Databricks-Spark-Assoc Troubleshooting and Tuning DataFrame Apps Practice Question
When analyzing a Spark job's execution plan, what does a 'BroadcastHashJoin' indicate compared to a 'SortMergeJoin'?
⚠ Common exam trap
Candidates often think BroadcastHashJoin speeds up execution by sorting data faster, missing that its primary advantage is completely eliminating network shuffling.
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
✓
BroadcastHashJoin avoids a shuffle operation.
A 'BroadcastHashJoin' indicates that Spark has decided to send the smaller table to all executors to perform the join in memory, avoiding a shuffle. A 'SortMergeJoin' involves shuffling both tables, which is significantly more expensive. Understanding when Spark chooses one over the other helps developers identify if they need to explicitly broadcast small tables or adjust configuration thresholds to optimize join performance and minimize costly cluster-wide shuffle operations.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
BroadcastHashJoin requires both tables to be pre-sorted.
Why it's wrong here
BroadcastHashJoin does not require sorting; it uses a hash map of the smaller table in memory. SortMergeJoin is the one that requires the data to be sorted and partitioned across the network, making BroadcastHashJoin much faster for joins involving at least one small, filterable table in the dataset.
- ✓
BroadcastHashJoin avoids a shuffle operation.
Why this is correct
Because the smaller table is sent to every executor node, the larger table remains in its original partition structure, and no data is shuffled across the network. This makes it a much faster join strategy compared to SortMergeJoin, which requires full shuffles of both tables to align the keys.
- ✗
SortMergeJoin is always faster than BroadcastHashJoin.
Why it's wrong here
SortMergeJoin is generally slower than BroadcastHashJoin for joins where one table fits in memory because it incurs high network I/O due to shuffling. BroadcastHashJoin is the preferred strategy for small-to-large joins, whereas SortMergeJoin is only necessary when both tables are too large to fit in memory.
- ✗
BroadcastHashJoin only works for inner joins.
Why it's wrong here
BroadcastHashJoin can support various join types, including left, right, and inner joins, depending on the Spark version and the nature of the data. The limiting factor is not the join type, but rather the size of the smaller table and whether it fits in the available broadcast memory on executors.
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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-Spark-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-Spark-Assoc exam.