What impacts the credit consumption of maintaining a materialized view? (Choose two.)
What impacts the credit consumption of maintaining a materialized view? (Choose two.)
The credit consumption for maintaining a materialized view is influenced by how often the base table changes and whether the materialized view has a cluster key defined. Changes to the base table require the materialized view to be updated, consuming compute resources. Additionally, defining a clustering key on a materialized view increases performance but adds to maintenance costs.
More changes to base table increases credit consumption for materialized view maintenance
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.
Agree it's C & D
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 Compute resources: In order to prevent materialized views from becoming out-of-date, Snowflake performs automatic background maintenance of materialized views.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.
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
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
It's C&D https://docs.snowflake.com/en/user-guide/views-materialized.html#label-materialized-views-maintenance-billing
CD are correct
CE make more sense .howdoes clustering key play role in pre computed query
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
CORRECT
C and D (https://www.snowflake.com/blog/using-materialized-views-to-solve-multi-clustering-performance-problems/)
Why not E?
Same question here. Compute Credit should be consume, right?
The question is about maintaining the view. Querying is not maintaining.
This because al tables are clustered on Snowflake, there is no mention of re-clustering.
Agree with C and D: https://docs.snowflake.com/en/user-guide/views-materialized#label-materialized-views-maintenance-billing
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
It is C and D for this.
clustering and base table modifications ratio