Utilize Query History and Caching to Reduce Development Time and Query Latency DATA-ANALYST-ASSOCIATE Practice Questions (Page 1)

Questions 1–5

1. 1expert · hard

   An analyst is comparing two queries in Query History. Query A has an execution time of 10 seconds and scan bytes of 1 GB. Query B has an execution time of 5 seconds and scan bytes of 10 GB. Which query is more likely to benefit from adding a filter on a highly selective column?

   • ANeither query, since filters only affect execution time, not scan bytes.
   • BQuery B, because its high scan bytes suggest it's reading a lot of unnecessary data.
   • CQuery A, because its lower scan bytes indicate it's already well-optimized.
   • DBoth queries equally, since filters always improve performance.
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2. 2foundation · easy

   Which approach in Query History would best help a data analyst identify a slow-running query that needs optimization?

   • AFilter queries by status 'Succeeded' only
   • BSort queries by execution time and review those with the longest durations
   • CSort queries by result size and review those with the largest results
   • DFilter queries by user to see only your own queries
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4. 3application · medium

   An analyst runs a query and gets results instantly, but suspects the results are stale because a data pipeline just finished updating the source table. What is the most reliable way to verify if the results came from the cache?

   • ACheck the query text for the 'CACHE' keyword.
   • BCheck the query's metrics in Query History; a cache hit will show minimal scan bytes and execution time.
   • CLook at the cluster's Spark UI to see if the query was skipped.
   • DRe-run the query and compare the results to the previous run.
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5. 4foundation · easy

   How can a data analyst benefit from result caching when running the same dashboard query multiple times?

   • AThe query will return results faster on subsequent runs because the cached result is reused
   • BThe query will use less memory on the cluster
   • CThe query will automatically be rewritten to a more efficient form
   • DThe query will always return the most up-to-date data regardless of changes
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6. 5foundation · easy

   Which practice is most effective for reducing development time when iterating on a SQL query in Databricks?

   • AAvoiding the use of caching to ensure fresh results
   • BRewriting the query from scratch each time
   • CUsing Query History to review and reuse previous query versions
   • DRunning the query on a larger cluster to speed up execution
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