DP-203 Develop data processing Practice Question
Which THREE actions improve the performance of a Delta table in Azure Databricks? (Choose three.)
⚠ Common exam trap
Many exam-takers confuse 'increasing shuffle partitions' (a Spark tuning parameter) with a direct performance improvement for Delta tables, when in fact it can worsen performance and is not a recommended action for Delta-specific optimization.
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
✓
Run VACUUM to remove old versions and compact small files
Running VACUUM on a Delta table removes old file versions that are no longer referenced by the Delta transaction log, freeing up storage and reducing the number of small files that must be read during queries. This compaction of small files improves I/O efficiency and query performance, especially in tables with frequent updates or deletes.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Run VACUUM to remove old versions and compact small files
Why this is correct
Compaction reduces file count.
- ✗
Disable auto-optimize to reduce overhead
Why it's wrong here
Auto-optimize helps performance.
- ✓
Run OPTIMIZE with ZORDER BY on frequently filtered columns
Why this is correct
Improves data skipping.
- ✗
Increase the number of shuffle partitions
Why it's wrong here
Not specific to Delta.
- ✓
Partition the table by high-cardinality columns
Why this is correct
Enables partition pruning.
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