Snowflake ARA-C01 Exam With Confidence Using Practice Dumps

In Snowflake, a stream records data manipulation language (DML) changes to its base table since the stream was created or last consumed. STREAM_1 will show all changes including the TRUNCATE operation, while STREAM_2, being APPEND_ONLY, will not show deletions like TRUNCATE. Therefore, STREAM_1 will count the three inserts, the TRUNCATE (counted as a single operation), and the subsequent two inserts before the delete, totaling 4. STREAM_2 will only count the three initial inserts and the two after the TRUNCATE, totaling 3, as it does not count the TRUNCATE or the delete operation.

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.

 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