Databricks-DA-Assoc Executing Queries with Databricks SQL Practice Question
An analyst is observing that a specific query is slow because it performs a large shuffle. Which of the following is the most likely cause, and how can it be mitigated?
⚠ Common exam trap
Candidates frequently blame network latency or hardware limits, missing that large shuffles in Spark/Databricks are almost always caused by poor data distribution and lack of colocation on join keys.
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
✓
Cause: Poor data colocation; Mitigation: Use Z-Ordering
Large shuffles occur when data must be redistributed across nodes to perform a join or group by operation. This often happens if the data is not partitioned or clustered effectively on the join keys. To mitigate this, the analyst should ensure that the table is Z-Ordered on the relevant columns, as this colocates the data and allows the engine to perform more efficient local operations instead of cross-node shuffles.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Cause: Lack of partitioning; Mitigation: Increase cluster size
Why it's wrong here
While increasing cluster size provides more resources, it does not fix the underlying shuffle. A shuffle is a result of data distribution; if the data isn't colocated, the cluster will still shuffle it regardless of its size. The better solution is to address the data layout itself.
- ✓
Cause: Poor data colocation; Mitigation: Use Z-Ordering
Why this is correct
A shuffle is often caused by data being spread randomly across files. Z-Ordering forces related data into the same files based on the specified columns. By colocation, the engine can perform operations on the same node, significantly reducing the amount of data transferred over the network during a shuffle.
- ✗
Cause: Too many files; Mitigation: Run VACUUM
Why it's wrong here
VACUUM removes old files but does not change the physical organization of the active data. If the shuffle is caused by poor data distribution, VACUUM will not help. The correct maintenance command to change physical file layout is OPTIMIZE, which handles file compaction and re-organization.
- ✗
Cause: Column data types; Mitigation: Use VARCHAR instead of STRING
Why it's wrong here
The choice between STRING and VARCHAR has negligible impact on query performance or shuffling in Databricks. Shuffling is a function of data volume and physical distribution on the storage layer, not the specific string character limit defined for a column in the table schema.
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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
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