Tune Model Hyperparameters SNOWPRO-ADVANCED-DATA-SCIENTIST Practice Questions (Page 4)

Questions 16–20

1. 16foundation · easy

   During hyperparameter tuning, if the model performs very well on the training data but poorly on the validation data, what is the most likely diagnosis?

   • AThe learning rate is too low
   • BOverfitting
   • CUnderfitting
   • DThe dataset is too small
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2. 17application · easy

   A data scientist is using scikit-learn's GridSearchCV to tune hyperparameters for a logistic regression model. They want to parallelize the search across multiple CPU cores to speed up the process. Which parameter should they set?

   • Arefit=True
   • Bn_jobs=-1
   • Cverbose=2
   • Dcv=10
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4. 18foundation · easy

   What is the defining characteristic of grid search for hyperparameter tuning?

   • AIt uses past evaluation results to intelligently choose the next hyperparameter set
   • BIt starts with a coarse grid and narrows down iteratively based on performance
   • CIt evaluates all possible combinations of a predefined set of hyperparameter values
   • DIt randomly samples hyperparameter values from a continuous distribution
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5. 19application · medium

   During hyperparameter tuning of a neural network, a data scientist observes that both training and validation loss are high and decrease slowly. What is the likely issue and what should they adjust?

   • AUnderfitting; increase the learning rate or model capacity
   • BOverfitting; increase the learning rate
   • COverfitting; decrease the learning rate
   • DUnderfitting; decrease the learning rate
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6. 20application · medium

   A data scientist is using Optuna to tune a model. They want to stop trials early if they are not performing well to save computational resources. Which Optuna feature should they use?

   • APruning (e.g., MedianPruner)
   • BRandomizedSearchCV
   • CGridSearchCV
   • DEarlyStopping callback
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