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AI Associate AI Fundamentals Practice Question

Exhibit

Custom Model Configuration:
- Name: LeadScorer_v2
- Algorithm: XGBoost
- Training Data: 100,000 records (80% won, 20% lost)
- Evaluation Metric: Accuracy

Refer to the exhibit. A data scientist built a model using training data where 80% of leads were won. The model achieved 80% accuracy. What is the main issue with this evaluation?

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

Accuracy is not a reliable metric because the data is imbalanced

Correct: Accuracy is misleading due to class imbalance; a model that always predicts 'won' would get 80% accuracy. Option A: Data size is fine. Option B: XGBoost is good for tabular data. Option D: Confidence score not provided.

Answer analysis

Option-by-option breakdown

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

  • The model lacks confidence scoring

    Why it's wrong here

    While confidence is important, the immediate issue is the misleading accuracy metric.

  • The algorithm choice (XGBoost) is inappropriate

    Why it's wrong here

    XGBoost is commonly used for classification tasks like lead scoring.

  • Accuracy is not a reliable metric because the data is imbalanced

    Why this is correct

    With 80% won leads, a constant 'won' prediction yields 80% accuracy, so accuracy does not measure model's discriminative power.

  • The training data size is insufficient

    Why it's wrong here

    100,000 records is generally sufficient for a lead scoring model.

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Written by Johnson Ajibi, MSc IT Security

Senior Network & Security Engineer · founder of Courseiva

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