Databricks Databricks-Machine-Learning-Professional 問題集

質問 1：
Which of the following is an advantage of using the python_function(pyfunc) model flavor over the built-in library-specific model flavors?
A. python_function can be used to store models in an MLmodel file
B. python_function can be used to deploy models without worrying about which library was used to create the model
C. python_function provides no benefits over the built-in library-specific model flavors
D. python_function can be used to deploy models without worrying about whether they are deployed in batch, streaming, or real-time environments
E. python_function can be used to deploy models in a parallelizable fashion
正解：E

質問 2：
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, there is no practitioner to debug poor model performance
B. None of these statements
C. Because the streaming deployment is always on, there is a need to confirm that the deployment can autoscale
D. Because the streaming deployment is always on, all types of data must be handled without producing an error
E. All of these statements
正解：C

質問 3：
Which of the following is a probable response to identifying drift in a machine learning application?
A. None of these responses
B. Retraining and deploying a model on more recent data
C. All of these responses
D. Sunsetting the machine learning application
E. Rebuilding the machine learning application with a new label variable
正解：A

質問 4：
A machine learning engineer is manually refreshing a model in an existing machine learning pipeline. The pipeline uses the MLflow Model Registry model "project". The machine learning engineer would like to add a new version of the model to "project".
Which of the following MLflow operations can the machine learning engineer use to accomplish this task?
A. The machine learning engineer needs to create an entirely new MLflow Model Registry model
B. mlflow.register_model
C. MlflowClient.get_model_version
D. MlflowClient.update_registered_model
E. mlflow.add_model_version
正解：D

質問 5：
Which of the following describes label drift?
A. Label drift is when there is a change in the relationship between input variables and target variables
B. Label drift is when there is a change in the distribution of the predicted target given by the model
C. Label drift is when there is a change in the distribution of an input variable
D. Label drift is when there is a change in the distribution of a target variable
E. None of these describe label drift
正解：C

質問 6：
Which of the following Databricks-managed MLflow capabilities is a centralized model store?
A. Experiments
B. Model Registry
C. Models
D. Model Serving
E. Feature Store
正解：D