Databricks-DE-Pro Developing Code (Python/SQL) Practice Question
Which TWO of the following are benefits of using the Databricks Delta Lake 'Optimize' command? (Select TWO)
⚠ Common exam trap
Test-takers frequently select index-based choices or vacuum operations, forgetting that OPTIMIZE specifically focuses on file compaction and collaborative Z-Ordering 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
✓
It compacts small files into larger, more efficient Parquet files.
The OPTIMIZE command compacts small files into larger files, which significantly improves read performance by reducing the metadata overhead of listing thousands of small files. It also allows for Z-Ordering, which collocated related information in the same files, enabling data skipping during query execution. These optimizations are crucial for maintaining high-performance data lakes as datasets grow over time in production environments.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
It automatically shrinks the data volume by dropping null values.
Why it's wrong here
OPTIMIZE does not modify the data content, such as dropping null values. Data cleaning should be done via standard transformation logic like filters or 'drop' operations before or during the write process. Relying on OPTIMIZE to clean data is a fundamental misunderstanding of the command's purpose, which is file compaction and indexing.
- ✓
It compacts small files into larger, more efficient Parquet files.
Why this is correct
Small files are a major performance killer in distributed systems because they cause excessive metadata operations and suboptimal I/O. By merging these into larger files, OPTIMIZE improves the efficiency of read queries, making the data lake more performant for BI tools and downstream processing tasks that require fast table scans.
- ✓
It enables Z-Ordering for more efficient data skipping.
Why this is correct
Z-Ordering is a technique that co-locates related data in the same file based on specific columns. This allows the Spark optimizer to skip entire files that do not contain the requested data, dramatically speeding up queries that filter by the Z-Ordered columns. It is an essential performance tuning step for large analytical tables.
- ✗
It converts Parquet files to CSV format for better compatibility.
Why it's wrong here
OPTIMIZE does not perform format conversion. Delta Lake tables are built on Parquet by default, which is a highly optimized, columnar format. Converting to CSV would be a significant downgrade in performance and feature support, as CSV lacks the schema evolution, metadata, and compression capabilities required for robust analytical data lake workloads.
- ✗
It automatically updates the table statistics for the CBO.
Why it's wrong here
While OPTIMIZE does update statistics that the Cost-Based Optimizer (CBO) uses, the primary purpose is file compaction and indexing. Furthermore, running 'ANALYZE TABLE' is the explicit, recommended command for updating statistics for the CBO. Relying solely on OPTIMIZE for statistics updates is not the best practice for ensuring the CBO has accurate data.
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Last reviewed September 2026 · checked against the official Databricks exam blueprint
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