Exam DEA-C02 Topic 1 Question 58 Discussion

Actual exam question for Snowflake's DEA-C02 exam
Question #: 58
Topic #: 1

Consider a table 'EVENT DATA' that stores events from various applications. The table has columns like 'EVENT ID, 'EVENT TIMESTAMP, 'APPLICATION ID', 'USER ID', and 'EVENT _ TYPE. A significant portion of queries filter on 'EVENT TIMESTAMP ranges AND 'APPLICATION ID. The data volume is substantial, and query performance is crucial. You observe high clustering depth after initial loading. Which combination of actions will provide the MOST effective performance optimization, addressing both clustering depth and query performance?

A. Cluster the table on 'EVENT TIMESTAMP' and periodically run 'OPTIMIZE TABLE EVENT DATA' using a small warehouse. Also, create a separate table clustered on 'APPLICATION B. Cluster the table on '(EVENT TIMESTAMP, APPLICATION IDY and periodically run 'OPTIMIZE TABLE EVENT DATA' using a warehouse sized appropriately for the table size. Then, monitor clustering depth regularly. C. Cluster the table on 'USER ICY and rely solely on Snowflake's automatic reclustering feature, without running 'OPTIMIZE TABLES manually. D. Create separate tables for each ' , each clustered on 'EVENT_TIMESTAMP'. Then, create a view that UNION ALLs these tables. E. Create multiple materialized views: one filtering on common 'EVENT TIMESTAMP' ranges, and another filtering on common 'APPLICATION ID' values.

Suggested Answer: B Vote an answer

Clustering on '(EVENT _ TIMESTAMP, APPLICATION_ID)' directly addresses the common query patterns. Regularly running 'OPTIMIZE TABLE EVENT DATA' with an appropriately sized warehouse ensures the data remains well-clustered as new data is added, reducing clustering depth and maintaining performance. Monitoring clustering depth is essential to identify when reclustering is needed. Clustering on a single dimension like 'USER IDS (C) doesn't address the primary query patterns. Creating separate tables (A, D) introduces complexity and management overhead. Materialized views (E) are helpful for specific pre-aggregated results, but clustering optimizes the base table for a wider range of queries. Optimizing with the right sized warehouse is crucial, a small warehouse might take an extremely long time.

by Payne at Nov 23, 2025, 02:04 AM

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