Databricks Certified Data Engineer - Associate
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A data engineering manager has noticed that each of the queries in a Databricks SQL dashboard takes a few minutes to update when they manually click the "Refresh" button. They are curious why this might be occurring, so a team member provides a variety of reasons on why the delay might be occurring.
Which of the following reasons fails to explain why the dashboard might be taking a few minutes to update?
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A
The SQL endpoint being used by each of the queries might need a few minutes to start up.
B
The queries attached to the dashboard might take a few minutes to run under normal circumstances.
Explanation:
Explanation
Correct Answer: A
Why Option A is incorrect (fails to explain the delay):
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SQL Endpoint Startup Time: In Databricks SQL, SQL endpoints (now called SQL warehouses) are designed to be either:
- Always-on: The warehouse stays warm and ready to execute queries immediately
- Auto-scaling: The warehouse can scale down to zero when idle, but when a query comes in, it typically starts up within seconds, not minutes
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Typical Startup Time: While there might be a brief startup delay (usually 30-60 seconds) for a warehouse that has scaled down to zero, this is generally not "a few minutes" and wouldn't explain consistent delays across multiple refreshes.
Why Option B is correct (does explain the delay):
- Query Execution Time: If the queries themselves are complex, process large amounts of data, or involve multiple joins and aggregations, they can legitimately take several minutes to execute.
- Refresh Operation: When you click "Refresh" on a dashboard, all queries are re-executed from scratch, so if each query normally takes a few minutes to run, the dashboard will take that long to update.
- Common Scenario: This is the most likely explanation for dashboard refresh delays - the underlying queries are computationally expensive.
Additional Context:
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Dashboard refresh delays can also be caused by:
- Large data volumes being processed
- Complex transformations in the queries
- Resource contention on the SQL warehouse
- Network latency or data source performance
- Caching configurations
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In Databricks SQL, you can optimize dashboard performance by:
- Using query result caching
- Optimizing SQL queries
- Using appropriate warehouse sizes
- Scheduling periodic refreshes instead of manual refreshes
Key Takeaway: The SQL endpoint startup time is generally not a primary cause of dashboard refresh delays, whereas query execution time is a fundamental and expected reason for such delays.
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