You are developing a data pipeline in Snowflake that processes sensitive customer data'. You need to implement robust data governance controls, including column-level security and data masking. Which of the following combinations of Snowflake features, when used together, provides the MOST comprehensive solution for achieving this?

• A. Dynamic tables and masking policies.
• B. Object tagging, column-level security policies (using views), and masking policies.
• C. Row-level security policies and data masking policies.
• D. Row access policies and data masking policies on base tables, supplemented with object tagging and column-level security policies on views that grant limited access to specific user roles.
• E. Data masking policies and network policies.

You have a Snowflake table 'raw_data' with columns 'id', 'timestamp', and 'payload'. A stream is defined on this table. A data pipeline reads changes from the stream and applies transformations before loading the data into a target table. However, the pipeline needs to handle cases where updates to the same 'id' occur multiple times within a short period, and only the latest version of the 'payload' should be processed. How can you achieve this idempotent processing of stream data to ensure only the latest payload is applied to the target table, avoiding duplicates and inconsistencies, using Snowflake streams?

• A. Before loading data into target table, create a temporary table by grouping Sid' and selecting the maximum 'timestamp' and corresponding 'payload' from stream. Finally, load this data into target table.
• B. Use a regular Snowflake task to periodically merge the stream data into the target table, overwriting any existing records with the same Sid'.
• C. Configure the stream with a unique key constraint on the Sid' column to prevent multiple updates for the same Sid' from being captured.
• D. Create a materialized view on the stream, grouping by 'id' and selecting the maximum 'timestamp' and corresponding 'payload'. Then, consume the materialized view instead of the stream.
• E. When processing data from the stream, use a MERGE statement with a staging table. Load all stream changes into the staging table, then merge from the staging table to the target table using 'timestamp' to identify the latest version.

Which of the following statements are true regarding using Dynamic Data Masking and Column-Level Security in Snowflake? (Select all that apply)

• A. Dynamic Data Masking can be used to apply different masking rules based on the user's role, IP address, or other contextual factors.
• B. Dynamic Data Masking is applied at query runtime, while Column-Level Security through views or roles is applied when the object is created.
• C. Dynamic Data Masking policies can reference external tables directly without requiring special grants.
• D. Using both Dynamic Data Masking and Column-Level Security (e.g. views) on the same column is redundant and will result in an error.
• E. Column-Level Security via views provides more fine-grained control over data access compared to Dynamic Data Masking.

You have created a secure external function that uses a Snowflake secret to retrieve a cryptographic key and mask sensitive data'. However, users are still able to see the sensitive data'. Which of the following actions is MOST likely to resolve the issue?

• A. Grant the 'EXECUTE FUNCTION' privilege on the function to the user.
• B. Ensure that the user has the 'USAGE privilege on the database and schema where the secret is stored.
• C. Verify that the user has the 'OWNERSHIP privilege on the table containing the sensitive data.
• D. Ensure that the user calling the function has the 'USAGE privilege on the security integration associated with the external function.
• E. Grant the ' SELECT privilege on the Snowflake secret object to the user.