Databricks-DA-Assoc Executing Queries with Databricks SQL Practice Question
An analyst is optimizing query performance in Databricks SQL. Which TWO of the following actions directly improve query execution speed by reducing the amount of data scanned during query execution?
⚠ Common exam trap
Candidates often select 'indexing' or 'caching' as performance solutions, which are not standard optimization techniques for reducing data scan size in Delta Lake environments.
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
✓
Implementing Z-Ordering on frequently filtered columns.
Data skipping and partition pruning are critical performance optimizations in Databricks SQL. By leveraging the Delta Lake transaction log and metadata, Databricks skips unnecessary files and partitions, drastically reducing I/O requirements. These techniques are fundamental for large-scale data analysis, as they allow queries to target only the specific subsets of data required to satisfy filters, significantly lowering latency and improving overall warehouse performance for end-users.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Implementing Z-Ordering on frequently filtered columns.
Why this is correct
Z-Ordering co-locates related data in the same set of files, which allows the query engine to skip large amounts of irrelevant data. When combined with data skipping, this significantly reduces the number of files scanned, leading to faster query performance for analytical workloads filtering on those specific columns.
- ✗
Increasing the SQL Warehouse cluster size.
Why it's wrong here
Increasing cluster size provides more compute memory and cores for parallel processing, but it does not inherently reduce the volume of data scanned from storage. While it may speed up CPU-bound tasks, it does not address the underlying I/O efficiency that results from effective metadata-based data skipping.
- ✓
Using partitioned columns in the WHERE clause.
Why this is correct
Partition pruning is a performance optimization that skips entire directories of data if they do not match the filter criteria provided in the WHERE clause. By narrowing the search space to relevant partitions, the query engine processes fewer files, resulting in significantly faster execution times for large datasets.
- ✗
Enabling query result caching for all users.
Why it's wrong here
Query result caching returns stored results for identical queries, but it does not change the amount of data scanned for new or modified queries. While caching is useful for repetitive reporting, it is not a data-level optimization technique designed to reduce the physical I/O scan footprint.
- ✗
Updating statistics using the ANALYZE TABLE command.
Why it's wrong here
While ANALYZE TABLE helps the cost-based optimizer choose better join strategies and execution plans, it does not directly reduce the raw amount of data scanned from storage files. It is an indirect optimization tool rather than a data-skipping strategy meant to minimize the volume of retrieved data.
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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
This Databricks-DA-Assoc practice question is part of Courseiva's free Databricks certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the Databricks-DA-Assoc exam.