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Databricks Machine Learning Professional Exam Questions
- Topic 1: Identify the requirements for tracking nested runs/ Describe an MLflow flavor and the benefits of using MLflow flavors
- Topic 2: Test whether the updated model performs better on the more recent data/ Identify when retraining and deploying an updated model is a probable solution to drift
- Topic 3: Create, overwrite, merge, and read Feature Store tables in machine learning workflows / View Delta table history and load a previous version of a Delta table
- Topic 4: Identify a use case for HTTP webhooks and where the Webhook URL needs to come/ Identify advantages of using Job clusters over all-purpose clusters
- Topic 5: Describe the advantages of using the pyfunc MLflow flavor/ Manually log parameters, models, and evaluation metrics using MLflow
- Topic 6: Identify live serving benefits of querying precomputed batch predictions/ Describe Structured Streaming as a common processing tool for ETL pipelines
- Topic 7: Describe concept drift and its impact on model efficacy/ Describe summary statistic monitoring as a simple solution for numeric feature drift
- Topic 8: Identify which code block will trigger a shown webhook/ Describe the basic purpose and user interactions with Model Registry
- Topic 9: Identify less performant data storage as a solution for other use cases/ Describe why complex business logic must be handled in streaming deployments
- Topic 10: Identify that data can arrive out-of-order with structured streaming/ Identify how model serving uses one all-purpose cluster for a model deployment
- Topic 11: Identify JIT feature values as a need for real-time deployment/ Describe how to list all webhooks and how to delete a webhook
- Topic 12: Describe model serving deploys and endpoint for every stage/ Identify scenarios in which feature drift and/or label drift are likely to occur
Free Databricks Databricks Machine Learning Professional Exam Actual Questions
Note: Premium Questions for Databricks Machine Learning Professional were last updated On Jul. 28, 2026 (see below)
Which of the following machine learning model deployment paradigms is the most common for machine learning projects?
Which of the following is a reason for using Jensen-Shannon (JS) distance over a Kolmogorov-Smirnov (KS) test for numeric feature drift detection?
A data scientist wants to remove the star_rating column from the Delta table at the location path. To do this, they need to load in data and drop the star_rating column.
Which option best code blocks accomplishes this task?
A data scientist is using MLflow to track their machine learning experiment. As a part of each MLflow run, they are performing hyperparameter tuning. The data scientist would like to have one parent run for the tuning process with a child run for each unique combination of hyperparameter values.
They are using the following code block:
The code block is not nesting the runs in MLflow as they expected.
Which of the following changes does the data scientist need to make to the above code block so that it successfully nests the child runs under the parent run in MLflow?
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