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In optimizing enterprise-scale semantic models, what technique assists in managing access to specific rows of data based on user roles or permissions?

In a warehouse or lakehouse environment, what process focuses on ensuring that data meets predefined quality standards before storage or analysis?

You have the following DataFrames:

transactions: Contains five columns named transaction_id, customer_id, product_id, amount, and date and has 10 million rows, with each row representing a transaction. customers: Contains customer details in 1,000 rows and three columns named customer_id, name, and country.

You need to join the DataFrames on the customer_id column. The solution must minimize data shuffling.

You write the following code.

from pyspark.sql import functions as F

results =

Which code should you run to populate the results DataFrame?

Which approach assists in optimizing query performance in enterprise-scale semantic models while ensuring selective access to specific rows of data based on user roles?

Which strategy is employed to optimize enterprise-scale semantic models for efficient data retrieval and storage?