A Delta table has 5 TB of data and is partitioned by date. Queries frequently filter on a high-cardinality customer_id column. The table has many small files per partition. You need to improve query performance with minimal disruption. Which approach is most effective?

Run OPTIMIZE with Z-order by customer_id

Create a Bloom filter index on customer_id

A streaming job writes millions of small updates to a Delta table every few minutes. The table has become fragmented with thousands of tiny files, and query performance has degraded because data skipping is ineffective. You need to reduce file count without disrupting the streaming job. Which approach should you use?

Use Z-order by the timestamp column

Enable Auto Optimize on the table

Run OPTIMIZE once after the streaming job stops

A Delta table is updated frequently with small batches of data. The table has many small files, and queries are slow because data skipping is ineffective. You need to improve query performance with minimal ongoing manual effort. Which action should you take?

Run OPTIMIZE once and do nothing else

Enable Auto Optimize and run OPTIMIZE once

Increase the number of partitions

What problem do optimized writes primarily address in Delta tables?

The inability to collect statistics on new columns

The creation of many small files that degrade query performance

The lack of partition pruning on low-cardinality columns

The need to manually run VACUUM to clean up old files

Which command is used to apply Z-ordering to a Delta table in Databricks?

OPTIMIZE table_name ZORDER BY (column1, column2)

MERGE INTO table_name ZORDER BY (column1, column2)

ANALYZE TABLE table_name ZORDER BY (column1, column2)

VACUUM table_name ZORDER BY (column1, column2)

DatabricksCertified Data Engineer Professional

Domain 6Objective 3

Understand the Optimization Techniques Used by Databricks to Ensure the Performance of Queries on Large Datasets (data Skipping, File Pruning, Etc.). DATA-ENGINEER-PROFESSIONAL Practice Questions (Page 1)

Part of the Cost & Performance Optimization domain, which accounts for 13% of the DATA-ENGINEER-PROFESSIONAL exam. Databricks does not publish an official question count, but from its 120-minute exam (~50–80 total, ~7–10 in this domain), expect 1–2 from this objective — we provide 30 practice questions to prepare you well beyond it. (estimate)

30questions here

6free pages

8concepts

13%of the exam

Questions 1–5

1expert · hard
A Delta table has 5 TB of data and is partitioned by date. Queries frequently filter on a high-cardinality customer_id column. The table has many small files per partition. You need to improve query performance with minimal disruption. Which approach is most effective?
Select an answer first
2application · medium
A streaming job writes millions of small updates to a Delta table every few minutes. The table has become fragmented with thousands of tiny files, and query performance has degraded because data skipping is ineffective. You need to reduce file count without disrupting the streaming job. Which approach should you use?
Select an answer first
3application · medium
A Delta table is updated frequently with small batches of data. The table has many small files, and queries are slow because data skipping is ineffective. You need to improve query performance with minimal ongoing manual effort. Which action should you take?
Select an answer first
4foundation · easy
What problem do optimized writes primarily address in Delta tables?
Select an answer first
5foundation · easy
Which command is used to apply Z-ordering to a Delta table in Databricks?
Select an answer first

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