Try and practice the latest Databricks : Databricks-Machine-Learning-Associate real questions & answers

A machine learning engineer has identified the best run from an MLflow Experiment. They have stored the run ID in the run_id variable and identified the logged model name as "model". They now want to register that model in the MLflow Model Registry with the name "best_model".
Which lines of code can they use to register the model associated with run_id to the MLflow Model Registry?

• A. millow.register_model(f"runs:/{run_id)/model")
• B. mlflow.register_model(f"runs:/{run_id}/model", "best_model")
• C. mlflow.register_model(f"runs:/{run_id}/best_model", "model")
• D. mlflow.register_model(run_id, "best_model")

A data scientist wants to use Spark ML to impute missing values in their PySpark DataFrame features_df. They want to replace missing values in all numeric columns in features_df with each respective numeric column's median value.
They have developed the following code block to accomplish this task:

The code block is not accomplishing the task.
Which reasons describes why the code block is not accomplishing the imputation task?

• A. The fit method needs to be called instead of transform.
• B. It does not fit the imputer on the data to create an ImputerModel.
• C. The inputCols and outputCols need to be exactly the same.
• D. It does not impute both the training and test data sets.

A data scientist is performing hyperparameter tuning using an iterative optimization algorithm. Each evaluation of unique hyperparameter values is being trained on a single compute node. They are performing eight total evaluations across eight total compute nodes. While the accuracy of the model does vary over the eight evaluations, they notice there is no trend of improvement in the accuracy. The data scientist believes this is due to the parallelization of the tuning process.
Which change could the data scientist make to improve their model accuracy over the course of their tuning process?

• A. Change the number of compute nodes to be half or less than half of the number of evaluations.
• B. Change the iterative optimization algorithm used to facilitate the tuning process.
• C. Change the number of compute nodes to be double or more than double the number of evaluations.
• D. Change the number of compute nodes and the number of evaluations to be much larger but equal.

A data scientist wants to tune a set of hyperparameters for a machine learning model. They have wrapped a Spark ML model in the objective function objective_function and they have defined the search space search_space.
As a result, they have the following code block:

Which of the following changes do they need to make to the above code block in order to accomplish the task?

• A. Reduce num_evals to be less than 10
• B. Change fmin() to fmax()
• C. Remove the algo=tpe.suggest argument
• D. Remove the trials=trials argument
• E. Change SparkTrials() to Trials()

A machine learning engineer is converting a decision tree from sklearn to Spark ML. They notice that they are receiving different results despite all of their data and manually specified hyperparameter values being identical.
Which of the following describes a reason that the single-node sklearn decision tree and the Spark ML decision tree can differ?

• A. Spark ML decision trees automatically prune overfit trees
• B. Spark ML decision trees test a random sample of feature variables in the splitting algorithm
• C. Spark ML decision trees test binned features values as representative split candidates
• D. Spark ML decision trees test more split candidates in the splitting algorithm
• E. Spark ML decision trees test every feature variable in the splitting algorithm