Databricks ML Data Scientist Databricks-Machine-Learning-Professional

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

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

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Databricks Certified Machine Learning Professional Sample Questions:

Question #1
Which of the following MLflow operations can be used to delete a model from the MLflow Model Registry?

A. client.transition_model_version_stage
B. client.delete_registered_model
C. client.update_registered_model
D. client.delete_model_version
E. client.delete_model

Question #2
A machine learning engineer has implemented a numeric drift monitoring solution by examining trends in the summary statistics of input variables. However, the engineer's stakeholders would like a more robust monitoring solution. Which of the following can provide a more robust drift monitoring solution for numeric feature variables?

A. Oversampling
B. Statistical tests
C. None of these can provide more robust drift monitoring than summary statistics
D. Correlations

Question #3
A data scientist has developed a scikit-learn random forest model model, but they have not yet logged model with MLflow. They have created a model signature model_signature and a few example input records input_records. Which block of code can be used to log all of this information?

A.

B.

C. mlflow.sklearn.log_model(model, "model")
mlflow.log_signature(model_signature)
mlflow.log_input(input_records)
D. There is no way to log a model signature.

Question #4
A data scientist has developed a model to predict whether or not it will rain using the expected temperature and expected cloud coverage. However, the proportion of days where it actually rains has increased dramatically from the proportion in the data on which the model was trained.
Which type of drift is present in the above scenario?

A. Feature drift
B. Label drift
C. Prediction drift
D. Concept drift

Question #5
A Data Scientist is building a product recommendation model that suggests additional items based on a customer's current shopping basket for an online merchant. The model needs to access the real-time basket contents at inference time to avoid recommending items already in the basket. The model will be deployed using Databricks Model Serving. The data scientist now wants to implement real-time feature engineering to incorporate the current basket contents into the model's prediction. Which approach will do this?

A. Write basket data to a feature table and publish to the online store for low-latency access.
B. Implement feature preprocessing in a separate microservice that feeds the model serving endpoint.
C. Create a custom pyfunc model that processes the basket contents as part of the model's predict() method.
D. Store basket data in a Delta table and configure automatic feature lookup during model serving.

Solutions: