Databricks Certified Machine Learning Professional Exam Practice Test

A machine learning engineer is manually refreshing a model in an existing machine learning pipeline. The pipeline uses the MLflow Model Registry model "project". The machine learning engineer would like to add a new version of the model to "project".

Which of the following MLflow operations can the machine learning engineer use to accomplish this task?

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)

B)

C)

D)

E)

A machine learning engineer is migrating a machine learning pipeline to use Databricks Machine Learning. They have programmatically identified the best run from an MLflow Experiment and stored its URI in the model_uri variable and its Run ID in the run_id variable. They have also determined that the model was logged with the name "model". Now, the machine learning engineer wants to register that model in the MLflow Model Registry with the name "best_model".

Which of the following lines of code can they use to register the model to the MLflow Model Registry?

A machine learning engineer is using the following code block as part of a batch deployment pipeline:

Which of the following changes needs to be made so this code block will work when the inference table is a stream source?

A machine learning engineer needs to select a deployment strategy for a new machine learning application. The feature values are not available until the time of delivery, and results are needed exceedingly fast for one record at a time.

Which of the following deployment strategies can be used to meet these requirements?

A data scientist has developed a scikit-learn random forest model model, but they have not yet logged model with MLflow. They want to obtain the input schema and the output schema of the model so they can document what type of data is expected as input.

Which of the following MLflow operations can be used to perform this task?

Which of the following MLflow operations can be used to automatically calculate and log a Shapley feature importance plot?