SnowPro-Core VCE Exam Download

To utilize the search optimization service in Snowflake, the correct syntax for granting privileges to a role involves specific commands that include adding search optimization capabilities:

• Option C: GRANT ADD SEARCH OPTIMIZATION ON SCHEMA TO ROLE . This command grants the specified role the ability to implement search optimization at the schema level, which is essential for enhancing search capabilities within that schema.

Options A and B do not include the correct verb "ADD," which is necessary for this specific type of grant command in Snowflake. Option D incorrectly mentions the database level, as search optimization privileges are typically configured at the schema level, not the database level.References: Snowflake documentation on the use of GRANT statements for configuring search optimization.

To obtain the most detailed information about individual table storage details in Snowflake, the TABLE STORAGE METRICS view is the recommended option. This view provides comprehensive metrics on storage usage, including data size, time travel size, fail-safe size, and other relevant storage metrics for each table. This level of detail is invaluable for monitoring, managing, and optimizing storage costs and performance.

References:

• Snowflake Documentation: Information Schema

A materialized view in Snowflake denotes a pre-computed data set derived from a SELECT query specification and stored for later use. Unlike standard views, which dynamically compute the data each time the view is accessed, materialized views store the result of the query at the time it is executed, thereby speeding up access to the data, especially for expensive aggregations on large datasets.

References:

• Snowflake Documentation: Materialized Views

Query pruning in Snowflake is the mechanism used to limit the number of micro-partitions scanned by a query. By analyzing the filters and conditions applied in a query, Snowflake can skip over micro-partitions that do not contain relevant data, thereby reducing the amount of data processed and improving query performance. This technique is particularly effective for large datasets and is a key component of Snowflake's performance optimization features.

References:

• Snowflake Documentation: Query Performance Optimization