Databricks-DA-Assoc Executing Queries with Databricks SQL Practice Question
Which THREE of the following are valid methods to optimize the performance of a slow-running SQL query in Databricks?
⚠ Common exam trap
Candidates often choose high-cardinality columns for partitioning instead of low-cardinality ones, leading to excessive small files and degraded query 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
✓
Using Z-Ordering on high-cardinality columns.
Performance optimization in Databricks involves a combination of data organization, compute resource management, and query plan refinement. By using techniques like Z-Ordering, partitioning, and leveraging the cost-based optimizer, analysts can significantly reduce query latency. These methods are essential for managing large-scale datasets, ensuring that compute resources are utilized effectively, and providing timely insights to business stakeholders who rely on the performance of analytical dashboards and reports.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Using Z-Ordering on high-cardinality columns.
Why this is correct
Z-Ordering co-locates related information in files, allowing the Databricks SQL engine to skip irrelevant data during scans. This is particularly effective for columns used frequently in WHERE clauses, as it narrows the data read operation to only the relevant files, drastically improving query execution speed.
- ✗
Manually rewriting every query to use nested subqueries.
Why it's wrong here
Rewriting queries into complex nested subqueries often makes them harder to read and may hinder the query optimizer's ability to create efficient execution plans. Modern query optimizers are typically better at optimizing flattened or standard SQL joins than convoluted, manually optimized nested subquery structures.
- ✓
Executing ANALYZE TABLE to update statistics.
Why this is correct
Updating table statistics provides the cost-based optimizer with accurate information about data distribution. This allows the optimizer to select more efficient join algorithms, such as choosing the best broadcast join strategy, which directly leads to faster query performance for complex analytical workloads and large-scale data joins.
- ✓
Partitioning tables on low-cardinality columns.
Why this is correct
Partitioning data based on logical categories, such as date or region, enables partition pruning. This feature allows the query engine to completely skip directories that do not match the filter criteria, reducing the total volume of data scanned and significantly accelerating query performance for filtered analytical operations.
- ✗
Increasing the query timeout setting to 24 hours.
Why it's wrong here
Increasing the query timeout allows long-running queries to persist, but it does nothing to improve performance. It is a configuration change that masks slow performance rather than addressing the root cause, which is usually inefficient data structure, poor indexing, or suboptimal join strategies that require actual optimization.
About these practice questions
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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.