Databricks Certified Machine Learning Professional (DATABRICKS-MACHINE-LEARNING-PROFESSIONAL) - DataBricks Actual Exam Questions
Last updated on May 13, 2026
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?
Indent the child run blocks within the parent run block
Add the nested=True argument to the parent run
Remove the nested=True argument from the child runs
Provide the same name to the run name parameter for all three run blocks
Add the nested=True argument to the parent run and remove the nested=True arguments from the child runs
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After a data scientist noticed that a column was missing from a production feature set stored as a Delta table, the machine learning engineering team has been tasked with determining when the column was dropped from the feature set. Which of the following SQL commands can be used to accomplish this task?
VERSION
DESCRIBE
HISTORY
DESCRIBE HISTORY
TIMESTAMP
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A data scientist set up a machine learning pipeline to automatically log a data visualization with each run. They now want to view the visualizations in Databricks. Which of the following locations in Databricks will show these data visualizations?
The MLflow Model Registry Model paqe
The Artifacts section of the MLflow Experiment page
Logged data visualizations cannot be viewed in Databricks
The Artifacts section of the MLflow Run page
The Figures section of the MLflow Run page
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Which of the following is a reason for using Jensen-Shannon (JS) distance over a Kolmogorov- Smirnov (KS) test for numeric feature drift detection?
All of these reasons
JS is not normalized or smoothed
None of these reasons
JS is more robust when working with large datasets
JS does not require any manual threshold or cutoff determinations
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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 of the following code blocks accomplishes this task?
spark.read.format(“delta”).load(path).drop(“star_rating”)
spark.read.format(“delta”).table(path).drop(“star_rating”)
Delta tables cannot be modified
spark.read.table(path).drop(“star_rating”)
spark.sql(“SELECT * EXCEPT star_rating FROM path”)
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