Data Transformations

The correct approach is to use a lateral join with a second FLATTEN function targeting the special_requests array from the first FLATTEN's VALUE output.

When dealing with nested arrays in Snowflake's semi-structured data, you need to FLATTEN each level of nesting separately. The first FLATTEN expands the 'room_allocations' array, giving you one row per room allocation. However, the 'special_requests' array within 'guest_preferences' remains as a single VARIANT array value. To extract individual special request strings, you need a second FLATTEN operation that targets this nested array.

The query structure would look like:

SELECT
ra.VALUE:room_number AS room_number,
sr.VALUE::STRING AS special_request
FROM reservations,
LATERAL FLATTEN(input => booking_details:room_allocations) ra,
LATERAL FLATTEN(input => ra.VALUE:guest_preferences:special_requests) sr

This creates a row for each individual special request while maintaining the relationship to its parent room allocation.

The option suggesting RECURSIVE parameter is incorrect because FLATTEN does not have a RECURSIVE parameter that automatically expands all nested arrays - this parameter exists for the RECURSIVE CTE construct but not for FLATTEN.

The option suggesting FLATTEN on guest_preferences object is incorrect because guest_preferences is an object (not an array), and flattening an object would give you key-value pairs of the object's properties (bed_type, floor_level, special_requests as keys), not the individual elements within the special_requests array.

The option suggesting PATH set to 'room_allocations.guest_preferences.special_requests' is incorrect because the PATH parameter in FLATTEN specifies a path within each array element being flattened, not a way to navigate through multiple nesting levels. FLATTEN operates on one array at a time, so you cannot use PATH to traverse through multiple array levels in a single FLATTEN call.

The correct approach is to query the TASK_HISTORY table function and use the GRAPH_RUN_GROUP_ID to correlate all task executions within the same DAG run.

The TASK_HISTORY table function is available in both INFORMATION_SCHEMA and ACCOUNT_USAGE schemas and provides comprehensive execution history for tasks. The key feature for monitoring DAG executions is the GRAPH_RUN_GROUP_ID column, which is a unique identifier assigned to all tasks that execute as part of the same DAG run. This allows you to:

1. Track which tasks completed, failed, or are still running within a single DAG execution
2. Correlate parent and child task executions automatically
3. Monitor the entire task tree from root to leaf tasks
4. Identify bottlenecks or failures at any point in the hierarchy

By filtering on the root task name and grouping by GRAPH_RUN_GROUP_ID, you can see the complete picture of each DAG run including all six tasks in the hierarchy.

The second option of querying each task separately and manually correlating by timestamps is error-prone and inefficient. Task execution times can vary, making timestamp-based correlation unreliable, especially with parallel branches like TASK_PREPAID_BILLING and TASK_POSTPAID_BILLING.

The third option incorrectly describes the purpose of SYSTEM$TASK_DEPENDENTS_ENABLE. This system function is used to recursively resume all dependent tasks in a DAG - it does not retrieve execution metrics or historical data.

The fourth option of querying QUERY_HISTORY filtered by warehouse is inadequate because it doesn't provide task-specific metadata like task names, run status, scheduled times, or the critical GRAPH_RUN_GROUP_ID needed to correlate DAG runs. It would also include non-task queries that happen to use the same warehouse.

The EXECUTE AS clause in a Snowflake stored procedure definition determines whether the procedure executes using the privileges of the procedure owner or the calling user.

In Snowflake, the EXECUTE AS clause has two options:
- EXECUTE AS OWNER (default): The procedure runs with the privileges of the user who owns the procedure. This is known as 'owner's rights' and allows the procedure to access objects that the owner has privileges on, regardless of the caller's privileges.
- EXECUTE AS CALLER: The procedure runs with the privileges of the user who calls the procedure. This is known as 'caller's rights' and restricts the procedure to only accessing objects that the calling user has privileges on.

The other answers are incorrect:

• The RETURNS clause specifies the data type of the value returned by the procedure. It has nothing to do with privilege inheritance or security context.

• The LANGUAGE clause specifies the programming language used to write the procedure (such as JavaScript, Python, Java, or SQL). It does not establish any authorization framework or permission settings.

• The CALLED ON NULL INPUT clause determines how the procedure behaves when passed NULL arguments. It controls whether the procedure is called or returns NULL automatically when NULL inputs are provided. It has no relation to privilege escalation or security behavior.