Databricks Databricks Machine Learning Professional Practice Exams
Last updated on Oct 21,2026- Exam Code: Databricks Machine Learning Professional
- Exam Name: Databricks Certified Machine Learning Professional
- Certification Provider: Databricks
- Latest update: Oct 21,2026
A machine learning engineering team wants to build a continuous pipeline for data preparation of a machine learning application. The team would like the data to be fully processed and made ready for inference in a series of equal-sized batches.
Which of the following tools can be used to provide this type of continuous processing?
- A . Spark UDFs
- B . [Structured Streaming
- C . MLflow
D Delta Lake - D . AutoML
Which of the following is a simple, low-cost method of monitoring numeric feature drift?
- A . Jensen-Shannon test
- B . Summary statistics trends
- C . Chi-squared test
- D . None of these can be used to monitor feature drift
- E . Kolmogorov-Smirnov (KS) test
Which of the following is a simple, low-cost method of monitoring numeric feature drift?
- A . Jensen-Shannon test
- B . Summary statistics trends
- C . Chi-squared test
- D . None of these can be used to monitor feature drift
- E . Kolmogorov-Smirnov (KS) test
Which of the following tools can the machine learning engineer use to assess their theory?
- A . Kolmogorov-Smirnov (KS) test
- B . One-way Chi-squared Test
- C . Two-way Chi-squared Test
- D . Jenson-Shannon distance
- E . None of these
Which of the following is a probable response to identifying drift in a machine learning application?
- A . None of these responses
- B . Retraining and deploying a model on more recent data
- C . All of these responses
- D . Rebuilding the machine learning application with a new label variable
- E . Sunsetting the machine learning application
A data scientist has developed a model model and computed the RMSE of the model on the test set. They have assigned this value to the variable rmse. They now want to manually store the RMSE value with the MLflow run.
They write the following incomplete code block:
Which of the following lines of code can be used to fill in the blank so the code block can successfully complete the task?
- A . log_artifact
- B . log_model
- C . log_metric
- D . log_param
- E . There is no way to store values like this.
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 . All of these reasons
- B . JS is not normalized or smoothed
- C . None of these reasons
- D . JS is more robust when working with large datasets
- E . JS does not require any manual threshold or cutoff determinations
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
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?
- A . spark.read.format(“delta”).load(path).drop(“star_rating”)
- B . spark.read.format(“delta”).table(path).drop(“star_rating”)
- C . Delta tables cannot be modified
- D . spark.read.table(path).drop(“star_rating”)
- E . spark.sql(“SELECT * EXCEPT star_rating FROM path”)
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 description with artifact
- D . Replace client.update_registered_model with mlflow
- E . Add a Python model as an argument to update_registered_model