Certified Machine Learning Associate v1.0

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 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 data scientist is using MLflow to track their machine learning experiment. As a part of each of their MLflow runs, they are performing hyperparameter tuning. The data scientist would like to have one parent run for the tuning process with a child run for each unique combination of hyperparameter values. All parent and child runs are being manually started with mlflow.start_run.
Which of the following approaches can the data scientist use to accomplish this MLflow run organization?

Which of the following approaches can be used to view the notebook that was run to create an MLflow run?

A data scientist is developing a machine learning pipeline using AutoML on Databricks Machine Learning.
Which of the following steps will the data scientist need to perform outside of their AutoML experiment?

A machine learning engineer has grown tired of needing to install the MLflow Python library on each of their clusters. They ask a senior machine learning engineer how their notebooks can load the MLflow library without installing it each time. The senior machine learning engineer suggests that they use Databricks Runtime for Machine Learning.
Which of the following approaches describes how the machine learning engineer can begin using Databricks Runtime for Machine Learning?

A data scientist is utilizing MLflow Autologging to automatically track their machine learning experiments. After completing a series of runs for the experiment experiment_id, the data scientist wants to identify the run_id of the run with the best root-mean-square error (RMSE).
Which of the following lines of code can be used to identify the run_id of the run with the best RMSE in experiment_id?

A machine learning engineer has been notified that a new Staging version of a model registered to the MLflow Model Registry has passed all tests. As a result, the machine learning engineer wants to put this model into production by transitioning it to the Production stage in the Model Registry.
From which of the following pages in Databricks Machine Learning can the machine learning engineer accomplish this task?

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 new data scientist has started working on an existing machine learning project. The project is a scheduled Job that retrains every day. The project currently exists in a Repo in Databricks. The data scientist has been tasked with improving the feature engineering of the pipeline’s preprocessing stage. The data scientist wants to make necessary updates to the code that can be easily adopted into the project without changing what is being run each day.
Which approach should the data scientist take to complete this task?

A machine learning engineering team has a Job with three successive tasks. Each task runs a single notebook. The team has been alerted that the Job has failed in its latest run.
Which of the following approaches can the team use to identify which task is the cause of the failure?

A data scientist is using Spark SQL to import their data into a machine learning pipeline. Once the data is imported, the data scientist performs machine learning tasks using Spark ML.
Which of the following compute tools is best suited for this use case?

A machine learning engineer is trying to perform batch model inference. They want to get predictions using the linear regression model saved at the path model_uri for the DataFrame batch_df. batch_df has the following schema: customer_id STRING
The machine learning engineer runs the following code block to perform inference on batch_df using the linear regression model at model_uri:

In which situation will the machine learning engineer’s code block perform the desired inference?

Which of the following evaluation metrics is not suitable to evaluate runs in AutoML experiments for regression problems?