At a minimum, you must specify four values when creating a new cluster with the projects.regions.clusters.create operation:
The project in which the cluster will be created
The region to use
The name of the cluster
The zone in which the cluster will be created
You can specify many more details beyond these minimum requirements. For example, you can also specify the number of workers, whether preemptible compute should be used, and the network settings.

To improve the performance of querying a large BigQuery table with filters on countryname and username, clustering the table by these fields is the most effective approach. Here's why option C is the best choice:
Clustering in BigQuery:
Clustering organizes data based on the values in specified columns. This can significantly improve query performance by reducing the amount of data scanned during query execution.
Clustering by countryname and username means that data is physically sorted and stored together based on these fields, allowing BigQuery to quickly locate and read only the relevant data for queries using these filters.
Filter Efficiency:
With the table clustered by countryname and username, queries that filter on these columns can benefit from efficient data retrieval, reducing the amount of data processed and speeding up query execution.
This directly addresses the performance issue of the dashboard queries that apply filters on these fields.
Steps to Implement:
Redesign the Table:
Create a new table with clustering on countryname and username:
CREATE TABLE project.dataset.new_table
CLUSTER BY countryname, username AS
SELECT * FROM project.dataset.customer_order;
Migrate Data:
Transfer the existing data from the original table to the new clustered table.
Update Queries:
Modify the dashboard queries to reference the new clustered table.
Reference:
BigQuery Clustering Documentation
Optimizing Query Performance

Reference https://cloud.google.com/bigquery/docs/gis-dataa