Question # 1
A large manufacturing company runs a dozen individual Snowflake accounts across its business divisions. The company wants to increase the level of data sharing to support supply chain optimizations and increase its purchasing leverage with multiple vendors.
The company’s Snowflake Architects need to design a solution that would allow the business divisions to decide what to share, while minimizing the level of effort spent on configuration and management. Most of the company divisions use Snowflake accounts in the same cloud deployments with a few exceptions for European-based divisions.
According to Snowflake recommended best practice, how should these requirements be met? | | A. Migrate the European accounts in the global region and manage shares in a connected graph architecture. Deploy a Data Exchange. | | B. Deploy a Private Data Exchange in combination with data shares for the European accounts.
| | C. Deploy to the Snowflake Marketplace making sure that invoker_share() is used in all secure views.
| | D. Deploy a Private Data Exchange and use replication to allow European data shares in the Exchange.
|
D. Deploy a Private Data Exchange and use replication to allow European data shares in the Exchange.
Explanation:
According to Snowflake recommended best practice, the requirements of the large manufacturing company should be met by deploying a Private Data Exchange in combination with data shares for the European accounts. A Private Data Exchange is a feature of the Snowflake Data Cloud platform that enables secure and governed sharing of data between organizations. It allows Snowflake customers to create their own data hub and invite other parts of their organization or external partners to access and contribute data sets. A Private Data Exchange provides centralized management, granular access control, and data usage metrics for the data shared in the exchange1. A data share is a secure and direct way of sharing data between Snowflake accounts without having to copy or move the data. A data share allows the data provider to grant privileges on selected objects in their account to one or more data consumers in other accounts2. By using a Private Data Exchange in combination with data shares, the company can achieve the following benefits:
The business divisions can decide what data to share and publish it to the Private Data Exchange, where it can be discovered and accessed by other members of the exchange. This reduces the effort and complexity of managing multiple data sharing relationships and configurations.
The company can leverage the existing Snowflake accounts in the same cloud deployments to create the Private Data Exchange and invite the members to join. This minimizes the migration and setup costs and leverages the existing Snowflake features and security.
The company can use data shares to share data with the European accounts that are in different regions or cloud platforms. This allows the company to comply with the regional and regulatory requirements for data sovereignty and privacy, while still enabling data collaboration across the organization.
The company can use the Snowflake Data Cloud platform to perform data analysis and transformation on the shared data, as well as integrate with other data sources and applications. This enables the company to optimize its supply chain and increase its purchasing leverage with multiple vendors.
Question # 2
An Architect has chosen to separate their Snowflake Production and QA environments using two separate Snowflake accounts.
The QA account is intended to run and test changes on data and database objects before pushing those changes to the Production account. It is a requirement that all database objects and data in the QA account need to be an exact copy of the database objects, including privileges and data in the Production account on at least a nightly basis.
Which is the LEAST complex approach to use to populate the QA account with the Production account’s data and database objects on a nightly basis? | A. 1) Create a share in the Production account for each database
2) Share access to the QA account as a Consumer
3) The QA account creates a database directly from each share
4) Create clones of those databases on a nightly basis
5) Run tests directly on those cloned databases | B. 1) Create a stage in the Production account
2) Create a stage in the QA account that points to the same external object-storage location
3) Create a task that runs nightly to unload each table in the Production account into the stage
4) Use Snowpipe to populate the QA account | C. 1) Enable replication for each database in the Production account
2) Create replica databases in the QA account
3) Create clones of the replica databases on a nightly basis
4) Run tests directly on those cloned databases | D. 1) In the Production account, create an external function that connects into the QA account and returns all the data for one specific table.
2) Run the external function as part of a stored procedure that loops through each table in the Production account and populates each table in the QA account. |
C. 1) Enable replication for each database in the Production account
2) Create replica databases in the QA account
3) Create clones of the replica databases on a nightly basis
4) Run tests directly on those cloned databases
Explanation:
This approach is the least complex because it uses Snowflake’s built-in replication feature to copy the data and database objects from the Production account to the QA account. Replication is a fast and efficient way to synchronize data across accounts, regions, and cloud platforms. It also preserves the privileges and metadata of the replicated objects. By creating clones of the replica databases, the QA account can run tests on the cloned data without affecting the original data. Clones are also zero-copy, meaning they do not consume any additional storage space unless the data is modified. This approach does not require any external stages, tasks, Snowpipe, or external functions, which can add complexity and overhead to the data transfer process.
