ARA-C01 Exam Dumps : SnowPro Advanced: Architect Certification Exam

 A standard virtual warehouse policy is one of the two scaling policies available for multi-cluster warehouses in Snowflake. The other policy is economic. A standard policy aims to prevent or minimize queuing by starting additional clusters as soon as the current cluster is fully loaded, regardless of the number of queries in the queue. This policy can improve query performance and concurrency, but it may also consume more credits than an economic policy, which tries to conserve credits by keeping the running clusters fully loaded before starting additional clusters. The scaling policy can be set when creating or modifying a warehouse, and it can be changed at any time.

Snowflake Documentation: Multi-cluster Warehouses

Snowflake Documentation: Scaling Policy for Multi-cluster Warehouses

When a new table (table_6) is added to a schema in the provider's account that is part of a data share, the consumer will not automatically see the new table. The consumer will only be able to access the new table once the appropriate privileges are granted by the provider. The correct process, as outlined in option D, involves using the provider’s ACCOUNTADMIN role to grant USAGE privileges on the database and schema, followed by SELECT privileges on the new table, specifically to the share that includes the consumer's database. This ensures that the consumer account can access the new table under the established data sharing setup.

This question tests several core Snowflake semi-structured data concepts that are explicitly part of the SnowPro Architect exam scope: working with VARIANT data, array handling, and the use of LATERAL FLATTEN. The activities element in the JSON structure is an array, meaning it must be flattened before individual attributes such as activityNumber and winner can be accessed. Option A is invalid because it attempts to directly reference fields inside an array without flattening it.

Option B correctly uses LATERAL FLATTEN on json:activities, which produces one row per activity. The alias p.value represents each array element, allowing access to activityNumber and winner. The IFF expression then compares the activity’s winner value with leader.number. When they match, the query returns leader.name.default; otherwise, it returns follower.name.default. Casting the result to VARCHAR ensures a proper scalar output.

Option C is incorrect because it attempts to return full JSON objects (leader or follower) rather than the nested name.default value. Option D uses OUTER => TRUE, which is unnecessary in this case because the activities array is guaranteed to exist; while it would still work, Snowflake exam questions typically expect the most precise and minimal correct solution.