最新なDatabricks Databricks-Machine-Learning-Professional問題集（62題）、真実試験の問題を全部にカバー！

質問 1：
Which of the following is a probable response to identifying drift in a machine learning application?
A. None of these responses
B. Sunsetting the machine learning application
C. Retraining and deploying a model on more recent data
D. All of these responses
E. Rebuilding the machine learning application with a new label variable
正解：A

質問 2：
A machine learning engineer has deployed a model recommender using MLflow Model Serving. They now want to query the version of that model that is in the Production stage of the MLflow Model Registry.
Which of the following model URIs can be used to query the described model version?
A. https://<databricks-instance>/model-serving/recommender/Production/invocations
B. https://<databricks-instance>/model-serving/recommender/stage-production/invocations
C. https://<databricks-instance>/model/recommender/stage-production/invocations
D. https://<databricks-instance>/model/recommender/Production/invocations
E. The version number of the model version in Production is necessary to complete this task.
正解：E

質問 3：
Which of the following operations in Feature Store Client fs can be used to return a Spark DataFrame of a data set associated with a Feature Store table?
A. There is no way to accomplish this task with fs
B. fs.create_table
C. fs.get_table
D. fs.write_table
E. fs.read_table
正解：B

質問 4：
A data scientist has developed a model model and computed the RMSE of the model on the test set. They have assigned this value to the variable rmse. They now want to manually store the RMSE value with the MLflow run.
They write the following incomplete code block:

Which of the following lines of code can be used to fill in the blank so the code block can successfully complete the task?
A. log_param
B. There is no way to store values like this.
C. log_metric
D. log_artifact
E. log_model
正解：D

質問 5：
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 of the following code blocks can they use to accomplish the task?
A.

B.

C.

D.

正解：B

質問 6：
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. MlflowClient.get_model_version
C. mlflow.register_model
D. mlflow.add_model_version
E. MlflowClient.update_registered_model
正解：E