Databricks-DE-Assoc Databricks Intelligence Platform Practice Question
A data engineer is using Databricks SQL to analyze data stored in a Delta table. The engineer wants to optimize query performance by leveraging Delta Lake features. Which TWO actions should the engineer take to improve query performance on the Delta table? (Choose two.)
⚠ Common exam trap
The trap here is assuming that simply increasing cluster resources or changing file format will improve performance, when the most effective Delta Lake optimizations are file compaction and data colocation.
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 the OPTIMIZE command to compact small files.
OPTIMIZE compacts small files into larger ones, reducing I/O and improving data skipping. Z-ORDER BY colocates data on a specified column, further enhancing data skipping for queries that filter on that column. Together, these Delta Lake features directly improve query performance by optimizing the physical layout of data. Other options either do not directly address file layout or would remove Delta Lake benefits.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Convert the table to Parquet format.
Why it's wrong here
Converting a Delta table to Parquet would lose Delta Lake features such as ACID transactions, time travel, and schema evolution. Parquet is a columnar format but does not provide the transaction log and optimization features of Delta Lake. This would likely degrade performance and functionality, not improve it.
- ✓
Run the OPTIMIZE command to compact small files.
Why this is correct
The OPTIMIZE command compacts small files into larger ones, reducing the number of files that need to be read during queries. This improves performance by reducing I/O overhead and enabling better data skipping. It is a standard Delta Lake maintenance operation for tables that receive frequent small updates or streaming writes.
- ✓
Use Z-ORDER BY on a frequently filtered column.
Why this is correct
Z-ORDER BY colocates related data in the same set of files, which improves data skipping when queries filter on that column. This reduces the amount of data scanned, speeding up queries. It is typically used in conjunction with OPTIMIZE to reorganize data for better performance on specific query patterns.
- ✗
Enable auto-compaction on the Delta table.
Why it's wrong here
Auto-compaction automatically compacts small files after a write, but it is not a direct action to improve query performance on an existing table. It helps prevent small file accumulation but does not optimize existing files. For immediate performance improvement, running OPTIMIZE is more direct. Auto-compaction is a table property that can be enabled, but it is not a one-time action to optimize an existing table.
- ✗
Increase the cluster size by adding more workers.
Why it's wrong here
Adding more workers can improve performance for large-scale processing, but it increases cost and may not address the underlying issue of inefficient file layout. For query performance on a Delta table, optimizing the file structure is often more effective and cost-efficient than simply adding resources. The scenario asks for Delta Lake features, not cluster scaling.
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JA
Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Databricks exam blueprint
This Databricks-DE-Assoc practice question is part of Courseiva's free Databricks certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the Databricks-DE-Assoc exam.