Databricks Databricks Machine Learning Professional Practice Exams
Last updated on Oct 22,2026- Exam Code: Databricks Machine Learning Professional
- Exam Name: Databricks Certified Machine Learning Professional
- Certification Provider: Databricks
- Latest update: Oct 22,2026
A machine learning engineer and data scientist are working together to convert a batch deployment to an always-on streaming deployment. The machine learning engineer has expressed that rigorous data tests must be put in place as a part of their conversion to account for potential changes in data formats.
Which of the following describes why these types of data type tests and checks are particularly important for streaming deployments?
- A . Because the streaming deployment is always on, all types of data must be handled without producing an error
- B . All of these statements
- C . Because the streaming deployment is always on, there is no practitioner to debug poor model performance
- D . Because the streaming deployment is always on, there is a need to confirm that the deployment can autoscale
- E . None of these statements
Which of the following MLflow operations can be used to delete a model from the MLflow Model Registry?
- A . client.transition_model_version_stage
- B . client.delete_model_version
- C . client.update_registered_model
- D . client.delete_model
- E . client.delete_registered_model
Which of the following MLflow operations can be used to delete a model from the MLflow Model Registry?
- A . client.transition_model_version_stage
- B . client.delete_model_version
- C . client.update_registered_model
- D . client.delete_model
- E . client.delete_registered_model
Which of the following describes the purpose of the context parameter in the predict method of Python models for MLflow?
- A . The context parameter allows the user to specify which version of the registered MLflow Model should be used based on the given application’s current scenario
- B . The context parameter allows the user to document the performance of a model after it has been deployed
- C . The context parameter allows the user to include relevant details of the business case to allow downstream users to understand the purpose of the model
- D . The context parameter allows the user to provide the model with completely custom if-else logic for the given application’s current scenario
- E . The context parameter allows the user to provide the model access to objects like preprocessing models or custom configuration files
Which of the following tools can assist in real-time deployments by packaging software with its own application, tools, and libraries?
- A . Cloud-based compute
- B . None of these tools
- C . REST APIs
- D . Containers
- E . Autoscaling clusters