Databricks-DA-Assoc Analyzing Queries Practice Question
An analyst is optimizing a query that performs multiple aggregations on a Delta table. Which TWO actions can the analyst take to improve query performance via the SQL editor?
⚠ Common exam trap
Candidates often suggest adding more compute resources or changing the cluster type as a first step, ignoring that query-level optimizations like pruning and Z-Ordering are far more effective.
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
✓
Use Z-Ordering on columns frequently used in WHERE filters.
Improving performance requires reducing the amount of data read. Using partition pruning allows the engine to skip irrelevant files, while Z-Ordering improves data skipping by clustering related information together. These techniques are fundamental for Databricks SQL performance, as they minimize I/O overhead. Mastering these adjustments allows analysts to write highly efficient code that scales effectively with growing datasets, reducing both latency and costs for the organization.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Use Z-Ordering on columns frequently used in WHERE filters.
Why this is correct
Z-Ordering co-locates related data in the same set of files, significantly enhancing data skipping capabilities. By clustering data based on frequently filtered columns, the engine can ignore irrelevant files during query execution, leading to faster data retrieval and lower overall compute resource consumption for the query.
- ✓
Enable partition pruning by including partition columns in the WHERE clause.
Why this is correct
Partition pruning restricts the scan to specific directories on storage. By explicitly filtering on partition columns in the WHERE clause, the engine avoids scanning partitions that do not meet the criteria, drastically reducing the volume of data read from storage and significantly accelerating the query's total execution time.
- ✗
Increase the cluster size to 'Large' for every query execution.
Why it's wrong here
Scaling up to a larger cluster is not always efficient, as it incurs higher costs and may not resolve I/O-bound queries. Proper performance tuning focuses on data layout and query logic first, as increasing compute power without addressing underlying data skipping issues yields diminishing returns and higher expenses.
- ✗
Force a Broadcast Join for all small tables.
Why it's wrong here
Forcing a join strategy can lead to 'Out of Memory' errors if the table size exceeds available executor memory. The query optimizer automatically chooses the most efficient join method based on table statistics. Manually overriding this without deep analysis often results in unstable query performance and failed executions.
- ✗
Convert all tables to Parquet format.
Why it's wrong here
Delta Lake, which is built on Parquet, provides critical features like ACID transactions and time travel that standard Parquet lacks. Converting back to raw Parquet loses these optimizations and metadata handling, which actually degrades performance and reliability in the Databricks ecosystem compared to using native Delta tables.
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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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