Databricks-DE-Pro Cost and Performance Optimization Practice Question
A data engineer manages a Delta table that is used for both batch analytics and frequent small updates from a streaming job. The table is not partitioned, and the engineer notices that queries are slowing down as the table grows. The engineer wants to improve query performance without changing the table schema or partitioning strategy. Which action should the engineer take?
⚠ Common exam trap
The trap here is thinking that VACUUM or adding compute solves query slowdowns, when the real fix is optimizing data layout with Z-ORDER to enable efficient 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
✓
Run OPTIMIZE with Z-ORDER on a commonly filtered column.
OPTIMIZE with Z-ORDER improves data skipping by clustering similar data together, which directly speeds up queries that filter on the Z-ORDERed column. It works without schema changes or partitioning and is compatible with frequent updates. Other options either do not address query performance or introduce trade-offs that harm Delta Lake capabilities.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Increase the cluster size used for all queries against the table.
Why it's wrong here
Adding more compute resources can temporarily speed up queries, but it does not fix the underlying issue of poor data layout. As the table continues to grow, the problem will persist, and the cost will increase. This is a reactive measure that does not improve data skipping or file organization, and it is not a sustainable optimization for a growing table with frequent updates.
- ✗
Convert the table to a Parquet table and use partitioning by date.
Why it's wrong here
Converting to Parquet loses Delta Lake features like ACID transactions, time travel, and efficient upserts, which are essential for frequent streaming updates. Partitioning by date changes the table structure and may not align with query patterns. This action would degrade the ability to handle small updates and does not guarantee better performance for the existing query workload.
- ✓
Run OPTIMIZE with Z-ORDER on a commonly filtered column.
Why this is correct
Z-ORDERing during OPTIMIZE co-locates related data in the same files, improving data skipping for queries that filter on the Z-ORDERed column. This reduces the amount of data scanned and speeds up queries, especially for large tables with frequent updates. It does not require schema changes or partitioning, and it can be scheduled regularly to maintain performance as new data arrives.
- ✗
Enable change data feed on the table and run VACUUM with a shorter retention period.
Why it's wrong here
Change data feed is for capturing row-level changes, not for improving query performance. VACUUM with a shorter retention period removes old files and can reduce storage cost, but it does not optimize data layout for queries. In fact, aggressive VACUUM can break time travel and streaming reads. This combination does not address the slowdown caused by suboptimal file organization.
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Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Databricks exam blueprint
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