Snowflake - Build and Architect Data Pipelines Using AWS - Lab - Column Level Dynamic Data Masking

The video tutorial explains how to implement column-level security in Snowflake by creating and applying a masking policy to sensitive data in a table. It demonstrates the process of setting up a masking policy, applying it to a specific column, and testing it with different user roles to ensure that sensitive data is masked for unauthorized users while remaining visible to authorized roles.

Why is it important to mask sensitive data in a database table?

• To enhance data visualization

• To improve database performance

• To reduce storage costs

• To prevent unauthorized access to sensitive information

What is the first step in creating a masking policy in Snowflake?

• Switching user roles

• Defining the masking policy

• Creating a new table

• Granting permissions to users

Which SQL command is used to apply a masking policy to a column in Snowflake?

• ALTER TABLE

• CREATE POLICY

• GRANT PERMISSION

• SELECT MASK

What role is created to test the masking policy in the example?

• Database Admin

• Reporting Intern

• Data Analyst

• Security Officer

How can you verify that a masking policy is working correctly?

• By exporting the data to a CSV file

• By running a performance test

• By switching to a role with restricted access and viewing the data

• By checking the database logs

What is the expected output when a user with restricted access views a masked column?

• An error message

• The actual data values

• A blank column

• Masked values like asterisks or nines

What is the next topic mentioned after column-level security?

• Database optimization

• Row-level security

• Data encryption

• User authentication