Databricks-DA-Assoc Creating Dashboards and Visualizations Practice Question
Which TWO of the following are valid ways to improve the performance of a slow-loading Databricks SQL dashboard? (Choose two)
⚠ Common exam trap
Candidates often rely solely on upgrading cluster hardware instead of implementing built-in caching and query-level optimizations.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Enable query result caching on the SQL warehouse.
Improving dashboard performance is critical for user adoption. By utilizing query result caching and optimizing the underlying SQL queries, analysts can significantly reduce latency. Caching stores the output of frequent queries, while query optimization ensures that the compute resources are used efficiently. These techniques are standard operational practices that ensure dashboard responsiveness, even when dealing with massive datasets common in modern data lakehouses.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Convert all visualizations into a single complex query.
Why it's wrong here
Combining all visualizations into one complex query often creates a bottleneck. If one part of the query is slow, the entire dashboard stalls. It is generally better to use separate, optimized queries for each visualization so that the SQL warehouse can execute them in parallel and cache them individually.
- ✓
Enable query result caching on the SQL warehouse.
Why this is correct
Query result caching stores the results of queries so that subsequent executions of the same query retrieve the data directly from the cache. This bypasses the need for the warehouse to re-read and re-process the underlying data, resulting in near-instant load times for repeated dashboard views.
- ✗
Add more users to the dashboard to increase processing power.
Why it's wrong here
Adding more users does not increase the processing power or compute resources assigned to a SQL warehouse. In fact, increasing user concurrency without scaling the warehouse size can lead to resource contention, causing the queries to run slower due to wait times within the serverless or pro warehouse environment.
- ✓
Optimize the underlying SQL queries using filters and aggregates.
Why this is correct
Optimizing queries by pushing down filters and pre-aggregating data ensures that the SQL warehouse processes only the necessary information. This reduces the amount of data scanned and transferred, minimizing compute time and memory usage, which directly leads to faster rendering of dashboard visualizations for the end-users.
- ✗
Switch the dashboard to manual refresh mode only.
Why it's wrong here
While manual refresh prevents background processing, it does not improve the performance of the query itself when the user actually triggers it. The user still experiences the slow load time. Performance improvements must come from optimizing the execution plan or utilizing caching, not simply deferring the execution time.
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JA
Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Databricks exam blueprint
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