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 is for converting existing Parquet to Delta format; it does not compact files.
- ✓
Run OPTIMIZE to compact small files.
Why this is correct
OPTIMIZE compacts small files into larger ones, improving read performance.
- ✗
Run DESCRIBE HISTORY to analyze file sizes.
Why it's wrong here
DESCRIBE HISTORY shows table history, not a performance optimization.
- ✗
Run VACUUM to delete old files.
Why it's wrong here
VACUUM cleans up files older than retention threshold but does not compact small files.
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