Databricks-DA-Assoc Executing Queries with Databricks SQL Practice Question
A data analyst is running a complex aggregate query in Databricks SQL that frequently times out before finishing. The underlying Delta table contains hundreds of gigabytes of historical log data partitioned by date. What is the most effective Databricks SQL query optimization technique to apply directly within the SQL statement?
⚠ Common exam trap
Candidates often try to enable caching or rewrite the entire query logic. They overlook the most fundamental performance gain, which is simply using partition pruning to reduce the data scanned.
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
✓
Include a explicit WHERE clause filtering on the date partition column to leverage partition pruning.
Applying a partition filter using the date column allows Databricks SQL to prune irrelevant files instantly, significantly reducing the data scan volume and preventing timeouts. This practice ensures queries execute efficiently over large historical datasets without needing constant infrastructure scaling or manual intervention.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Increase the overall timeout limit within the cluster configuration settings.
Why it's wrong here
Altering the cluster timeout limits merely masks the underlying performance bottleneck by waiting longer rather than addressing the root cause of excessive data scanning. Analysts should optimize query patterns to minimize data movement instead of relying on extended timeouts.
- ✓
Include a explicit WHERE clause filtering on the date partition column to leverage partition pruning.
Why this is correct
Adding a WHERE clause on the date partition column lets Databricks SQL prune irrelevant partitions, reading only the required date range instead of scanning hundreds of gigabytes. This reduces I/O and prevents the query from timing out.
- ✗
Convert the Delta table format into a standard Parquet format inside an external Hive metastore.
Why it's wrong here
Converting to Parquet removes Delta statistics, partitioning benefits and file skipping, worsening the timeout. It would suit interoperability with external Hive engines, but the fix here is query optimisation such as predicate pushdown on the date partition.
- ✗
Wrap the main aggregation query inside a recursive common table expression.
Why it's wrong here
Recursive CTEs iterate over self-referencing hierarchies, not large flat aggregations; they add overhead rather than reducing scanned data. It is tempting because CTEs simplify complex query structure, and recursion suits bill-of-materials or org-chart traversal. Here, partition pruning on the date column or pre-aggregation would cut the rows scanned before the aggregation times out.
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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.