Databricks-DE-Assoc Data Transformation and Modeling Practice Question
A data engineer needs to join two massive datasets. One dataset is very small (10MB), and the other is very large (1TB). To ensure the join operation is performed as efficiently as possible, which join strategy should be enforced?
⚠ Common exam trap
Candidates often select a standard sort-merge join or shuffle hash join out of habit, overlooking the massive performance benefits of broadcasting a tiny 10MB table.
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
✓
Enforce a Broadcast Hash Join.
Selecting the correct join strategy is vital for preventing data shuffles, which are expensive. A Broadcast join is perfect for scenarios where one side is small enough to fit in memory on every worker node. By broadcasting the small table, the engine avoids a massive shuffle of the large table, resulting in significantly faster performance and reduced network traffic.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Enforce a Sort-Merge Join.
Why it's wrong here
Sort-Merge join requires shuffling both large datasets across the network to be sorted and joined on the same worker nodes. This is extremely expensive for massive datasets and is typically used when both sides of the join are too large to fit in memory.
- ✓
Enforce a Broadcast Hash Join.
Why this is correct
Broadcast Hash Join sends the small table to all executor nodes, allowing the large table to be joined locally without a shuffle. This minimizes network traffic and compute overhead, making it the most efficient choice for joining a small table with a large one.
- ✗
Enforce a Cartesian Product Join.
Why it's wrong here
Cartesian Product joins create a result set where every row of the first table is paired with every row of the second. This leads to an explosion of data, often causing out-of-memory errors and extremely slow execution times for any non-trivial dataset size.
- ✗
Enforce a Shuffle Hash Join.
Why it's wrong here
Shuffle Hash Join involves shuffling both datasets by the join key. This is unnecessary when one table is small enough to be broadcast. Shuffling massive amounts of data is slow and inefficient compared to broadcasting a small, static lookup table.
About these practice questions
This Databricks-DE-Assoc question is part of Courseiva's 276-question bank — original exam-style content with full explanations and wrong-answer analysis, never real exam questions or exam dumps. Learn why practice questions differ from exam dumps →
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-DE-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-DE-Assoc exam.