AI Model Training, Testing, and Validation AAIR Practice Questions (Page 5)

Questions 21–25

1. 21foundation · easy

   In the bias-variance trade-off, what does increasing model complexity typically do?

   • ADecreases both bias and variance
   • BIncreases both bias and variance
   • CDecreases bias and increases variance
   • DIncreases bias and decreases variance
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2. 22foundation · easy

   What is the primary goal of robustness testing in AI model testing?

   • ATo compare the model's performance with human experts
   • BTo ensure the model performs well under varied and unexpected input conditions
   • CTo measure the model's accuracy on the training set
   • DTo verify that the model's training data is free from errors
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4. 23foundation · easy

   What is the main purpose of k-fold cross-validation?

   • ATo increase the size of the training dataset by generating synthetic samples
   • BTo eliminate the need for a separate test set
   • CTo reduce the variance of the model's predictions
   • DTo assess model stability and performance by training and evaluating on multiple data subsets
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5. 24foundation · easy

   What is the primary characteristic of an overfitted model?

   • AIt performs equally well on training and test data
   • BIt performs well on training data but poorly on unseen data
   • CIt has a high bias and low variance
   • DIt performs poorly on both training and test data
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6. 25application · medium

   A financial institution is validating a loan-approval model. The business requirement is to approve at least 60% of applicants while keeping the default rate below 5%. The model currently approves 55% of applicants with a 3% default rate. Which validation approach best confirms the model meets the business requirement?

   • APerform cross-validation to ensure the model is stable across data subsets
   • BEvaluate the model's approval rate and default rate on the test set against the business thresholds
   • CReview the model's documentation to confirm the training process was followed
   • DCheck the model's accuracy on the test set to ensure it is above 90%
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