Databricks Certified Machine Learning Professional Databricks Machine Learning Professional Exam Questions

A machine learning engineer wants to deploy a model for real-time serving using MLflow Model Serving. For the model, the machine learning engineer currently has one model version in each of the stages in the MLflow Model Registry. The engineer wants to know which model versions can be queried once Model Serving is enabled for the model.

Which of the following lists all of the MLflow Model Registry stages whose model versions are automatically deployed with Model Serving?

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 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?

A)

B)

It is not possible to deploy a scikit-learn model on a Spark DataFrame.

C)

D)

E)

Which of the following describes the purpose of the context parameter in the predict method of Python models for MLflow?

Which of the following is an advantage of using the python_function(pyfunc) model flavor over the built-in library-specific model flavors?

A machine learning engineer needs to deliver predictions of a machine learning model in real-time. However, the feature values needed for computing the predictions are available one week before the query time.

Which of the following is a benefit of using a batch serving deployment in this scenario rather than a real-time serving deployment where predictions are computed at query time?

A data scientist is utilizing MLflow to track their machine learning experiments. After completing a series of runs for the experiment with experiment ID exp_id, the data scientist wants to programmatically work with the experiment run data in a Spark DataFrame. They have an active MLflow Client client and an active Spark session spark.

Which of the following lines of code can be used to obtain run-level results for exp_id in a Spark DataFrame?