Snowflake DEA-C01 Exam With Confidence Using Practice Dumps

The Snowflake Kafka connector uses three Snowflake objects: pipe, internal table stage, and internal named stage. The pipe object is used to load data from an external stage into a Snowflake table using COPY statements. The internal table stage is used to store files that are loaded from Kafka topics into Snowflake using PUT commands. The internal named stage is used to store files that are rejected by the COPY statements due to errors or invalid data. The other options are not objects that are used by the Snowflake Kafka connector. Option B, serverless task, is an object that can execute SQL statements on a schedule without requiring a warehouse. Option C, internal user stage, is an object that can store files for a specific user in Snowflake using PUT commands. Option F, storage integration, is an object that can enable secure access to external cloud storage services without exposing credentials.

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 output that is provided by both the SYSTEM$CLUSTERING_DEPTH function and the SYSTEM$CLUSTERING_INFORMATION function is average_depth. This output indicates the average number of micro-partitions that contain data for a given column value or combinationof column values. The other outputs are not common to both functions. The notes output is only provided by the SYSTEM$CLUSTERING_INFORMATION function and it contains additional information or recommendations about the clustering status of the table. The average_overlaps output is only provided by the SYSTEM$CLUSTERING_DEPTH function and it indicates the average number of micro-partitions that overlap with other micro-partitions for a given column value or combination of column values. The total_partition_count output is only provided by the SYSTEM$CLUSTERING_INFORMATION function and it indicates the total number of micro-partitions in the table.