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Databricks Certified Machine Learning Professional (Databricks Machine Learning Professional) - Databricks Exam Questions

Last updated on June 20, 2026

97% Exam Compliance
60 Total Questions
1
Question
Which of the following deployment paradigms can centrally compute predictions for a single record with exceedingly fast results?
Options
A Real-time
B Batch
C Streaming
D Edge/on-device
E None of these strategies will accomplish the task.
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2
Question
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?
Options
A None of these statements
B All of these statements
C Because the streaming deployment is always on, all types of data must be handled without producing an error
D Because the streaming deployment is always on, there is no practitioner to debug poor model performance
E Because the streaming deployment is always on, there is a need to confirm that the deployment can autoscale
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3
Question
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

Exhibit

It is not possible to deploy a scikit-learn model on a Spark DataFrame. to complete the task?

Exhibit

Exhibit

A) B)
C) D)
E)
Options
A Option E
B Option B
C Option A
D Option C
E Option D
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4
Question
In a continuous integration, continuous deployment (CI/CD) process for machine learning pipelines,

which of the following events commonly triggers the execution of automated testing?
Options
A The arrival of a new model version in the MLflow Model Registry
B CI/CD pipelines are not needed for machine learning pipelines
C The launch of a new cost-efficient SQL endpoint
D The arrival of a new feature table in the Feature Store
E The launch of a new cost-efficient job cluster
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5
Question
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?
Options
A The machine learning engineer needs to create an entirely new MLflow Model Registry model
B MlflowClient.update_registered_model
C mlflow.register_model
D mlflow.add_model_version
E MlflowClient.get_model_version
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