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Implement natural language processing solutionshardMultiple ChoiceObjective-mapped

AI-102 Practice Question: Implement natural language processing solutions

A financial services company uses Azure AI Language's custom text classification to categorize loan applications as 'Approved', 'Denied', or 'Review Required'. The model is trained on historical data but is producing poor accuracy on new applications. The data scientist suspects data leakage between training and test sets. What should the data scientist do to validate this?

⚠ Common exam trap

Watch out — candidates often confuse data leakage with model performance issues and choose to increase data or adjust thresholds, not realizing that the core problem is the integrity of the train-test split, which must be validated through chronological separation.

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 and ensure no overlapping data between train and test sets.

Data leakage occurs when information from outside the training set inadvertently influences the model, often due to overlapping or non-independent data splits. By splitting the data chronologically (e.g., training on older applications and testing on newer ones), the data scientist ensures that no future information leaks into the training process, which directly validates whether temporal leakage is causing poor accuracy. This approach is standard for time-series or sequential data like loan applications, where patterns may shift over time.

Answer analysis

Option-by-option breakdown

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

  • Increase the training dataset size and retrain the model.

    Why it's wrong here

    More data does not address data leakage.

  • Use k-fold cross-validation during training.

    Why it's wrong here

    Cross-validation may still have leakage if splitting is not careful.

  • Adjust the classification confidence threshold.

    Why it's wrong here

    Threshold adjustment does not fix data leakage.

  • Split the data chronologically and ensure no overlapping data between train and test sets.

    Why this is correct

    Chronological split prevents future data from leaking into training.

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JA

Written by Johnson Ajibi, MSc IT Security

Senior Network & Security Engineer · founder of Courseiva

This AI-102 practice question is part of Courseiva's free Microsoft 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 AI-102 exam.