Utilize Query History and Caching to Reduce Development Time and Query Latency DATA-ANALYST-ASSOCIATE Practice Questions (Page 4)

Questions 16–20

1. 16foundation · easy

   In Query History, which metric indicates the amount of data read from storage during query execution?

   • AResult size
   • BScan bytes
   • CRows returned
   • DExecution time
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2. 17application · medium

   A data analyst is investigating a sudden slowdown in a nightly reporting dashboard. They suspect a specific colleague's queries are consuming excessive cluster resources. In the Databricks Query History interface, which sequence of actions most directly isolates the problematic queries?

   • ASort by 'Scan bytes' descending and look for queries with unusually high data scans.
   • BFilter by date range for the last 24 hours and export the full query history to CSV for manual review.
   • CFilter by the colleague's username, then sort by execution time descending and review the top results.
   • DSearch for the dashboard name in the query text, then filter by status 'Failed'.
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4. 18expert · hard

   A team runs a complex SQL query that joins multiple large tables. The query is run by different analysts with slightly different formatting (e.g., different whitespace or capitalization). The query is slow and they want to leverage result caching. What is the most effective approach?

   • AAsk analysts to run the query on the same cluster to benefit from disk caching.
   • BStandardize the query text by creating a shared SQL file or view that all analysts use.
   • CIncrease the cluster size to make the query faster, regardless of caching.
   • DUse a lower-level API that bypasses the query text check for caching.
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5. 19expert · hard

   A team is migrating from a legacy BI tool to Databricks SQL. They want to identify which of their existing SQL queries are most resource-intensive to prioritize optimization efforts. They have a list of query texts from the legacy tool. What is the most efficient way to assess these queries in Databricks?

   • AAsk the legacy BI tool administrator for its query performance logs.
   • BRun each query once in Databricks SQL and then sort by 'Scan Bytes' in Query History.
   • CManually review each query's complexity and estimate its cost based on the number of joins.
   • DUse the Query History search to find similar queries that have already been run.
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6. 20foundation · easy

   What is a potential cost benefit of using result caching for repeated queries?

   • AElimination of all compute costs for the workspace
   • BNo cost benefit; caching only improves speed
   • CReduced storage costs because results are compressed
   • DReduced compute usage because the query does not need to be re-executed each time
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