Databricks-DA-Assoc Executing Queries with Databricks SQL Practice Question
An analyst is using Databricks SQL and wants to optimize query performance for large-scale joins. Which TWO actions should the analyst perform to improve the performance of join operations involving large Delta tables?
⚠ Common exam trap
Candidates often select incorrect optimization techniques like partitioning on high-cardinality columns or assume auto-optimization handles everything without needing manual statistics collection via ANALYZE TABLE commands for the CBO.
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
✓
Run ANALYZE TABLE table_name COMPUTE STATISTICS FOR COLUMNS join_key
For large joins, Databricks SQL relies on statistics and efficient data layout. Collecting accurate table statistics allows the Cost-Based Optimizer (CBO) to select the most efficient join type, such as Broadcast or Shuffle Hash. Additionally, Z-Ordering on join keys ensures that related data is physically colocated, drastically reducing data shuffling across the cluster nodes during execution, which leads to faster query runtimes and better resource utilization.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Run ANALYZE TABLE table_name COMPUTE STATISTICS FOR COLUMNS join_key
Why this is correct
Computing statistics for specific columns provides the CBO with cardinalities and distribution information. This data allows the query engine to accurately estimate the size of the tables and choose the most efficient join algorithm, preventing suboptimal execution plans that lead to excessive shuffling and memory issues.
- ✗
Manually partition the table by the join key column
Why it's wrong here
Partitioning by a high-cardinality join key leads to the small file problem, which degrades performance rather than improving it. Partitioning is best suited for low-cardinality columns used frequently in WHERE clauses, not for general-purpose join optimization, as it can cause significant file fragmentation and overhead.
- ✓
Apply ZORDER BY on the columns used in the JOIN clause
Why this is correct
Z-Ordering colocates data based on the values in the specified columns. By Z-Ordering on join keys, the engine can prune more effectively and potentially leverage merge joins, which are highly efficient when data is already sorted and colocated, significantly reducing the amount of data shuffled over the network.
- ✗
Increase the number of partitions to the maximum allowed
Why it's wrong here
Increasing partitions to the maximum creates many tiny files, which increases metadata overhead and slows down scan speeds. Effective performance comes from optimally sized files, typically in the range of 1GB, rather than simply having the highest possible number of partitions defined for the table.
- ✗
Set the table format to Parquet instead of Delta
Why it's wrong here
Parquet is a storage format, not a table format. Delta Lake adds critical features like ACID transactions, time travel, and Z-Ordering on top of Parquet. Switching to plain Parquet would lose the performance-enhancing features of Delta Lake, such as data skipping and efficient metadata management.
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.