- A
Remove duplicate records only from the test set to ensure uniqueness
Why wrong: Removing duplicates only from the test set could introduce bias; deduplication should be done consistently across the whole dataset.
- B
Shuffle the entire dataset randomly before splitting into train and test sets
Why wrong: Random shuffling before splitting can cause leakage if the data has temporal dependencies; it ignores time order.
- C
Split the data chronologically (e.g., use data before a certain date for training, after for testing)
Chronological splitting preserves the temporal order, preventing future data from leaking into the training set.
- D
Normalize numerical features using statistics computed on the entire dataset before splitting
Why wrong: Normalizing on the full dataset leaks information from the test set into the training set; normalization should be fit on training data only.
- E
Perform feature selection using only the training data, then apply the same features to the test set
Feature selection should be done within the training set to avoid leakage; applying the same selection to the test set is fine as long as the selection process didn't see the test data.
AI0-001 Implementing AI Solutions Practice Question
This AI0-001 practice question tests your understanding of implementing ai solutions. 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.
A data scientist is preparing a dataset for training a customer churn prediction model. To prevent train/test leakage, which TWO practices should be followed? (Select TWO)
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
Split the data chronologically (e.g., use data before a certain date for training, after for testing)
To prevent leakage, time-based splitting respects temporal order (no future data in training). Not normalizing before splitting avoids information from the test set influencing training. The other options either cause leakage or are unrelated.
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.
- ✗
Remove duplicate records only from the test set to ensure uniqueness
Why it's wrong here
Removing duplicates only from the test set could introduce bias; deduplication should be done consistently across the whole dataset.
- ✗
Shuffle the entire dataset randomly before splitting into train and test sets
Why it's wrong here
Random shuffling before splitting can cause leakage if the data has temporal dependencies; it ignores time order.
- ✓
Split the data chronologically (e.g., use data before a certain date for training, after for testing)
Why this is correct
Chronological splitting preserves the temporal order, preventing future data from leaking into the training set.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Normalize numerical features using statistics computed on the entire dataset before splitting
Why it's wrong here
Normalizing on the full dataset leaks information from the test set into the training set; normalization should be fit on training data only.
- ✓
Perform feature selection using only the training data, then apply the same features to the test set
Why this is correct
Feature selection should be done within the training set to avoid leakage; applying the same selection to the test set is fine as long as the selection process didn't see the test data.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Many certification questions include familiar terms but test a specific constraint. Read the exact wording before choosing an answer that is generally true but wrong for this case.
Detailed technical explanation
How to think about this question
This question should be treated as a scenario, not a definition check. Identify the problem, the constraint and the best action. Then compare each option against those facts.
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.
- Use explanations to understand the rule behind the answer.
TExam Day Tips
- Underline the problem statement mentally.
- 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 AI0-001 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
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FAQ
Questions learners often ask
What does this AI0-001 question test?
Implementing AI Solutions — This question tests Implementing AI Solutions — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Split the data chronologically (e.g., use data before a certain date for training, after for testing) — To prevent leakage, time-based splitting respects temporal order (no future data in training). Not normalizing before splitting avoids information from the test set influencing training. The other options either cause leakage or are unrelated.
What should I do if I get this AI0-001 question wrong?
Identify which AI0-001 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
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: Jul 4, 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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