Snowflake Architect Performance and Workload Optimization Flashcards
5 cards from real Snowflake Architect Certification practice questions. Tap to flip, then mark Knew It or Still Learning — missed cards come back until you master them.
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What is the primary purpose of Snowflake’s Automatic Clustering feature?
Answer: To automatically reorganize data for optimal performance
Snowflake's Automatic Clustering feature is designed to automatically reorganize data within micro-partitions to maintain optimal query performance. Over time, DML operations can lead to data being stored in a suboptimal order, reducing the effectiveness of micro-partition pruning. Automatic Clustering continuously reorders data based on clustering keys, ensuring that queries remain efficient by minimizing the amount of data scanned.
Which feature allows Snowflake to automatically scale compute resources based on workload demands?
Answer: Multi-Cluster Warehouses
Multi-Cluster Warehouses are a key feature that allows Snowflake to automatically scale compute resources based on workload demands. When query concurrency or complexity increases, Snowflake can automatically provision additional clusters within a warehouse to handle the load. Conversely, it can suspend idle clusters to save costs, providing elastic and efficient resource management.
What is the primary benefit of using Materialized Views in Snowflake?
Answer: They pre-compute and store query results for faster execution.
The primary benefit of using Materialized Views in Snowflake is to pre-compute and store the results of complex or frequently executed queries. Instead of running the full query every time, Snowflake can quickly retrieve the pre-calculated results from the materialized view. This significantly reduces query execution time and computational cost, especially for analytical workloads on large datasets.
Which query optimization technique is recommended for Snowflake when working with large datasets?
Answer: Leverage column pruning and filter predicates.
When working with large datasets in Snowflake, leveraging column pruning and filter predicates is a highly recommended query optimization technique. Column pruning involves selecting only the necessary columns, reducing the amount of data transferred and processed. Filter predicates (WHERE clauses) allow Snowflake to eliminate irrelevant micro-partitions, significantly reducing the data scanned and improving query performance.
How does Query Caching improve performance in Snowflake?
Answer: By storing query results for reuse without re-execution
Query Caching significantly improves performance in Snowflake by storing the results of previously executed queries. If an identical query is submitted again, Snowflake can serve the results directly from the cache without needing to re-execute the query against the underlying data. This dramatically speeds up response times for repetitive queries and reduces compute costs.