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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
A machine learning engineering team has written predictions computed in a batch job to a Delta table for querying. However, the team has noticed that the querying is running slowly. The team has already tuned the size of the data files. Upon investigating, the team has concluded that the rows meeting the query condition are sparsely located throughout each of the data files. Based on the scenario, which of the following optimization techniques could speed up the query by colocating similar records while considering values in multiple columns?
Options
A Tuning the file size
B Bin-packing
C Z-Ordering
D Write as a Parquet file
E Data skipping
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2
Question
A data scientist has written a function to track the runs of their random forest model. The data scientist is changing the number of trees in the forest across each run.

Which of the following MLflow operations is designed to log single values like the number of trees in a random forest?
Options
A There is no way to store values like this.
B mlflow.log_model
C mlflow.log_artifact
D mlflow.log_metric
E mlflow.log_param
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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
A machine learning engineer has registered a sklearn model in the MLflow Model Registry using the sklearn model flavor with UI model_uri.

Which of the following operations can be used to load the model as an sklearn object for batch deployment?
Options
A mlflow.sklearn.load_model(model_uri)
B mlflow.pyfunc.read_model(model_uri)
C mlflow.spark.load_model(model_uri)
D mlflow.sklearn.read_model(model_uri)
E mlflow.pyfunc.load_model(model_uri)
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5
Question
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?
Options
A TIMESTAMP
B DESCRIBE
C VERSION
D HISTORY
E DESCRIBE HISTORY
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