PROFESSIONAL-MACHINE-LEARNING-ENGINEER Question #119: Real Exam Question with Answer & Explanation

Question

You are experimenting with a built-in distributed XGBoost model in Vertex AI Workbench user- managed notebooks. You use BigQuery to split your data into training and validation sets using the following queries: CREATE OR REPLACE TABLE 'myproject.mydataset.training' AS (SELECT * FROM 'myproject.mydataset.mytable' WHERE RAND() <= 0.8); CREATE OR REPLACE TABLE 'myproject.mydataset.validation' AS (SELECT * FROM 'myproject.mydataset.mytable' WHERE RAND() <= 0.2); After training the model, you achieve an area under the receiver operating characteristic curve (AUC ROC) value of 0.8, but after deploying the model to production, you notice that your model performance has dropped to an AUC ROC value of 0.65. What problem is most likely occurring?

Options

• AThere is training-serving skew in your production environment.
• BThere is not a sufficient amount of training data.
• CThe tables that you created to hold your training and validation records share some records, and
• DThe RAND() function generated a number that is less than 0.2 in both instances, so every record