DP-203 Develop data processing Practice Question
You are working with a Delta Lake table in Azure Databricks. The table is updated frequently with new data and occasionally with updates to existing rows. You need to optimize read performance for queries that filter on a specific date column. The table is partitioned by date. Which optimization technique should you apply?
⚠ Common exam trap
Many exam-takers confuse OPTIMIZE (file compaction) with ZORDER BY (data clustering), assuming any performance optimization technique will improve filter queries, but only ZORDER BY physically reorders data to enable efficient data skipping.
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 ZORDER BY on the date column.
ZORDER BY on the date column co-locates related data within each partition, significantly reducing the amount of data scanned for queries that filter on that column. This is especially beneficial for a frequently updated Delta table where data is already partitioned by date, as ZORDER BY optimizes the layout within partitions without changing the partition scheme.
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 OPTIMIZE on the table.
Why it's wrong here
OPTIMIZE compacts small files into larger ones but does not optimize for selective filtering.
- ✓
Run ZORDER BY on the date column.
Why this is correct
Z-ordering co-locates column data, improving data skipping for filters on that column.
- ✗
Run ANALYZE STATISTICS on the table.
Why it's wrong here
ANALYZE STATISTICS collects metadata for the optimizer but does not physically reorganize data.
- ✗
Run VACUUM to clean up old versions.
Why it's wrong here
VACUUM is for housekeeping, not read optimization.
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