Databricks-DA-Assoc Executing Queries with Databricks SQL Practice Question
An analyst is preparing a dashboard and needs to ensure that the queries powering it are as performant as possible. Which THREE techniques should the analyst use to optimize these SQL queries in Databricks?
⚠ Common exam trap
Candidates often confuse VACUUM with OPTIMIZE or assume Z-Ordering alone is enough without running ANALYZE to update the Cost-Based Optimizer's statistics for best performance.
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
✓
Regularly run OPTIMIZE on the underlying tables
Optimizing dashboards requires a combination of efficient data layout, metadata management, and query-level tuning. Using the 'OPTIMIZE' command keeps files consolidated, while 'ANALYZE' provides the CBO with the statistics it needs for optimal plan selection. Finally, using 'Z-Ordering' on high-cardinality columns frequently appearing in filters or joins ensures the query engine can skip large volumes of irrelevant data, directly leading to faster dashboard response times.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Regularly run OPTIMIZE on the underlying tables
Why this is correct
Regular optimization compacts small files created by ingestion processes into larger files. This reduction in the total number of files directly decreases metadata overhead and scan times, leading to more responsive queries, which is a fundamental requirement for interactive dashboard performance in Databricks SQL.
- ✗
Always use SELECT * to fetch all available columns
Why it's wrong here
Fetching all columns with SELECT * is an anti-pattern. It increases I/O overhead by forcing the engine to read data that isn't needed. Limiting queries to only required columns reduces data transfer and memory pressure, which is essential for maintaining high performance in analytical dashboard queries.
- ✓
Compute statistics using the ANALYZE command
Why this is correct
Updating statistics via ANALYZE is critical for the Cost-Based Optimizer. Without accurate statistics, the optimizer might choose an inefficient join strategy or incorrect scan path, leading to sluggish queries. Maintaining fresh statistics is a standard best practice for optimizing performance in Databricks SQL environments.
- ✓
Z-Order tables on frequently filtered or joined columns
Why this is correct
Z-Ordering physically re-arranges data so that related values are colocated in the same files. When columns used in filters or joins are Z-Ordered, the data skipping capabilities of the Delta engine are maximized, leading to significantly fewer files being scanned and faster execution for common queries.
- ✗
Convert all Delta tables to CSV format for speed
Why it's wrong here
CSV is a text-based, row-oriented format that does not support metadata, data skipping, or efficient column-level access. Converting to CSV would severely degrade performance, as the engine would have to parse the entire file instead of reading only the relevant columns and data blocks.
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Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Databricks exam blueprint
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