How I Prepared and Passed the Databricks Data Engineer Professional Exam in 15 Days : Part 2

2026-04-18 · Source: Data Engineering on Medium · Field: Technology & Digital — Data Science & Analytics, Artificial Intelligence & Machine Learning, Cloud Computing & IT Infrastructure · Depth: Advanced, long

Summary

This article, "How I Prepared and Passed the Databricks Data Engineer Professional Exam in 15 Days : Part 2," details key topics and sample questions for the Databricks Data Engineer Professional Exam, focusing on the "Cost & Performance Optimisation" section, which accounts for 13% of the exam. It covers Unity Catalog's role in data governance, lineage, tagging, AI documentation, egress cost control, and system tables for billing and usage. The content also explores Delta optimization techniques like deletion vectors, liquid clustering, Z-Ordering, auto-optimize, auto-compaction, and Change Data Feed (CDF) for incremental updates. Additionally, it addresses identifying performance bottlenecks using Query Profiler and optimizing Spark shuffle operations, joins, and data spills.

Key takeaway

For Data Engineers preparing for the Databricks Data Engineer Professional Exam, focus on the "Cost & Performance Optimisation" section. You should thoroughly understand Unity Catalog's governance features, Delta Lake optimization techniques like deletion vectors and liquid clustering, and how to use Query Profiler to diagnose Spark job performance issues. Practice sample questions related to these areas to solidify your understanding and improve your chances of passing.

Key insights

Databricks exam preparation requires deep understanding of Unity Catalog, Delta optimizations, and performance tuning.

Principles

• Unity Catalog reduces data redundancies and maintenance burden.
• Deletion vectors prevent full file rewrites for DML operations.
• Liquid clustering dynamically optimizes data layout for changing queries.

Method

To optimize Delta tables, enable CDF for row-level changes, use liquid clustering for adaptive data layout, and leverage Query Profiler to diagnose performance bottlenecks in Spark jobs.

In practice

• Use `ALTER TABLE ... SET TAGS` for existing Unity Catalog tables.
• Enable CDF and `WITH HISTORY` for Delta Sharing time-travel queries.
• Analyze `system.billing.usage` to identify high DBU consumption workloads.

Topics

• Databricks Data Engineer Exam
• Unity Catalog
• Delta Lake Optimization
• Deletion Vectors
• Liquid Clustering

Best for: Data Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Data Engineering on Medium.