Snowflake ADA-C01 Practice Test - Questions Answers, Page 2

According to the Snowflake documentation1, stages without credentials are a way to create external stages that use storage integrations to access data files in cloud storage without providing any credentials to Snowflake. Storage integrations are objects that define a trust relationship between Snowflake and a cloud provider, allowing Snowflake to authenticate and authorize access to the cloud storage. To limit data exfiltration after a storage integration and associated stages are created, the following account-level parameters can be set:

* REQUIRE_STORAGE_INTEGRATION_FOR_STAGE_CREATION: This parameter enforces that all external stages must be created using a storage integration. This prevents users from creating external stages with inline credentials or URLs that point to unauthorized locations.

* REQUIRE_STORAGE_INTEGRATION_FOR_STAGE_OPERATION: This parameter enforces that all operations on external stages, such as PUT, GET, COPY, and LIST, must use a storage integration. This prevents users from performing operations on external stages with inline credentials or URLs that point to unauthorized locations.

* PREVENT_UNLOAD_TO_INLINE_URL: This parameter prevents users from unloading data from Snowflake tables to inline URLs that do not use a storage integration. This prevents users from exporting data to unauthorized locations.

Therefore, the correct answer is option D, which sets all these parameters to true. Option A is incorrect because it sets PREVENT_UNLOAD_TO_INLINE_URL to false, which allows users to unload data to inline URLs that do not use a storage integration. Option B is incorrect because it sets both REQUIRE_STORAGE_INTEGRATION_FOR_STAGE_CREATION and REQUIRE_STORAGE_INTEGRATION_FOR_STAGE_OPERATION to false, which allows users to create and operate on external stages without using a storage integration. Option C is incorrect because it sets all the parameters to false, which does not enforce any restrictions on data exfiltration.

According to the Snowflake documentation1, a multi-cluster warehouse is a virtual warehouse that consists of multiple clusters of compute resources that can scale up or down automatically to handle the concurrency and performance needs of the queries submitted to the warehouse. A multi-cluster warehouse has a minimum and maximum number of clusters that can be specified by the administrator. Option A is a possible solution to address the issues happening once a month, as it allows the administrator to use a task to increase the cluster size for the time period that the more complex queries are running and another task to reduce the size of the cluster once the complex queries complete. This way, the warehouse can have more resources available to handle the complex queries without reaching the maximum limit of 10 clusters, and then return to the normal cluster size to save costs. Option B is another possible solution to address the issues happening once a month, as it allows the administrator to have the group running the complex monthly queries use a separate appropriately-sized warehouse to support their workload. This way, the warehouse can isolate the complex queries from the standard workload and avoid queue time and resource contention. Option C is not a recommended solution to address the issues happening once a month, as it would increase the costs and complexity of managing the multi-cluster warehouse, and may not solve the underlying problem of inefficient queries. Option D is a good practice to improve the performance of the queries, but it is not a direct solution to address the issues happening once a month, as it requires analyzing and optimizing the complex queries using clustering keys or materialized views, which may not be feasible or effective in all cases. Option E is not a recommended solution to address the issues happening once a month, as it would increase the costs and waste resources by starting more clusters than needed for the standard workload.