Databricks Certified Machine Learning Professional: Databricks-Machine-Learning-Professional Exam

"Databricks Certified Machine Learning Professional", also known as Databricks-Machine-Learning-Professional exam, is a Databricks Certification. With the complete collection of questions and answers, PrepPDF has assembled to take you through 193 Q&As to your Databricks-Machine-Learning-Professional Exam preparation. In the Databricks-Machine-Learning-Professional exam resources, you will cover every field and category in ML Data Scientist Certification helping to ready you for your successful Databricks Certification.

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  • Exam Code: Databricks-Machine-Learning-Professional
  • Exam Name: Databricks Certified Machine Learning Professional
  • Certification Provider: Databricks
  • Corresponding Certification: ML Data Scientist
  • Updated: Nov 01, 2026
  • No. of Questions: 193 Questions & Answers with Testing Engine
  • Download Limit: Unlimited

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Databricks Databricks-Machine-Learning-Professional Exam Syllabus Topics:

TopicDetails
Topic 1
  • Identify which code block will trigger a shown webhook
  • Describe the basic purpose and user interactions with Model Registry
Topic 2
  • 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 3
  • 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 4
  • Identify live serving benefits of querying precomputed batch predictions
  • Describe Structured Streaming as a common processing tool for ETL pipelines
Topic 5
  • Describe concept drift and its impact on model efficacy
  • Describe summary statistic monitoring as a simple solution for numeric feature drift
Topic 6
  • Identify less performant data storage as a solution for other use cases
  • Describe why complex business logic must be handled in streaming deployments
Topic 7
  • Identify the requirements for tracking nested runs
  • Describe an MLflow flavor and the benefits of using MLflow flavors

Reference: https://www.databricks.com/learn/certification/machine-learning-professional

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Databricks Certified Machine Learning Professional Sample Questions:

A Data Scientist has created a sales forecasting model, named sales-forecasting. The model is deployed to a model serving endpoint with a schema. They need to invoke this model using Python and would prefer to use a SDK function to make the request. Which method suits these requirements?

  • A. Import the MLflow Deployments class and use the predict method and provide the endpoint and input parameters.
  • B. Import the MLflow Deployments class and use the ai_query method and provide the endpoints and request parameters.
  • C. Make an API request to the MODEL_VERSION_URI and provide the dataframe_split as the request parameter.
  • D. Use the built in ai_query function and provide the endpoints and request parameters.
Reveal Solution  Discussion  0

Correct Answer: A  🗳️

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A machine learning engineer has detected that concept drift is occurring in a production machine learning application. Which result is the impact of concept drift?

  • A. The model's latency will increase
  • B. The model's efficacy will increase
  • C. The model's efficacy will decrease
  • D. The model's latency will decrease
Reveal Solution  Discussion  0

Correct Answer: C  🗳️

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A machine learning engineer needs a python_model to access a collection of files using its load_context operation. The collection of files being accessed by python_model.load_context needs to be saved when the model is being logged. A dictionary of the names and paths of these files is properly stored in my_dict.
The machine learning engineer has written the following incomplete block of code:

Which lines of code can be used to fill in the blank to successfully complete the code block to accomplish the task?

  • A. artifacts
  • B. context
  • C. model_context
  • D. None of these lines of code can be used to successfully complete the code block
Reveal Solution  Discussion  0

Correct Answer: A  🗳️

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A Data Scientist has trained a regression model using Spark's MLlib to predict housing prices.
The model needs to be evaluated to determine how well it predicts on a testing dataset. The Data Scientist wants to use a standard regression metric for the evaluation. They also want to evaluate the model with the least processing overhead required. Which approach will suit their needs?

  • A. Convert the testing dataset to a pandas DataFrame and use scikit-learn's root_mean_squared_error function to calculate the root mean squared error on the testing dataset.
  • B. Implement a Spark UDF to calculate the root mean squared error for each row in the testing dataset and aggregate these values to obtain the overall RMSE score.
  • C. Use the MLlib's RegressionEvaluator with the metricName set to rmse to calculate root mean squared error on the testing dataset.
  • D. Use the MLlib's RankingEvaluator with the metricName set to rmse to calculate root mean squared error on the testing dataset.
Reveal Solution  Discussion  0

Correct Answer: C  🗳️

Explanation: Only visible for PrepPDF members. You can sign-up / login (it's free).

Which of the following operations in Feature Store Client fs can be used to return a Spark DataFrame of a data set associated with a Feature Store table?

  • A. fs.create_table
  • B. fs.get_table
  • C. There is no way to accomplish this task with fs
  • D. fs.read_table
  • E. fs.write_table
Reveal Solution  Discussion  0

Correct Answer: A  🗳️

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