DEA-C02 Exam Dumps | You have a data pipeline that aggregates web server logs hourly. The pipeline loads data into a Snowflake

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Question 112/149

You have a data pipeline that aggregates web server logs hourly. The pipeline loads data into a Snowflake table 'WEB LOGS' which is partitioned by 'event_time'. You notice that queries against this table are slow, especially those that filter on specific time ranges. Analyze the following Snowflake table definition and query pattern and select the options to diagnose and fix the performance issue: Table Definition:

A. The table is already partitioned by 'event_time' , so there is no need for further optimization.

B. Change the table to use clustering on 'event_time' instead of partitioning to improve query performance for range filters.

C. Create a materialized view that pre-aggregates the 'status_code' by hour to speed up the aggregation query.

D. Increase the warehouse size to improve query performance.

E. Add a search optimization strategy to the table on the 'event_time' column.

Correct Answer: B,C,E

Partitioning in Snowflake is primarily for data management and micro-partition elimination on exact matches, not range queries. Clustering (B) reorders the data for better performance with range-based queries. A materialized view (C) pre-computes the aggregation, significantly speeding up the specific query. A search optimization strategy (E) can improve performance without requiring a full table scan. Increasing warehouse size (D) may help to some extent but is not the most targeted optimization. Option A is incorrect because partitioning alone doesn't solve the range query performance issue.

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