Databricks-DA-Assoc Analyzing Queries Practice Question
A data analyst notices that a query involving a join between a large table and a small lookup table is performing poorly. The analyst wants to optimize the join performance without changing the underlying data. Which technique should they apply?
⚠ Common exam trap
Candidates often rely on the Catalyst optimizer to automatically broadcast every small table, failing to manually add explicit broadcast hints when needed.
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
✓
Add an explicit BROADCAST hint to the small table in the JOIN clause.
Broadcasting the small table forces the cluster to send the entire lookup table to every node in the cluster, avoiding a full shuffle of the large table. This technique significantly reduces network overhead during joins when one side fits in memory. Mastering join strategies is essential for analysts to write efficient SQL that minimizes cluster resource consumption and shortens execution times on large datasets.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Add an explicit BROADCAST hint to the small table in the JOIN clause.
Why this is correct
The BROADCAST hint explicitly instructs the Spark catalyst optimizer to perform a broadcast hash join. This eliminates the need for a shuffle exchange, which is the most expensive part of a join, by duplicating the smaller dataset across all executor nodes for local lookup operations.
- ✗
Increase the number of shuffle partitions to 2000.
Why it's wrong here
Increasing the shuffle partitions excessively can lead to data fragmentation, where tasks become too small and the overhead of scheduling tasks outweighs the computation time. This does not address the fundamental bottleneck of shuffling data between executors during a join operation between tables of varying sizes.
- ✗
Convert the large table to a Delta table with Z-Ordering.
Why it's wrong here
Z-Ordering improves data skipping by clustering related information together on disk, which is effective for filtering queries. However, Z-Ordering does not change the physical distribution of data during a join operation across a cluster, meaning it will not optimize the shuffle performance for this specific query.
- ✗
Use the MERGE INTO statement instead of a standard JOIN.
Why it's wrong here
The MERGE INTO statement is a DML operation used to synchronize data between tables, such as performing updates or inserts. It is not an alternative to a standard SELECT JOIN and does not inherently optimize join performance or change how data is exchanged across the cluster during execution.
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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.