Free DATABRICKS-MACHINE-LEARNING-PROFESSIONAL Exam Dumps Preview

Which of the following Databricks-managed MLflow capabilities is a centralized model store?

A machine learning engineering team has written predictions computed in a batch job to a Delta table for querying. However, the team has noticed that the querying is running slowly. The team has already tuned the size of the data files. Upon investigating, the team has concluded that the rows meeting the query condition are sparsely located throughout each of the data files.
Based on the scenario, which of the following optimization techniques could speed up the query by colocating similar records while considering values in multiple columns?

After a data scientist noticed that a column was missing from a production feature set stored as a Delta table, the machine learning engineering team has been tasked with determining when the column was dropped from the feature set.

Which of the following SQL commands can be used to accomplish this task?

Which of the following machine learning model deployment paradigms is the most common for machine learning projects?

A machine learning engineer wants to move their model version model_version for the MLflow Model Registry model model from the Staging stage to the Production stage using MLflow Client client.

Which of the following code blocks can they use to accomplish the task?

A data scientist has computed updated feature values for all primary key values stored in the Feature Store table features. In addition, feature values for some new primary key values have also been computed. The updated feature values are stored in the DataFrame features_df. They want to replace all data in features with the newly computed data.

Which of the following code blocks can they use to perform this task using the Feature Store Client fs?

A data scientist wants to remove the star_rating column from the Delta table at the location path. To do this, they need to load in data and drop the star_rating column.

Which of the following code blocks accomplishes this task?

Which of the following MLflow Model Registry use cases requires the use of an HTTP Webhook?

A machine learning engineer has developed a random forest model using scikit-learn, logged the model using MLflow as random_forest_model, and stored its run ID in the run_id Python variable. They now want to deploy that model by performing batch inference on a Spark DataFrame spark_df.

Which of the following code blocks can they use to create a function called predict that they can use to complete the task?