Free Snowflake ARA-R01 Exam Dumps Questions & Answers

A company is following the Data Mesh principles, including domain separation, and chose one Snowflake account for its data platform.
An Architect created two data domains to produce two data products. The Architect needs a third data domain that will use both of the data products to create an aggregate data product. The read access to the data products will be granted through a separate role.
Based on the Data Mesh principles, how should the third domain be configured to create the aggregate product if it has been granted the two read roles?

• A.Use secondary roles for all users.
• B.Create a hierarchy between the two read roles.
• C.Request that the two data domains share data using the Data Exchange.
• D.Request a technical ETL user with the sysadmin role.

A company has a source system that provides JSON records for various loT operations. The JSON Is loading directly into a persistent table with a variant field. The data Is quickly growing to 100s of millions of records and performance to becoming an issue. There is a generic access pattern that Is used to filter on the create_date key within the variant field.
What can be done to improve performance?

• A.Alter the target table to Include additional fields pulled from the JSON records. This would Include a create_date field with a datatype of time stamp. When this field Is used in the filter, partition pruning will occur.
• B.Alter the target table to include additional fields pulled from the JSON records. This would include a create_date field with a datatype of varchar. When this field is used in the filter, partition pruning will occur.
• C.Validate the size of the warehouse being used. If the record count is approaching 100s of millions, size XL will be the minimum size required to process this amount of data.
• D.Incorporate the use of multiple tables partitioned by date ranges. When a user or process needs to query a particular date range, ensure the appropriate base table Is used.

How can an Architect enable optimal clustering to enhance performance for different access paths on a given table?

• A.Create super projections that will automatically create clustering.
• B.Create multiple materialized views with different cluster keys.
• C.Create multiple clustering keys for a table.
• D.Create a clustering key that contains all columns used in the access paths.

What is a characteristic of Role-Based Access Control (RBAC) as used in Snowflake?

• A.A user can create managed access schemas to support future grants and ensure only schema owners can grant privileges to other roles.
• B.A user can use a "super-user" access along with securityadmin to bypass authorization checks and access all databases, schemas, and underlying objects.
• C.Privileges can be granted at the database level and can be inherited by all underlying objects.
• D.A user can create managed access schemas to support current and future grants and ensure only object owners can grant privileges to other roles.

A company is designing a process for importing a large amount of loT JSON data from cloud storage into Snowflake. New sets of loT data get generated and uploaded approximately every 5 minutes.
Once the loT data is in Snowflake, the company needs up-to-date information from an external vendor to join to the data. This data is then presented to users through a dashboard that shows different levels of aggregation.
The external vendor is a Snowflake customer.
What solution will MINIMIZE complexity and MAXIMIZE performance?

• A.1. Create an external table over the JSON data in cloud storage.
  2. Create a task that runs every 5 minutes to run a transformation procedure on new data, based on a saved timestamp.
  3. Ask the vendor to expose an API so an external function can be used to generate a call to join the data back to the loT data in the transformation procedure.
  4. Give the transformed table access to the dashboard tool.
  5. Perform the aggregations on the dashboard tool.
• B.1. Create a Snowpipe to bring the JSON data into Snowflake.
  2. Use streams and tasks to trigger a transformation procedure when new JSON data arrives.
  3. Ask the vendor to create a data share with the required data that is then imported into the Snowflake account.
  4. Join the vendor's data back to the loT data in a transformation procedure
  5. Create materialized views over the larger dataset to perform the aggregations required by the dashboard.
  6. Give the materialized views access to the dashboard tool.
• C.1. Create an external table over the JSON data in cloud storage.
  2. Create a task that runs every 5 minutes to run a transformation procedure on new data based on a saved timestamp.
  3. Ask the vendor to create a data share with the required data that can be imported into the company's Snowflake account.
  4. Join the vendor's data back to the loT data using a transformation procedure.
  5. Create views over the larger dataset to perform the aggregations required by the dashboard.
  6. Give the views access to the dashboard tool.
• D.1. Create a Snowpipe to bring the JSON data into Snowflake.
  2. Use streams and tasks to trigger a transformation procedure when new JSON data arrives.
  3. Ask the vendor to expose an API so an external function call can be made to join the vendor's data back to the loT data in a transformation procedure.
  4. Create materialized views over the larger dataset to perform the aggregations required by the dashboard.
  5. Give the materialized views access to the dashboard tool.