ARA-R01 Exam Dumps : SnowPro Advanced: Architect Recertification Exam

According to the SnowPro Advanced: Architect documents and learning resources, column masking policies are applied at query time based on the privileges of the user who runs the query. Therefore, if a user has the privilege to see unmasked data in a column, they will see the original data when they query that column. If they load this column data into another column that does not have a masking policy, the unmasked data will be loaded in the new column, and any user who can query the new column will see the unmasked data as well. The masking policy does not affect the underlying data in the column, only the query results.

References:

• Snowflake Documentation: Column Masking
• Snowflake Learning: Column Masking

• The correct answer is B and E because they use the correct syntax and values for the identifier function and the session variables.
• The identifier function allows you to use a variable or expression as an identifier (such as a table name or column name) in a SQL statement. It takes a single argument and returns it as an identifier. For example, identifier($tbl_ref) returns EMP_TBL as an identifier.
• The session variables are set using the SET command and can be referenced using the $ sign. For example, $var1 returns Name1 as a value.
• Option A is incorrect because it uses Stbl_ref and Scol_ref, which are not valid session variables or identifiers. They should be $tbl_ref and $col_ref instead.
• Option C is incorrect because it uses identifier, which is not a valid syntax for the identifier function. It should be identifier($tbl_ref) instead.
• Option D is incorrect because it uses Cvarl, var2, and var3, which are not valid session variables or values. They should be $var1, $var2, and $var3 instead. References:
• Snowflake Documentation: Identifier Function
• Snowflake Documentation: Session Variables
• Snowflake Learning: SnowPro Advanced: Architect Exam Study Guide

In Snowflake, the change tracking metadata for a table is utilized by the MERGE command and the STREAM object. The MERGE command uses change tracking to determine how to apply updates and inserts efficiently based on differences between source and target tables. STREAM objects, on the other hand, specifically capture and store change data, enabling incremental processing based on changes made to a table since the last stream offset was committed.References: Snowflake Documentation on MERGE and STREAM Objects.