최신 Databricks-Machine-Learning-Professional 무료덤프 - Databricks Certified Machine Learning Professional

A. None. Staging. Production

B. Staging. Production. Archived

C. None. Staging. Production. Archived

D. Development. Staging. Production. Archived

E. Development. Staging. Production

A. Concept drift is when there is a change in the distribution of an input variable

B. None of these describe Concept drift

C. Concept drift is when there is a change in the distribution of a target variable

D. Concept drift is when there is a change in the distribution of the predicted target given by the model

E. Concept drift is when there is a change in the relationship between input variables and target variables

A. mlflow.shap.log_explanation

B. mlflow.shap

C. mlflow.log_figure

D. client.log_artifact

E. None of these operations can accomplish the task.

A. Real-time

B. None of these strategies will accomplish the task.

C. Batch

D. Edge/on-device

E. Streaming

A. mlflow.pyfunc.load_model(model_uri)

B. mlflow.spark.load_model(model_uri)

C. mlflow.sklearn.load_model(model_uri)

D. mlflow.sklearn.read_model(model_uri)

E. mlflow.pyfunc.read_model(model_uri)

A. The schema of input data can be validated when serving models

B. The model will have a unique identifier in the MLflow experiment

C. The model will be secured by the user that developed it

D. The schema of input data will be converted to match the signature

E. The model can be deployed using real-time serving tools