a feature is created using the target, the model sees information it should not have.
AI0-001 AI Models and Data Engineering Practice Question
This AI0-001 practice question tests your understanding of ai models and data engineering. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
Which THREE are common causes of data leakage in machine learning pipelines?
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
Option B is correct because 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.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
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.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
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.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
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.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
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.
Detailed technical explanation
How to think about this question
Data leakage often occurs when preprocessing steps like normalization or feature engineering are applied to the entire dataset before splitting. For example, computing mean and standard deviation for normalization on the full dataset leaks information from the test set into the training set, artificially reducing variance and inflating validation scores. In real-world scenarios, this can cause models to fail dramatically in production when encountering unseen data distributions.
KKey Concepts to Remember
- Read the scenario before looking for a memorised answer.
- Find the constraint that changes the correct option.
- Eliminate answers that are true in general but not in this case.
TExam Day Tips
- Watch for words such as best, first, most likely and least administrative effort.
- Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A practitioner preparing for the AI0-001 exam encounters this exact type of scenario on the job. The correct answer here is not the most general option — it is the best answer for the specific constraint described. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Real exam questions reward reading the full scenario before eliminating options, because the constraint defines which answer fits.
What to study next
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
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FAQ
Questions learners often ask
What does this AI0-001 question test?
AI Models and Data Engineering — This question tests AI Models and Data Engineering — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Using future information to predict the present — Option B is correct because 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.
What should I do if I get this AI0-001 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
Last reviewed: Jun 30, 2026
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.
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