Snowflake DEA-C01 Exam With Confidence Using Practice Dumps

The IS_ROLE_IN_SESSION function is used to set up column-level masking using a role in the hierarchy of the current user. Column-level masking is a feature in Snowflake that allows users to apply dynamic data masking policies to specific columns based on the roles of the users who access them. The IS_ROLE_IN_SESSION function takes a role name as an argument and returns true if the role is in the current user’s session, or false otherwise. The function can be used in a masking policy expression to determine whether to mask or unmask a column value based on the role of the user. For example:

CREATE OR REPLACE MASKING POLICY email_mask AS (val string) RETURNS string -> CASE WHEN IS_ROLE_IN_SESSION(‘HR’) THEN val ELSE REGEXP_REPLACE(val, ‘(.).(.@.)’, ‘\1****\2’) END;

In this example, the IS_ROLE_IN_SESSION function is used to create a masking policy for an email column. The masking policy returns the original email value if the user has the HR role in their session, or returns a masked email value with asterisks if not.

The characteristics of data transformations in Snowpark that should be considered to meet this requirement are:

• It is possible to join multiple tables using DataFrames.
• Snowpark operations are executed lazily on the server.

These characteristics indicate how Snowpark can perform data transformations using DataFrames, which are similar to the ones used in Scala. DataFrames are distributed collections of rows that can be manipulated using various operations, such as joins, filters, aggregations, etc. DataFrames can be created from different sources, such as tables, files, or SQL queries. Snowpark operations are executed lazily on the server, which means that they are not performed until an action is triggered, such as a write or a collect operation. This allows Snowpark to optimize the execution plan and reduce the amount of data transferred between the client and the server.

The other options are not characteristics of data transformations in Snowpark that should be considered to meet this requirement. Option C is incorrect because User-Defined Functions (UDFs) are pushed down to Snowflake and executed on the server. Option D is incorrect because Snowpark does not require a separate cluster outside of Snowflake for computations, but rather uses virtual warehouses within Snowflake. Option E is incorrect because columns in different DataFrames with the same name should be referred to with dot notation, not squared brackets.

 This option is the best way to automate the process of bringing the CSV file into Snowflake with the least amount of operational overhead. SnowSQL is a command-line tool that can be used to execute SQL statements and scripts on Snowflake. By scheduling a SQL file that executes a PUT command, the CSV file can be pushed from the on-premise server to the internal stage in Snowflake. Then, by creating a pipe that runs a COPY INTO statement that references the internal stage, Snowpipe can automatically load the file from the internal stage into the table when it detects a new file in the stage. This way, there is no need to manually start or monitor a virtual warehouse or task.