DP-203 Develop data processing Practice Question
You have an Azure Databricks notebook that processes data from a Delta table. The notebook runs slowly due to many small files. You need to optimize the Delta table for faster reads. Which Delta Lake operation should you run?
⚠ Common exam trap
Many candidates confuse VACUUM (which cleans up old files) with OPTIMIZE (which compacts files), or think DESCRIBE HISTORY is a performance-tuning command rather than a diagnostic tool.
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 to compact small files.
The OPTIMIZE command in Delta Lake compacts many small files into larger ones by rewriting data files based on the table's partitioning scheme. This reduces the number of files that need to be read during queries, significantly improving read performance. Since the notebook is slow due to many small files, OPTIMIZE directly addresses the root cause.
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 CONVERT TO DELTA on the underlying Parquet files.
Why it's wrong here
CONVERT TO DELTA merely registers existing Parquet files in the transaction log; it neither merges nor rewrites them, so the small-file count persists. It suits one-off migrations of Parquet directories into Delta. The stem needs compaction, which OPTIMIZE performs by bin-packing small files.
- ✓
Run OPTIMIZE to compact small files.
Why this is correct
OPTIMIZE compacts many small files into larger ones, reducing per-file overhead and metadata pressure so reads scan fewer files. This directly addresses the small-file problem causing the slow notebook, and it is the Delta Lake operation designed for bin compaction.
- ✗
Run DESCRIBE HISTORY to analyze file sizes.
Why it's wrong here
DESCRIBE HISTORY returns the table's transaction log entries, including operation metrics, but reads no data and rewrites nothing. It suits auditing changes or debugging versions. Faster reads require physically consolidating the many small files, which OPTIMIZE achieves through bin-packing.
- ✗
Run VACUUM to delete old files.
Why it's wrong here
VACUUM deletes unreferenced data files older than the retention threshold, reducing storage, not file count; it can even remove files still needed. It suits reclaiming space after rewrites. The slowness stems from many small live files, which OPTIMIZE compacts into fewer larger ones.
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