Snowflake ARA-R01 Exam Dumps - Valid Questions Answers

 These two features are relevant for modeling using Data Vault on Snowflake. Data Vault is a data modeling approach that organizes data into hubs, links, and satellites. Data Vault is designed to enable high scalability, flexibility, and performance for data integration and analytics. Snowflake is a cloud data platform that supports various data modeling techniques, including Data Vault. Snowflake provides some features that can enhance the Data Vault modeling, such as:

• Snowflake’s support of multi-table inserts into the data model’s Data Vault tables. Multi-table inserts (MTI) are a feature that allows inserting data from a single query into multiple tables in a single DML statement. MTI can improve the performance and efficiency of loading datainto Data Vault tables, especially for real-time or near-real-time data integration. MTI can also reduce the complexity and maintenance of the loading code, as well as the data duplication and latency12.
• Scaling up the virtual warehouses will support parallel processing of new source loads. Virtual warehouses are a feature that allows provisioning compute resources on demand for data processing. Virtual warehouses can be scaled up or down by changing the size of the warehouse, which determines the number of servers in the warehouse. Scaling up the virtual warehouses can improve the performance and concurrency of processing new source loads into Data Vault tables, especially for large or complex data sets. Scaling up the virtual warehouses can also leverage the parallelism and distribution of Snowflake’s architecture, which can optimize the data loading and querying34.

References:

• Snowflake Documentation: Multi-table Inserts
• Snowflake Blog: Tips for Optimizing the Data Vault Architecture on Snowflake
• Snowflake Documentation: Virtual Warehouses
• Snowflake Blog: Building a Real-Time Data Vault in Snowflake

• The correct answer is A because it grants the specific privilege to create a materialized view on the schema EDW.STG_SCHEMA to the user USER_01 directly.
• Option B is incorrect because it grants the privilege to create a materialized view on the entire database EDW, which is too broad and unnecessary. Also, there is a typo in the user name (USERJD1 instead of USER_01).
• Option C is incorrect because it grants the privilege to create a materialized view on a different schema (ECW.STG_SCHEKA instead of EDW.STG_SCHEMA). Also, there is no need to create a new role for this purpose.
• Option D is incorrect because it grants the privilege to create a materialized view on an invalid object (EDW.STG_SCHEMA is not a valid schema name, it should be EDW.STG_SCHEMA). Also, there is no need to create a new role for this purpose. References:
• Snowflake Documentation: CREATE MATERIALIZED VIEW
• Snowflake Documentation: Working with Materialized Views
• [Snowflake Documentation: GRANT Privileges on a Schema]

The query is executing a clone operation on an existing tablet_saleswith an offset to account for the retention time. The syntax used is correct for cloning a table in Snowflake, and the use of theat(offset => -60*30)clause is valid. This specifies that the clone should be based on the state of the table 30 minutes prior (60 seconds * 30). Assuming the tablet_salesexists and has been modified within the last 30 minutes, and considering thedata_retention_time_in_daysisset to 1 day (which enables time travel queries for the past 24 hours), the tablet_sales_clonewould be successfully created based on the state oft_sales30 minutes before the clone command was issued.