A data engineer is using the Snowflake web UI to execute a complex SQL query involving multiple joins and aggregations. The query is running slower than expected. What are the most efficient steps the

Question

Options

• AUse the 'Profile' option in the worksheet to examine the query execution plan and identify
• BIncrease the warehouse size using the drop-down menu in the worksheet and rerun the query.
• CEnable caching for the query result by setting the parameter to TRUE in the worksheet.
• DDownload the query execution plan as a JSON file for offline analysis using external tools.
• EReview the Query History page in the web UI to analyze query statistics (e.g., execution time,

• A
  77% (27)
• C
  17% (6)
• D
  6% (2)

Explanation

These three options represent the legitimate in-UI diagnostic and optimization workflow. Option A (Query Profile) is Snowflake's primary performance diagnostic tool-it shows the query execution plan as an operator tree, highlights the most expensive steps (e.g., full table scans, large joins), and identifies bottlenecks. Option B (increasing warehouse size) adds more compute resources, which can reduce execution time for compute-bound queries and is directly actionable from the UI. Option E (Query History) provides aggregate statistics-execution time, bytes scanned, rows produced-that help confirm whether the query is consistently slow and identify patterns. Option C is misleading: Snowflake result caching (USE_CACHED_RESULT) is enabled by default and doesn't need to be set manually for basic use; it also doesn't help with a slow-running query itself. Option D is not a standard Snowflake UI export feature for execution plans.

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