hardMultiple ChoiceObjective-mapped
DP-203 Practice Question: Designing a data processing solution using Azure…
You are designing a data processing solution using Azure Databricks with Delta Lake. The data is partitioned by date and ingested daily. You notice that the Delta table has many small files, causing slow read performance. Which strategy should you recommend to optimize the table for faster queries?
⚠ Common exam trap
Test-takers frequently confuse ZORDER BY (which improves data skipping but not file count) with OPTIMIZE (which reduces file count), or mistakenly think VACUUM or adding partitions solves the small file problem, when in fact they either don't address it or make it worse.
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 OPTIMIZE on the table to compact small files.
Running OPTIMIZE on a Delta Lake table compacts many small files into larger ones, reducing the number of files that need to be read during queries. This directly addresses the slow read performance caused by the small file problem, which is common in daily partitioned ingestion. OPTIMIZE uses bin-packing to merge files up to a target size (default 256 MB), improving scan efficiency without changing the data.
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 to compact small files.
Why this is correct
OPTIMIZE merges small files into larger ones.
- ✗
Run ZORDER BY on the date column.
Why it's wrong here
Z-ordering improves data skipping, not file compaction.
- ✗
Run VACUUM to delete old files.
Why it's wrong here
VACUUM removes unreferenced files, does not compact.
- ✗
Increase the number of partitions by adding a new partition column.
Why it's wrong here
More partitions lead to more small files.
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