Databricks Databricks Machine Learning Professional Practice Exams
Last updated on Oct 21,2026- Exam Code: Databricks Machine Learning Professional
- Exam Name: Databricks Certified Machine Learning Professional
- Certification Provider: Databricks
- Latest update: Oct 21,2026
A machine learning engineer has registered a sklearn model in the MLflow Model Registry using the sklearn model flavor with UI model_uri.
Which of the following operations can be used to load the model as an sklearn object for batch deployment?
- A . mlflow.spark.load_model(model_uri)
- B . mlflow.pyfunc.read_model(model_uri)
- C . mlflow.sklearn.read_model(model_uri)
- D . mlflow.pyfunc.load_model(model_uri)
- E . mlflow.sklearn.load_model(model_uri)
A data scientist has developed a model to predict ice cream sales using the expected temperature and expected number of hours of sun in the day. However, the expected temperature is dropping beneath the range of the input variable on which the model was trained.
Which of the following types of drift is present in the above scenario?
- A . Label drift
- B . None of these
- C . Concept drift
- D . Prediction drift
- E . Feature drift
Which of the following MLflow operations can be used to automatically calculate and log a Shapley feature importance plot?
- A . mlflow.shap.log_explanation
- B . None of these operations can accomplish the task.
- C . mlflow.shap
- D . mlflow.log_figure
- E . client.log_artifact
Which of the following describes the concept of MLflow Model flavors?
- A . A convention that deployment tools can use to wrap preprocessing logic into a Model
- B . A convention that MLflow Model Registry can use to version models
- C . A convention that MLflow Experiments can use to organize their Runs by project
- D . A convention that deployment tools can use to understand the model
- E . A convention that MLflow Model Registry can use to organize its Models by project
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. At the same time, they would like to archive any model versions that are already in the Production stage.
Which of the following code blocks can they use to accomplish the task?
A)
B)
C)
D)
- A . Option A
- B . Option B
- C . Option C
- D . Option D
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 . Staging. Production. Archived
- B . Production
- C . None. Staging. Production. Archived
- D . Staging. Production
- E . [None. Staging. Production
Which of the following operations in Feature Store Client fs can be used to return a Spark DataFrame of a data set associated with a Feature Store table?
- A . fs.create_table
- B . fs.write_table
- C . fs.get_table
- D . There is no way to accomplish this task with fs
- E . fs.read_table
Which of the following deployment paradigms can centrally compute predictions for a single record with exceedingly fast results?
- A . Streaming
- B . Batch
- C . Edge/on-device
- D . None of these strategies will accomplish the task.
- E . Real-time
A data scientist would like to enable MLflow Autologging for all machine learning libraries used in a notebook. They want to ensure that MLflow Autologging is used no matter what version of the Databricks Runtime for Machine Learning is used to run the notebook and no matter what workspace-wide configurations are selected in the Admin Console.
Which of the following lines of code can they use to accomplish this task?
- A . mlflow.sklearn.autolog()
- B . mlflow.spark.autolog()
- C . spark.conf.set(“autologging”, True)
- D . It is not possible to automatically log MLflow runs.
- E . mlflow.autolog()
Which of the following describes label drift?
- A . Label drift is when there is a change in the distribution of the predicted target given by the model
- B . None of these describe label drift
- C . Label drift is when there is a change in the distribution of an input variable
- D . Label drift is when there is a change in the relationship between input variables and target variables
- E . Label drift is when there is a change in the distribution of a target variable