Train Models Using Snowpark ML SNOWPRO-ADVANCED-DATA-SCIENTIST Practice Questions (Page 3)

Questions 11–14

1. 11foundation · easy

   What is the primary benefit of using Snowpark ML's modeling module for model training?

   • AIt converts all models to SQL functions for deployment.
   • BIt eliminates the need to write any SQL code.
   • CIt automatically tunes hyperparameters for every model.
   • DIt enables training models without moving data out of Snowflake.
   Reveal Solution Discussion Select an answer first
2. 12application · easy

   A data science team is using Snowpark ML to train a model. They have a Snowpark DataFrame `train_df` and want to use `GridSearchCV` to tune hyperparameters. They want to evaluate the model using a specific metric, `f1_score`. Which parameter should they set in `GridSearchCV`?

   • ASet `scoring='f1'`.
   • BSet `eval_metric='f1'`.
   • CSet `score='f1'`.
   • DSet `metric='f1'`.
   Reveal Solution Discussion Select an answer first
4. 13application · easy

   A data science team has trained a Snowpark ML model and saved it to a stage. They now want to load it and use it to make predictions on a Snowpark DataFrame `new_data_df`. Which method should they call on the loaded model?

   • A`model.evaluate(new_data_df)`
   • B`model.predict(new_data_df)`
   • C`model.transform(new_data_df)`
   • D`model.fit(new_data_df)`
   Reveal Solution Discussion Select an answer first
5. 14foundation · easy

   In a typical Snowpark ML model training workflow, which sequence of steps is correct?

   • AEvaluate model, load data, train model, split data
   • BSplit data, train model, load data, evaluate model
   • CTrain model, load data, evaluate model, split data
   • DLoad data, split data, train model, evaluate model
   Reveal Solution Discussion Select an answer first