LookML-Developer 무료 덤프문제 온라인 액세스

The developer has moved the orders Explore (shown below) from model_a to model_b, where both models are in the same project, and all users have access to both models.
Connection: "demo"
include: ".view"
explore: orders {}
What will happen after making this change?

• A.Dashboard tiles and Looks that rely on this Explore will be deleted.
• B.Dashboard tiles and Looks will be automatically pointed to the orders Explore in model_b.
• C.Dashboard tiles and Looks that rely on this Explore will return an error.
• D.Dashboard tiles and Looks will redirect to the new database connection.

Users report that every time they change the filter on their Explore, the filters take a very long time to populate.
How can the developer improve the filtering experience with this Explore?

• A.Limit the filter suggestions using the suggestions parameter.
• B.Add an always_filter parameter to restrict the filter suggestions.
• C.Add persistence to the base view of the Explore.
• D.Use an access_filter parameter to automatically apply filters.

A developer commits changes after adding LookML for a new measure. Upon pulling from production, the developer notices the following lines in the LookML:

• A.Remove "<<<<<< HEAD", "======", and everything following "======"
• B.Remove everything between "<<<<<< HEAD" and ">>>>>> branch 'master'"
• C.Remove everything between "<<<<<< HEAD" and "======", and ">>>>>> branch 'master'"
• D.Remove "<<<<<< HEAD", "======", and ">>>>>> branch 'master'"

Users have built a popular dashboard and want to have change management built in for any edits made to the dashboard. The developer sets up version control for the model on which the dashboard is based.
What should the developer build to meet the business requirement?

• A.A link to the dashboard included in the project.
• B.An Explore LookML file based on the dashboard.
• C.A dashboard LookML file included in the project.
• D.A native derived table based on the dashboard.

A developer needs to implement three persistent derived tables (PDTs) as described below.
The PDTs need to be refreshed after the daily ETL pipeline adds incremental loads to the underlying tables.
Each PDT is built off of one underlying table in the database (one PDT per table).
The underlying tables for each PDT are updated one after the other, and a new row is added to an ETL log table each time a table is updated.
Due to the unpredictable nature of the ETL pipeline, each PDT does not refresh at the same time from day to day.
Each PDT takes over an hour to build, and to save on compute costs each PDT should only be refreshed once per day.
How can the developer set up the PDTs according to these requirements?

• A.Create one datagroup tied to all three PDTs that runs when the total row count across all three tables changes.
• B.Create three separate datagroups tied to three PDTs that run when each corresponding table's row count changes.
• C.Create three separate datagroups tied to three PDTs that run when a new row is added to the ETL log table.
• D.Create one datagroup tied to all three PDTs that parameterizes the view name for each PDT in the SQL trigger condition.