Learn with Databricks : Databricks-Machine-Learning-Associate training material for 100% pass

A data scientist is developing a single-node machine learning model. They have a large number of model configurations to test as a part of their experiment. As a result, the model tuning process takes too long to complete. Which of the following approaches can be used to speed up the model tuning process?

• A. Parallelize with Hyperopt
• B. Implement MLflow Experiment Tracking
• C. Enable autoscaling clusters
• D. Scale up with Spark ML

A machine learning engineer has created a Feature Table new_table using Feature Store Client fs. When creating the table, they specified a metadata description with key information about the Feature Table. They now want to retrieve that metadata programmatically.
Which of the following lines of code will return the metadata description?

• A. fs.create_training_set("new_table")
• B. fs.get_table("new_table")
• C. There is no way to return the metadata description programmatically.
• D. fs.get_table("new_table").load_df()
• E. fs.get_table("new_table").description

Which of the following describes the relationship between native Spark DataFrames and pandas API on Spark DataFrames?

• A. pandas API on Spark DataFrames are more performant than Spark DataFrames
• B. pandas API on Spark DataFrames are less mutable versions of Spark DataFrames
• C. pandas API on Spark DataFrames are single-node versions of Spark DataFrames with additional metadata
• D. pandas API on Spark DataFrames are unrelated to Spark DataFrames
• E. pandas API on Spark DataFrames are made up of Spark DataFrames and additional metadata

The implementation of linear regression in Spark ML first attempts to solve the linear regression problem using matrix decomposition, but this method does not scale well to large datasets with a large number of variables.
Which of the following approaches does Spark ML use to distribute the training of a linear regression model for large data?

• A. Iterative optimization
• B. Spark ML cannot distribute linear regression training
• C. Logistic regression
• D. Singular value decomposition
• E. Least-squares method

A data scientist wants to parallelize the training of trees in a gradient boosted tree to speed up the training process. A colleague suggests that parallelizing a boosted tree algorithm can be difficult.
Which of the following describes why?

• A. Gradient boosting is an iterative algorithm that requires information from the previous iteration to perform the next step.
• B. Gradient boosting is not a linear algebra-based algorithm which is required for parallelization
• C. Gradient boosting calculates gradients in evaluation metrics using all cores which prevents parallelization.
• D. Gradient boosting requires access to all data at once which cannot happen during parallelization.