Databricks Data Engineer Associate: pipelines and operations

Objectives and progression

Independent preparation aligned with the seven domains of the guide effective from 4 May 2026. Learn to choose compute, ingest data with clear state and contracts, transform with SQL and PySpark, coordinate jobs, and recover failures without duplicate writes. The path covers version promotion with Git and bundles, performance and cost diagnosis, and Unity Catalog privileges and policies. Fictional fund-data and reconciliation cases connect engineering with project delivery and APS readiness. Includes 34 primary sources, option explanations, progress, and an internal 28-decision assessment in 60 minutes. This initial coverage is not a full mock or a substitute for practice in a controlled environment.

Audience: Data, development, APS, and project professionals building and operating Databricks pipelines.

Prerequisites: SQL, Python, data, and cloud fundamentals. The exam has no prerequisites; hands-on experience is recommended.

340 estimated study minutes

• Connect storage, processing, and governance to operational requirements.
• Choose ingestion methods and handle source changes without losing traceability.
• Interpret SQL and DataFrames with attention to duplicates, nulls, and cardinality.
• Orchestrate execution and prepare retries that respect existing effects.
• Version changes and distinguish configuration validation from functional evidence.
• Locate time spent and assess optimizations with comparable results.
• Apply privileges and policies with attention to inheritance and storage.

Modules

1. Platform, compute, and data contracts
2. Incremental ingestion, state, and schema
3. Transformation, grain, and quality
4. Jobs, dependencies, and recovery
5. Git, bundles, and environment promotion
6. Diagnosis, performance, and cost
7. Unity Catalog, access, and lifecycle

Continue learning

Data / AI

• SQL
• Python
• Professional Data Engineer
• AWS Certified Machine Learning Engineer - Associate
• Application Production Support

References and version

2026-05-04

• Databricks Certified Data Engineer Associate · 2026-10-01
• Data Engineer Associate exam guide effective 4 May 2026 · 2026-10-01
• Databricks compute · 2026-10-01
• Medallion architecture · 2026-10-01
• Auto Loader ingestion and checkpoints · 2026-10-01
• Auto Loader schema inference and evolution · 2026-10-01
• COPY INTO incremental ingestion · 2026-10-01
• Lakeflow Connect connector concepts · 2026-10-01
• PySpark DataFrames and evaluation · 2026-10-01
• Databricks SQL joins · 2026-10-01
• Explode arrays and maps · 2026-10-01
• SQL set operators · 2026-10-01
• try_cast and invalid input · 2026-10-01
• Count aggregate semantics · 2026-10-01
• Materialized views and refresh · 2026-10-01
• Lakeflow Jobs orchestration · 2026-10-01
• Troubleshoot and repair failed jobs · 2026-10-01
• Job schedules and triggers · 2026-10-01
• Table update triggers · 2026-10-01
• Git folders concepts · 2026-10-01
• Git integration and production guidance · 2026-10-01
• Declarative Automation Bundles lifecycle · 2026-10-01
• Validate deploy and run a bundle · 2026-10-01
• Diagnose performance with Spark UI · 2026-10-01
• Spark skew and spill · 2026-10-01
• Liquid clustering · 2026-10-01
• Predictive optimization · 2026-10-01
• Unity Catalog managed tables · 2026-10-01
• Unity Catalog external tables · 2026-10-01
• Manage Unity Catalog privileges · 2026-10-01
• Unity Catalog privilege reference · 2026-10-01
• Legacy SQL DENY scope · 2026-10-01
• Attribute-based access control · 2026-10-01
• ABAC DENY policies Beta and limits · 2026-10-01