Databricks Certified Machine Learning Professional
Last Update Jan 24, 2025
Total Questions : 60
To help you prepare for the Databricks-Machine-Learning-Professional Databricks exam, we are offering free Databricks-Machine-Learning-Professional Databricks exam questions. All you need to do is sign up, provide your details, and prepare with the free Databricks-Machine-Learning-Professional practice questions. Once you have done that, you will have access to the entire pool of Databricks Certified Machine Learning Professional Databricks-Machine-Learning-Professional test questions which will help you better prepare for the exam. Additionally, you can also find a range of Databricks Certified Machine Learning Professional resources online to help you better understand the topics covered on the exam, such as Databricks Certified Machine Learning Professional Databricks-Machine-Learning-Professional video tutorials, blogs, study guides, and more. Additionally, you can also practice with realistic Databricks Databricks-Machine-Learning-Professional exam simulations and get feedback on your progress. Finally, you can also share your progress with friends and family and get encouragement and support from them.
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 machine learning engineer is migrating a machine learning pipeline to use Databricks Machine Learning. They have programmatically identified the best run from an MLflow Experiment and stored its URI in themodel_urivariable and its Run ID in therun_idvariable. They have also determined that the model was logged with the name"model". Now, the machine learning engineer wants to register that model in the MLflow Model Registry with the name"best_model".
Which of the following lines of code can they use to register the model to the MLflow Model Registry?
A data scientist has developed a scikit-learn random forest model model, but they have not yet logged model with MLflow. They want to obtain the input schema and the output schema of the model so they can document what type of data is expected as input.
Which of the following MLflow operations can be used to perform this task?
A data scientist has developed a scikit-learn modelsklearn_modeland they want to log the model using MLflow.
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?
TESTED 24 Jan 2025
