Databricks-DE-Pro Cost and Performance Optimization Practice Question
Which property should be configured to allow Databricks to automatically optimize the size of files during write operations in Delta Lake?
⚠ Common exam trap
Students often confuse Auto Compact with Optimize Write, or suggest running manual OPTIMIZE commands instead of configuring the automatic write-time property.
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
✓
spark.databricks.delta.optimizeWrite.enabled
The `spark.databricks.delta.optimizeWrite.enabled` property is essential for write-time optimization. It dynamically groups data before writing to storage, ensuring that the created files are of optimal size. This prevents the small-file problem from occurring in the first place, reducing the need for post-write maintenance and improving read query performance significantly. It is a proactive performance optimization that is highly recommended for high-frequency write workloads.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
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spark.sql.shuffle.partitions
Why it's wrong here
This property controls the number of partitions used during shuffle operations. While important for tuning join and aggregation performance, it does not control the physical size of the files being written to disk. It is unrelated to the automated file compaction functionality provided by Delta Lake.
- ✓
spark.databricks.delta.optimizeWrite.enabled
Why this is correct
This setting enables the optimize-write feature, which automatically compacts data into optimal file sizes during the write process. By doing this upfront, it prevents the creation of numerous small files, which is a major performance bottleneck for read-heavy analytical workloads on large Delta tables.
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spark.databricks.io.cache.enabled
Why it's wrong here
This setting enables the Databricks I/O cache, which caches frequently accessed data on local SSDs for faster reads. While it improves read performance, it does not affect the file layout or size on persistent storage, and it does not help with the small-file problem during writes.
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spark.sql.autoBroadcastJoinThreshold
Why it's wrong here
This setting defines the maximum size of a table that can be broadcast during a join. It is a crucial parameter for join optimization but has no impact on how data files are physically written to storage or the management of file sizes in Delta tables.
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Last reviewed September 2026 · checked against the official Databricks exam blueprint
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