Online Access Free Databricks-Machine-Learning-Professional Practice Test

A machine learning engineer wants to move their model version model_version for the MLflow Model Registry model model from the Staging stage to the Production stage using MLflow Client client. At the same time, they would like to archive any model versions that are already in the Production stage. Which code block can they use to accomplish the task?

• A.
• B.
• C.
• D.

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

A Machine Learning Engineer is implementing integration tests for an ML pipeline in Databricks.
The current integration test runs the complete workflow but takes four hours to execute due to large dataset processing and extensive model training. They need to select an approach that will be the most effective for optimizing integration test execution while maintaining test reliability. The approach should also be based on MLOps best practices. Which approach will do this?

• A.Run integration tests only in the production environment using full datasets to ensure complete accuracy of the testing process.
• B.Replace integration tests with unit tests for each pipeline component to reduce execution time and focus on individual component validation in a staging environment that closely matches production.
• C.Use small subsets of production-like data and reduce training iterations while maintaining the same pipeline structure and validation checkpoints in a staging environment that closely matches production.
• D.Skip the model training phase entirely and only test data transformations and API endpoints using mock model predictions.

Which of the following describes label drift?

• A.Label drift is when there is a change in the distribution of the predicted target given by the model
• B.Label drift is when there is a change in the distribution of an input variable
• C.None of these describe label drift
• D.Label drift is when there is a change in the relationship between input variables and target variables
• E.Label drift is when there is a change in the distribution of a target variable

A data scientist has developed a scikit-learn model sklearn_model and they want to log the model using MLflow.
They write the following incomplete code block:

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

• A.mlflow.spark.log_model(sklearn_model, "model")
• B.mlflow.sklearn.log_model(sklearn_model, "model")
• C.mlflow.spark.track_model(sklearn_model, "model")
• D.mlflow.sklearn.track_model(sklearn_model, "model")
• E.mlflow.sklearn.load_model("model")