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What is the main advantage of using Materialized Views in Snowflake?
They always contain the latest data in real-time.
They can be directly updated by DML statements.
They physically store pre-computed results to improve query performance.
They are primarily used for data masking.
Snowflake Materialized Views physically store the results of a defined query. This pre-computation significantly improves performance for repetitive or complex queries, especially those based on aggregations and joins. Snowflake automatically maintains materialized views asynchronously. They do not update in real-time, cannot be changed through DML, and are not intended for masking—dynamic masking policies serve that purpose.
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How can Snowsight be used to monitor the performance of a virtual warehouse?
Use the SHOW WAREHOUSES command.
Use the LOAD_HISTORY view.
Use the DESCRIBE WAREHOUSE command.
Use the QUERY_HISTORY view.
To monitor virtual warehouse performance in Snowsight, the correct approach is using theQUERY_HISTORYview or its Snowsight equivalent under the Activity section. QUERY_HISTORY provides detailed metrics on execution time, queueing, credit consumption, compilation time, and warehouse utilization for every executed query. By analyzing this information, users can diagnose issues such as concurrency bottlenecks, under-sized warehouses, long-running queries, or excessive I/O.
SHOW WAREHOUSES returns metadata about warehouse configurations (size, state, scaling policy) but does not provide performance data. LOAD_HISTORY tracks COPY INTO operations only and is not representative of general warehouse workload. DESCRIBE WAREHOUSE shows configuration details but no runtime or historical performance metrics.
Thus, the most accurate and complete way to assess warehouse performance—particularly execution delays, queuing, or excessive credit usage—is by using QUERY_HISTORY.
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What is a benefit of using an external stage to load data into Snowflake?
External stages reduce data storage costs because data is stored outside Snowflake.
External stages provide automatic data purging after successful loads.
External stages are more secure than internal stages for sensitive data.
External stages reduce the number of objects in a database.
External stages point to files in external cloud storage (S3, Azure Blob, GCS). Because the data is not stored inside Snowflake, the user avoids Snowflake storage charges, which can significantly reduce cost for large staging datasets.
External stages do not automatically delete files, are not inherently more secure than internal stages, and still count as database objects even though they reference external storage.
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What is the primary purpose of the COPY INTO command in Snowflake for data loading?
To replicate databases across regions.
To create new virtual warehouses.
To load data from staged files into a table.
To manage user roles and permissions.
COPY INTO
