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：
A machine learning engineer wants to deploy a model for real-time serving using MLflow Model Serving. For the model, the machine learning engineer currently has one model version in each of the stages in the MLflow Model Registry. The engineer wants to know which model versions can be queried once Model Serving is enabled for the model.
Which of the following lists all of the MLflow Model Registry stages whose model versions are automatically deployed with Model Serving?
A. Staging. Production
B. Staging. Production. Archived
C. Production
D. None. Staging. Production. Archived
E. [None. Staging. Production
正解：A

質問 2：
A data scientist wants to remove the star_rating column from the Delta table at the location path. To do this, they need to load in data and drop the star_rating column.
Which of the following code blocks accomplishes this task?
A. spark.read.table(path).drop("star_rating")
B. spark.read.format("delta").load(path).drop("star_rating")
C. spark.read.format("delta").table(path).drop("star_rating")
D. Delta tables cannot be modified
E. spark.sql("SELECT * EXCEPT star_rating FROM path")
正解：A

質問 3：
Which of the following describes concept drift?
A. Concept drift is when there is a change in the distribution of the predicted target given by the model
B. Concept drift is when there is a change in the relationship between input variables and target variables
C. Concept drift is when there is a change in the distribution of an input variable
D. None of these describe Concept drift
E. Concept drift is when there is a change in the distribution of a target variable
正解：A

質問 4：
A data scientist has created a Python function compute_features that returns a Spark DataFrame with the following schema:

The resulting DataFrame is assigned to the features_df variable. The data scientist wants to create a Feature Store table using features_df.
Which of the following code blocks can they use to create and populate the Feature Store table using the Feature Store Client fs?
A. features_df.write.mode("fs").path("new_table")
B. features_df.write.mode("feature").path("new_table")
C.
D.
E.
正解：E

質問 5：
A data scientist set up a machine learning pipeline to automatically log a data visualization with each run. They now want to view the visualizations in Databricks.
Which of the following locations in Databricks will show these data visualizations?
A. The Artifacts section of the MLflow Run page
B. The Artifacts section of the MLflow Experiment page
C. The MLflow Model Registry Model paqe
D. The Figures section of the MLflow Run page
E. Logged data visualizations cannot be viewed in Databricks
正解：D

質問 6：
Which of the following describes label drift?
A. Label drift is when there is a change in the distribution of an input variable
B. Label drift is when there is a change in the relationship between input variables and target variables
C. Label drift is when there is a change in the distribution of the predicted target given by the model
D. None of these describe label drift
E. Label drift is when there is a change in the distribution of a target variable
正解：A