What is the primary purpose of Snowflake’s Automatic Clustering feature?
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Automatic Clustering ensures that data is efficiently organized in micro-partitions, optimizing query performance without requiring manual intervention.
Which feature allows Snowflake to automatically scale compute resources based on workload demands?
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Multi-Cluster Warehouses enable Snowflake to automatically add or remove clusters to handle varying workloads, ensuring consistent query performance.
What is the primary benefit of using Materialized Views in Snowflake?
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Materialized Views improve query performance by storing pre-computed results, reducing the need to process large datasets repeatedly.
Which query optimization technique is recommended for Snowflake when working with large datasets?
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Column pruning and filter predicates ensure that Snowflake processes only the necessary columns and rows, improving query efficiency.
How does Query Caching improve performance in Snowflake?
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Query caching stores the results of executed queries, allowing subsequent identical queries to retrieve results instantly without re-processing.