AI Life Cycle (Model Selection, Training, and Validation) AAISM Practice Questions (Page 5)

Questions 21–25

1. 21foundation · easy

   What is the primary purpose of having a separate test set in data splitting?

   • ATo increase the amount of training data
   • BTo provide an unbiased final evaluation of the model's performance
   • CTo tune hyperparameters during training
   • DTo validate the model during each training iteration
   Reveal Solution Discussion Select an answer first
2. 22application · medium

   A team is comparing two models for a binary classification task. Model A has high bias and low variance. Model B has low bias and high variance. The team needs to choose a model that will generalize well to unseen data. Which approach should they take?

   • ASelect Model A because it is more stable and less likely to overfit.
   • BSelect Model B because it can capture complex patterns, even if it overfits.
   • CUse ensemble methods to combine models and reduce variance while maintaining low bias.
   • DChoose the model with the higher training accuracy.
   Reveal Solution Discussion Select an answer first
4. 23foundation · easy

   During the model training process, what is the primary purpose of hyperparameter tuning?

   • ATo reduce the size of the training dataset to speed up training
   • BTo find the optimal set of external configuration settings that control the training process
   • CTo adjust the model's architecture to improve performance on the training data
   • DTo automatically label the training data
   Reveal Solution Discussion Select an answer first
5. 24foundation · easy

   What is the primary advantage of using k-fold cross-validation over a single holdout validation set?

   • AIt is faster to compute than a single holdout split
   • BIt guarantees the model will not overfit
   • CIt reduces the need for a separate test set
   • DIt uses the data more efficiently by training and validating on multiple splits
   Reveal Solution Discussion Select an answer first
6. 25foundation · easy

   What does the AUC-ROC metric measure?

   • AThe accuracy of the model at a specific threshold
   • BThe precision of the model on the positive class
   • CThe recall of the model on the negative class
   • DThe model's ability to distinguish between positive and negative classes across all classification thresholds
   Reveal Solution Discussion Select an answer first