Utilize Query History and Caching to Reduce Development Time and Query Latency DATA-ANALYST-ASSOCIATE Practice Questions (Page 2)

Questions 6–10

1. 6application · medium

   A data analyst runs a query that returns results from the result cache. However, they know that a critical data update was just completed in the source table. What is the correct way to ensure the next execution of the query reflects the new data?

   • AAdd a comment to the SQL query to force a new cache entry.
   • BUse the 'REFRESH TABLE' command on the source table to invalidate the cache.
   • CManually clear the cluster's disk cache using the REST API before re-running the query.
   • DRun the query again immediately; Databricks automatically invalidates the cache when the underlying table changes.
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2. 7foundation · easy

   A data analyst needs to ensure that a query returns the latest data even if a cached result exists. What should they do?

   • ARun the query from a different notebook
   • BChange the query text slightly, such as adding a comment
   • CForce a refresh by using the 'Clear cache' or 'Refresh' option for the query
   • DWait for the cache to expire automatically
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4. 8foundation · easy

   In Query History, which combination of filters would a data analyst use to find queries that contain the text 'customer' and were run by a specific user?

   • AStatus filter for 'Succeeded' and Time filter for 'Last 7 days'
   • BTime filter for 'Last hour' and Status filter for 'Running'
   • CText search for 'customer' and User filter for that user
   • DUser filter for that user and Status filter for 'Failed'
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5. 9application · medium

   A dashboard is running slowly. In Query History, an analyst notices that a particular query has a high 'Execution Time' but a very low 'Scan Bytes'. What is the most likely performance bottleneck indicated by this metric combination?

   • AThe query is scanning a highly compressed columnar file format.
   • BThe query is performing a large amount of data shuffling or complex joins in memory.
   • CThe query is reading from a slow external storage system.
   • DThe query is hitting the result cache and returning a large result set.
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6. 10expert · hard

   A team has a set of dashboards that run the same underlying queries but with different date range parameters (e.g., 'last 7 days', 'last 30 days'). The queries are slow. What is the most effective way to improve performance using caching?

   • ARely on result caching, which will cache results for each unique parameter combination.
   • BUse a larger cluster to handle the different queries faster.
   • CPre-aggregate the data into a summary table that covers all date ranges.
   • DDisable result caching to avoid stale data across different date ranges.
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