databricks DATABRICKS MACHINE LEARNING PROFESSIONAL Exam Questions
Questions for the DATABRICKS MACHINE LEARNING PROFESSIONAL were updated on : Dec 01 ,2025
Page 1 out of 4. Viewing questions 1-15 out of 60
Question 1
A machine learning engineer has created a webhook with the following code block:
Which of the following code blocks will trigger this webhook to run the associate job?
A)
B)
C)
D)
E)
- A. Option A
- B. Option B
- C. Option C
- D. Option D
Answer:
C
Question 2
A machine learning engineer wants to view all of the active MLflow Model Registry Webhooks for a
specific model.
They are using the following code block:
Which of the following changes does the machine learning engineer need to make to this code block
so it will successfully accomplish the task?
- A. There are no necessary changes
- B. Replace list with view in the endpoint URL
- C. Replace POST with GET in the call to http request
- D. Replace list with webhooks in the endpoint URL
- E. Replace POST with PUT in the call to http request
Answer:
D
Question 3
A machine learning engineer wants to programmatically create a new Databricks Job whose schedule
depends on the result of some automated tests in a machine learning pipeline.
Which of the following Databricks tools can be used to programmatically create the Job?
- A. MLflow APIs
- B. AutoML APIs
- C. MLflow Client
- D. Jobs cannot be created programmatically
- E. Databricks REST APIs
Answer:
E
Question 4
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
Answer:
E
Question 5
A machine learning engineer is attempting to create a webhook that will trigger a Databricks Job
job_id when a model version for model model transitions into any MLflow Model Registry stage.
They have the following incomplete code block:
Which of the following lines of code can be used to fill in the blank so that the code block
accomplishes the task?
- A. "MODEL_VERSION_CREATED"
- B. "MODEL_VERSION_TRANSITIONED_TO_PRODUCTION"
- C. "MODEL_VERSION_TRANSITIONED_TO_STAGING"
- D. "MODEL_VERSION_TRANSITIONED_STAGE" E. "MODEL_VERSION_TRANSITIONED_TO_STAGING", "MODEL_VERSION_TRANSITIONED_TO_PRODUCTION"
Answer:
C
Question 6
Which of the following Databricks-managed MLflow capabilities is a centralized model store?
- A. Models
- B. Model Registry
- C. Model Serving
- D. Feature Store
- E. Experiments
Answer:
C
Question 7
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
Answer:
C
Question 8
A machine learning engineering manager has asked all of the engineers on their team to add text
descriptions to each of the model projects in the MLflow Model Registry. They are starting with the
model project "model" and they'd like to add the text in the model_description variable.
The team is using the following line of code:
Which of the following changes does the team need to make to the above code block to accomplish
the task?
- A. Replace update_registered_model with update_model_version
- B. There no changes necessary
- C. Replace client.update_registered_model with mlflow
- D. Add a Python model as an argument to update_registered_model
Answer:
B
Question 9
A machine learning engineer wants to log and deploy a model as an MLflow pyfunc model. They
have custom preprocessing that needs to be completed on feature variables prior to fitting the model
or computing predictions using that model. They decide to wrap this preprocessing in a custom
model class ModelWithPreprocess, where the preprocessing is performed when calling fit and when
calling predict. They then log the fitted model of the ModelWithPreprocess class as a pyfunc model.
Which of the following is a benefit of this approach when loading the logged pyfunc model for
downstream deployment?
- A. The pvfunc model can be used to deploy models in a parallelizable fashion
- B. The same preprocessing logic will automatically be applied when calling fit
- C. The same preprocessing logic will automatically be applied when calling predict
- D. This approach has no impact when loading the logged Pvfunc model for downstream deployment
- E. There is no longer a need for pipeline-like machine learning objects
Answer:
E
Question 10
Which of the following MLflow Model Registry use cases requires the use of an HTTP Webhook?
- A. Starting a testing job when a new model is registered
- B. Updating data in a source table for a Databricks SQL dashboard when a model version transitions to the Production stage
- C. Sending an email alert when an automated testing Job fails
- D. None of these use cases require the use of an HTTP Webhook
- E. Sending a message to a Slack channel when a model version transitions stages
Answer:
B
Question 11
Which of the following lists all of the model stages are available in the MLflow Model Registry?
- A. Development. Staging. Production
- B. None. Staging. Production
- C. Staging. Production. Archived
- D. None. Staging. Production. Archived
- E. Development. Staging. Production. Archived
Answer:
A
Question 12
Which of the following is an advantage of using the python_function(pyfunc) model flavor over the
built-in library-specific model flavors?
- A. python_function provides no benefits over the built-in library-specific model flavors
- B. python_function can be used to deploy models in a parallelizable fashion
- C. python_function can be used to deploy models without worrying about which library was used to create the model
- D. python_function can be used to store models in an MLmodel file
- E. python_function can be used to deploy models without worrying about whether they are deployed in batch, streaming, or real-time environments
Answer:
B
Question 13
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. mlflow.register_model
- B. MlflowClient.update_registered_model
- C. mlflow.add_model_version
- D. MlflowClient.get_model_version
- E. The machine learning engineer needs to create an entirely new MLflow Model Registry model
Answer:
B
Question 14
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. Option A
- B. Option B
- C. Option C
- D. Option D
- E. option E
Answer:
A
Question 15
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. mlflow.register_model(model_uri, "best_model")
- B. mlflow.register_model(run_id, "best_model")
- C. mlflow.register_model(f"runs:/{run_id}/best_model", "model")
- D. mlflow.register_model(model_uri, "model")
- E. mlflow.register_model(f"runs:/{run_id}/model")
Answer:
D