Databricks Databricks-Machine-Learning-Professional 問題集

質問 1：
A machine learning engineer wants to view all of the active MLflow Model Registry Webhooks for a specific model.
They are using the following code block:

Which of the following changes does the machine learning engineer need to make to this code block so it will successfully accomplish the task?
A. Replace POST with GET in the call to http request
B. Replace list with webhooks in the endpoint URL
C. Replace POST with PUT in the call to http request
D. Replace list with view in the endpoint URL
E. There are no necessary changes
正解：B

質問 2：
Which of the following describes the concept of MLflow Model flavors?
A. A convention that MLflow Model Registry can use to version models
B. A convention that MLflow Model Registry can use to organize its Models by project
C. A convention that deployment tools can use to understand the model
D. A convention that deployment tools can use to wrap preprocessing logic into a Model
E. A convention that MLflow Experiments can use to organize their Runs by project
正解：E

質問 3：
Which of the following describes concept drift?
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
正解：D

質問 4：
A machine learning engineer is converting a Hyperopt-based hyperparameter tuning process from manual MLflow logging to MLflow Autologging. They are trying to determine how to manage nested Hyperopt runs with MLflow Autologging.
Which of the following approaches will create a single parent run for the process and a child run for each unique combination of hyperparameter values when using Hyperopt and MLflow Autologging?
A. Ensuring that a built-in model flavor is used for the model logging
B. Startinq a manual parent run before calling fmin
C. Starting a manual child run within the objective function
D. MLflow Autoloqqinq will automatically accomplish this task with Hyperopt
E. There is no way to accomplish nested runs with MLflow Autoloqqinq and Hyperopt
正解：B

質問 5：
A machine learning engineer has registered a sklearn model in the MLflow Model Registry using the sklearn model flavor with UI model_uri.
Which of the following operations can be used to load the model as an sklearn object for batch deployment?
A. mlflow.sklearn.read_model(model_uri)
B. mlflow.spark.load_model(model_uri)
C. mlflow.pyfunc.read_model(model_uri)
D. mlflow.sklearn.load_model(model_uri)
E. mlflow.pyfunc.load_model(model_uri)
正解：E

質問 6：
A machine learning engineer is migrating a machine learning pipeline to use Databricks Machine Learning. They have programmatically identified the best run from an MLflow Experiment and stored its URI in the model_uri variable and its Run ID in the run_id variable. They have also determined that the model was logged with the name "model". Now, the machine learning engineer wants to register that model in the MLflow Model Registry with the name "best_model".
Which of the following lines of code can they use to register the model to the MLflow Model Registry?
A. mlflow.register_model(f"runs:/{run_id}/model")
B. mlflow.register_model(model_uri, "best_model")
C. mlflow.register_model(f"runs:/{run_id}/best_model", "model")
D. mlflow.register_model(model_uri, "model")
E. mlflow.register_model(run_id, "best_model")
正解：D