I agree with C & D.
https://docs.snowflake.com/en/user-guide/views-materialized.html#effects-of-changes-to-base-tables-on-materialized-views
Answer-c --> When a base table changes, all materialized views defined on the table are updated by a background service that uses compute resources provided by Snowflake.
https://docs.snowflake.com/en/user-guide/views-materialized#label-materialized-views-maintenance-billing
Answer CD as per the evidence in the link, user queries does not come under maintenance
C and E
Materialized views impact your costs for both storage and compute resources:
Storage: Each materialized view stores query results, which adds to the monthly storage usage for your account.
Compute resources: In order to prevent materialized views from becoming out-of-date, Snowflake performs automatic background maintenance of materialized views. When a base table changes, all materialized views defined on the table are updated by a background service that uses compute resources provided by Snowflake.
This one make more sense
I found this in Snowflake Doc :
Materialized Views and Clustering
Defining a clustering key on a materialized view is supported and can increase performance in many situations. However, it also adds costs.
https://docs.snowflake.com/en/user-guide/views-materialized#label-materialized-views-maintenance-billing
C and E
Materialized views impact your costs for both storage and compute resources:
Storage: Each materialized view stores query results, which adds to the monthly storage usage for your account.
Compute resources: In order to prevent materialized views from becoming out-of-date, Snowflake performs automatic background maintenance of materialized views. When a base table changes, all materialized views defined on the table are updated by a background service that uses compute resources provided by Snowflake.
I would rather go with the highly voted answers (C&D) and not the key provided (D&E) based on the Snowflake Guide: https://docs.snowflake.com/en/user-guide/views-materialized.html#effects-of-changes-to-base-tables-on-materialized-views
In general, the costs are proportional to:
The number of materialized views created on each base table, and the amount of data that changes in each of those materialized views when the base table changes. Any changes to micro-partitions in the base table require eventual materialized view maintenance, whether those changes are due to reclustering or DML statements run on the base table.
The number of those materialized views that are clustered. Maintaining clustering (of either a table or a materialized view) adds costs.
https://docs.snowflake.com/en/user-guide/views-materialized.html#effects-of-changes-to-base-tables-on-materialized-views
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