Question # 3
| Which statements describe characteristics of the use of materialized views in Snowflake? (Choose two.) | | A. They can include ORDER BY clauses.
| | B. They cannot include nested subqueries.
| | C. They can include context functions, such as CURRENT_TIME().
| | D. They can support MIN and MAX aggregates.
| | E. They can support inner joins, but not outer joins.
|
B. They cannot include nested subqueries.
D. They can support MIN and MAX aggregates.
Explanation:
According to the Snowflake documentation, materialized views have some limitations on the query specification that defines them. One of these limitations is that they cannot include nested subqueries, such as subqueries in the FROM clause or scalar subqueries in the SELECT list. Another limitation is that they cannot include ORDER BY clauses, context functions (such as CURRENT_TIME()), or outer joins. However, materialized views can support MIN and MAX aggregates, as well as other aggregate functions, such as SUM, COUNT, and AVG.
Question # 4
| A user has activated primary and secondary roles for a session.
What operation is the user prohibited from using as part of SQL actions in Snowflake using the secondary role? | | A. Insert | | B. Create | | C. Delete | | D. Truncate |
B. Create
Explanation:
In Snowflake, when a user activates a secondary role during a session, certain privileges associated with DDL (Data Definition Language) operations are restricted. The CREATE statement, which falls under DDL operations, cannot be executed using a secondary role. This limitation is designed to enforce role-based access control and ensure that schema modifications are managed carefully, typically reserved for primary roles that have explicit permissions to modify database structures.References: Snowflake's security and access control documentation specifying the limitations and capabilities of primary versus secondary roles in session management.
Question # 5
| What are characteristics of Dynamic Data Masking? (Select TWO).
| | A. A masking policy that Is currently set on a table can be dropped.
| | B. A single masking policy can be applied to columns in different tables.
| | C. A masking policy can be applied to the value column of an external table.
| | D. The role that creates the masking policy will always see unmasked data In query results
| | E. A masking policy can be applied to a column with the GEOGRAPHY data type.
|
A. A masking policy that Is currently set on a table can be dropped.
B. A single masking policy can be applied to columns in different tables.
Explanation:
Dynamic Data Masking is a feature that allows masking sensitive data in query results based on the role of the user who executes the query. A masking policy is a user-defined function that specifies the masking logic and can be applied to one or more columns in one or more tables. A masking policy that is currently set on a table can be dropped using the ALTER TABLE command. A single masking policy can be applied to columns in different tables using the ALTER TABLE command with the SET MASKING POLICY clause. The other options are either incorrect or not supported by Snowflake. A masking policy cannot be applied to the value column of an external table, as external tables do not support column-level security. The role that creates the masking policy will not always see unmasked data in query results, as the masking policy can be applied to the owner role as well. A masking policy cannot be applied to a column with the GEOGRAPHY data type, as Snowflake only supports masking policies for scalar data types.
Question # 6
| A group of Data Analysts have been granted the role analyst role. They need a Snowflake database where they can create and modify tables, views, and other objects to load with their own data. The Analysts should not have the ability to give other Snowflake users outside of their role access to this data.
How should these requirements be met? | | A. Grant ANALYST_R0LE OWNERSHIP on the database, but make sure that ANALYST_ROLE does not have the MANAGE GRANTS privilege on the account. | | B. Grant SYSADMIN ownership of the database, but grant the create schema privilege on the database to the ANALYST_ROLE. | | C. Make every schema in the database a managed access schema, owned by SYSADMIN, and grant create privileges on each schema to the ANALYST_ROLE for each type of object that needs to be created. | | D. Grant ANALYST_ROLE ownership on the database, but grant the ownership on future [object type] s in database privilege to SYSADMIN. |
C. Make every schema in the database a managed access schema, owned by SYSADMIN, and grant create privileges on each schema to the ANALYST_ROLE for each type of object that needs to be created.
Explanation:
The requirements state that the data analysts need to be able to create and modify database objects and load data, but should not be able to manage access for users outside of their role.
Option C: By making each schema within the database a managed access schema and having them owned by SYSADMIN, the ability to grant privileges on the schema's objects is strictly controlled. Managed access schemas limit the granting of privileges to the role specified as the owner of the schema, in this case, SYSADMIN. The ANALYST_ROLE can be granted the privileges necessary to create and modify objects within these schemas, satisfying the requirement for the analysts to perform their tasks without being able to extend access beyond their role.
Question # 7
| What built-in Snowflake features make use of the change tracking metadata for a table? (Choose two.)
| | A. The MERGE command
| | B. The UPSERT command
| | C. The CHANGES clause
| | D. A STREAM object
| | E. The CHANGE_DATA_CAPTURE command
|
A. The MERGE command
D. A STREAM object
Explanation:
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.
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