Databricks DATABRICKS-MACHINE-LEARNING-PROFESSIONAL Exam Dumps

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. mlflow.models.schema.infer_schema

B. mlflow.models.signature.infer_signature

C. mlflow.models.Model.get_input_schema

D. mlflow.models.Model.signature

E. There is no way to obtain the input schema and the output schema of an unlogged model.

A machine learning engineer and data scientist are working together to convert a batch deployment to an always-on streaming deployment. The machine learning engineer has expressed that rigorous data tests must be put in place as a part

of their conversion to account for potential changes in data formats.

Which of the following describes why these types of data type tests and checks are particularly important for streaming deployments?

A. Because the streaming deployment is always on, all types of data must be handled without producing an error

B. All of these statements

C. Because the streaming deployment is always on, there is no practitioner to debug poor model performance

D. Because the streaming deployment is always on, there is a need to confirm that the deployment can autoscale

E. None of these statements

A machine learning engineer wants to log and deploy a model as an MLflow pyfunc model. They have custom preprocessing that needs to be completed on feature variables prior to fitting the model or computing predictions using that model.

They decide to wrap this preprocessing in a custom model class ModelWithPreprocess, where the preprocessing is performed when calling fit and when calling predict. They then log the fitted model of the ModelWithPreprocess class as a

pyfunc model.

Which of the following is a benefit of this approach when loading the logged pyfunc model for downstream deployment?

A. The pyfunc model can be used to deploy models in a parallelizable fashion

B. The same preprocessing logic will automatically be applied when calling fit

C. The same preprocessing logic will automatically be applied when calling predict

D. This approach has no impact when loading the logged pyfunc model for downstream deployment

E. There is no longer a need for pipeline-like machine learning objects