What parameter triggers a state change in the datagroup if its value differs from prior results?

What parameter triggers a state change in the datagroup if its value differs from prior results?

• A. data_trigger
• B. sql_trigger
• C. default_trigger
• D. cache_trigger

Answer: (B)

The parameter that triggers a state change in the datagroup when its value differs from prior results is the SQL trigger. SQL triggers are used to execute a specific SQL statement or set of statements automatically in response to certain events, such as changes in data. In the context of Looker, when the SQL query associated with the SQL trigger returns a different value than the previous executions, it indicates that the underlying data has changed, prompting Looker to update its cache and refresh the data group. This mechanism is crucial for maintaining accurate and up-to-date insights within reports and dashboards, as it ensures that any modifications in the underlying data source are reflected promptly. The SQL trigger effectively monitors the data conditions and signals the datagroup to take action when necessary. The other options—data_trigger, default_trigger, and cache_trigger—are either not standard Looker parameters or serve different purposes within the LookML framework. Therefore, SQL trigger stands out as the correct option for monitoring data changes and triggering state changes in the datagroup.

The parameter that triggers a state change in the datagroup when its value differs from prior results is the SQL trigger. SQL triggers are used to execute a specific SQL statement or set of statements automatically in response to certain events, such as changes in data. In the context of Looker, when the SQL query associated with the SQL trigger returns a different value than the previous executions, it indicates that the underlying data has changed, prompting Looker to update its cache and refresh the data group.

This mechanism is crucial for maintaining accurate and up-to-date insights within reports and dashboards, as it ensures that any modifications in the underlying data source are reflected promptly. The SQL trigger effectively monitors the data conditions and signals the datagroup to take action when necessary.

The other options—data_trigger, default_trigger, and cache_trigger—are either not standard Looker parameters or serve different purposes within the LookML framework. Therefore, SQL trigger stands out as the correct option for monitoring data changes and triggering state changes in the datagroup.