Databricks Databricks-Certified-Data-Analyst-Associate Exam With Confidence Using Practice Dumps

 Databricks SQL uses a query cache to store the results of queries that have been executed previously. This improves the performance and efficiency of repeated queries. To determine if a query result was pulled from the cache, you can go to the Query History tab in the Databricks SQL UI and click on the text of the query. A slideout will appear on the right side of the screen, showing the query details, including the cache status. If the result came from the cache, the cache status will show “Cached”. If the result did not come from the cache, the cache status will show “Not cached”. You can also see the cache hit ratio, which is the percentage of queries that were served from the cache. References: The answer can be verified from Databricks SQL documentation which provides information on how to use the query cache and how to check the cache status. Reference link: Databricks SQL - Query Cache

Query History in Databricks SQL is intended for reviewing executed queries, understanding their execution plans, and identifying performance issues or errors for debugging purposes. It allows users to analyze query duration, resources used, and potential bottlenecks. However, Query History does not provide any capability to automate the execution of queries across multiple warehouses; automation must be handled through jobs or external orchestration tools, not through the Query History feature itself.

The correct query to get the percentage rank of products within region by the sales is option B. This query uses the PERCENT_RANK() window function to calculate the relative rank of each product within each region based on the sales amount. The window function is partitioned by region and ordered by sales in descending order. The result is aliased as rank and displayed along with the region and product columns. The other options are incorrect because:

A. Option A uses the RANK() window function instead of the PERCENT_RANK() function. The RANK() function returns the rank of each row within the partition, but not the percentage rank. Also, the query does not have a GROUP BY clause, which is required for aggregate functions like SUM().

C. Option C uses the DENSE_RANK() window function instead of the PERCENT_RANK() function. The DENSE_RANK() function returns the rank of each row within the partition, but not the percentage rank. Also, the query does not have a GROUP BY clause, which is required for aggregate functions like SUM().

D. Option D uses the ROW_NUMBER() window function instead of the PERCENT_RANK() function. The ROW_NUMBER() function returns the sequential number of each row within the partition, but not the percentage rank. Also, the query does not have a GROUP BY clause, which is required for aggregate functions like SUM(). References:

1: PERCENT_RANK (Transact-SQL)

2: Window functions in Databricks SQL

3: Databricks Certified Data Analyst Associate Exam Guide