Snowflake Updated ARA-C01 Exam Questions and Answers by tom

A healthcare company that handles PHI data must ensure compliance with relevant privacy standards, such as HIPAA, HITRUST, and GDPR. Snowflake provides several features and best practices to help customers meet their data protection and compliance requirements1.

One best practice recommendation is to use, at minimum, the Business Critical edition of Snowflake. This edition provides the highest level of data protection and security, including end-to-end encryption with customer-managed keys, enhanced object-level security, and HIPAA and HITRUST compliance2. Therefore, option A is correct.

Another best practice recommendation is to create Dynamic Data Masking policies and apply them to columns that contain PHI. Dynamic Data Masking is a feature that allows masking or redacting sensitive data based on the current user’s role. This way, only authorized users can view the unmasked data, while others will see masked values, such as NULL, asterisks, or random characters3. Therefore, option B is correct.

A third best practice recommendation is to use the External Tokenization feature to obfuscate sensitive data. External Tokenization is a feature that allows replacing sensitive data with tokens that are generated and stored by an external service, such as Protegrity. This way, the original data is never stored or processed by Snowflake, and only authorized users can access the tokenized data through the external service4. Therefore, option D is correct.

Option C is incorrect, because the Internal Tokenization feature is not available in Snowflake. Snowflake does not provide any native tokenization functionality, but only supports integration with external tokenization services4.

Option E is incorrect, because rewriting SQL queries to eliminate projections of PHI data based on current_role() is not a best practice. This approach is error-prone, inefficient, and hard to maintain. A better alternative is to use Dynamic Data Masking policies, which can automatically mask data based on the user’s role without modifying the queries3.

Option F is incorrect, because avoiding sharing data with partner organizations is not a best practice. Snowflake enables secure and governed data sharing with internal and external consumers, such as business units, customers, or partners. Data sharing does not involve copying or moving data, but only granting access privileges to the shared objects. Data sharing can also leverage Dynamic Data Masking and External Tokenization features to protect sensitive data5.

 Snowflake’s Security & Compliance Reports : Snowflake Editions : Dynamic Data Masking : External Tokenization : Secure Data Sharing

 Snowflake context functions are functions that return information about the current session, user, role, warehouse, database, schema, or object. They can be used to help determine whether a user is authorized to see data that has column-level security enforced by setting masking policy conditions based on the context functions. The following context functions are relevant for column-level security:

current_role: This function returns the name of the role in use for the current session. It can be used to set masking policy conditions that target the current session and are not affected by the execution context of the SQL statement. For example, a masking policy condition using current_role can allow or deny access to a column based on the role that the user activated in the session.

invoker_role: This function returns the name of the executing role in a SQL statement. It can be used to set masking policy conditions that target the executing role and are affected by the execution context of the SQL statement. For example, a masking policy condition using invoker_role can allow or deny access to a column based on the role that the user specified in the SQL statement, such as using the AS ROLE clause or a stored procedure.

is_role_in_session: This function returns TRUE if the user’s current role in the session (i.e. the role returned by current_role) inherits the privileges of the specified role. It can be used to set masking policy conditions that involve role hierarchy and privilege inheritance. For example, a masking policy condition using is_role_in_session can allow or deny access to a column based on whether the user’s current role is a lower privilege role in the specified role hierarchy.

The other options are not valid ways to use the Snowflake context functions for column-level security:

Set masking policy conditions using is_role_in_session targeting the role in use for the current account. This option is incorrect because is_role_in_session does not target the role in use for the current account, but rather the role in use for the current session. Also, the current account is not a role, but rather a logical entity that contains users, roles, warehouses, databases, and other objects.

Determine if there are ownership privileges on the masking policy that would allow the use of any function. This option is incorrect because ownership privileges on the masking policy do not affect the use of any function, but rather the ability to create, alter, or drop the masking policy. Also, this is not a way to use the Snowflake context functions, but rather a way to check the privileges on the masking policy object.

Assign the accountadmin role to the user who is executing the object. This option is incorrect because assigning the accountadmin role to the user who is executing the object does not involve using the Snowflake context functions, but rather granting the highest-level role to the user. Also, this is not a recommended practice for column-level security, as it would give the user full access to all objects and data in the account, which could compromise data security and governance.

Context Functions

Advanced Column-level Security topics

Snowflake Data Governance: Column Level Security Overview

Data Security Snowflake Part 2 - Column Level Security

This command will fail because while the user has USAGE permission ondb2andschema2through Role B, and can create a view inschema2, they do not have SELECT permission ondb1.schemal.table1with Role B. Since Role A, which has SELECT permission ondb1.schemal.table1, is not the currently active role when the viewv2is being created indb2.schema2, the user does not have the necessary permissions to read fromdb1.schemal.table1to create the view. Snowflake’s security model requires that the active role have all necessary permissions to execute the command.

The ON_ERROR clause is an optional parameter for the COPY INTO command that specifies the behavior of the command when it encounters errors in the files. The ON_ERROR clause can have one of the following values1:

CONTINUE: This value instructs the command to continue loading the file and return an error message for a maximum of one error encountered per data file. The difference between the ROWS_PARSED and ROWS_LOADED column values represents the number of rows that include detected errors. To view all errors in the data files, use the VALIDATION_MODE parameter or query the VALIDATE function1.

SKIP_FILE: This value instructs the command to skip the file when it encounters a data error on any of the records in the file. The command moves on to the next file in the stage and continues loading. The skipped file is not loaded and no error message is returned for the file1.

ABORT_STATEMENT: This value instructs the command to stop loading data when the first error is encountered. The command returns an error message for the file and aborts the load operation. This is the default value for the ON_ERROR clause1.

Therefore, options A, B, and C are correct.

 COPY INTO

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