Databricks' exam outline for DBX-DEP as of October 9, 2026, mapped to the chapters of Ultra Transcenders Databricks Data Engineer Professional.
| Objective | Chapters |
|---|---|
| Section 1: Developing Code for Data Processing using Python and SQL (23%) | |
| Implement scalable Python project structures for Declarative Automation Bundles (formerly Databricks Asset Bundles / DABs) to support modular development and CI/CD integration. | 2. Python projects, libraries and dependencies; 11. Testing, Git folders and bundles |
| Apply troubleshooting techniques to dependency conflicts and installation failures for external libraries (PyPI, local wheels, source archives) across serverless, pipeline, and bundle-deployed environments. | 2. Python projects, libraries and dependencies |
| Develop User-Defined Functions (UDFs) using Pandas, Python, and SQL, including Unity Catalog functions for the given constraints. | 3. UDFs and advanced Spark transformations |
| Create production-ready data pipelines for streaming data using Lakeflow Pipelines and Auto Loader. | 5. Lakeflow Pipelines and Auto Loader |
| Create and automate ETL workloads using Lakeflow Jobs via the UI, API, and CLI. | 10. Lakeflow Jobs |
| Choose between a streaming table and a materialized view for a given latency, cost, and refresh requirement. | 5. Lakeflow Pipelines and Auto Loader |
| Implement CDC pipelines using AUTO CDC APIs (formerly APPLY CHANGES) in Lakeflow Pipelines, including SCD Type 1 and Type 2 via stored_as_scd_type. | 6. Change data capture |
| Choose between Spark Structured Streaming and Apache Spark™ Declarative Pipelines for scalable ETL given operational constraints. | 4. Structured Streaming: state and recovery |
| Create Lakeflow Jobs that use control-flow operators such as If/Else conditions and For Each loops. | 10. Lakeflow Jobs |
| Choose appropriate compute and configuration for environments and dependencies — including serverless compute (serverless environments, dependency management, performance mode), high-memory notebook tasks, and auto-optimization settings (e.g., disallowing retries). | 1. Compute for data engineering |
| Develop unit and integration tests using assertDataFrameEqual, assertSchemaEqual, DataFrame.transform, and testing frameworks to ensure code correctness. | 11. Testing, Git folders and bundles |
| Apply Structured Streaming stateful-processing semantics, including watermarks, output modes, foreachBatch, and checkpoints for fault-tolerant exactly-once state recovery. | 4. Structured Streaming: state and recovery |
| Section 2: Data Ingestion & Acquisition (12%) | |
| Develop data ingestion pipelines to ingest a variety of data formats, including Delta Lake, Parquet, JSON, Iceberg, CSV, and Binary from diverse sources such as message buses (Kafka, Kinesis, Pub/Sub) and cloud storage. | 7. Ingesting files and streams |
| Configure incremental CDC pipelines using Lakeflow Pipelines with Delta or Iceberg as the target table format. | 6. Change data capture; 7. Ingesting files and streams |
| Configure CDC ingestion pipelines from relational database sources, including SQL Server, MySQL, and PostgreSQL, using Lakeflow Connect. | 6. Change data capture |
| Implement OpenSharing (D2D and Databricks-to-Open) and Clean Rooms for privacy-preserving collaboration. | 8. Sharing and federation |
| Configure Lakehouse Federation with governance across supported source systems, applying UC permissions and connection-level credentials. | 8. Sharing and federation |
| Section 3: Data Manipulation (12%) | |
| Apply advanced data transformations, including window functions, joins, and aggregations, using Spark SQL and PySpark to process large datasets. | 3. UDFs and advanced Spark transformations |
| Develop a model and query semi-structured data using the VARIANT data type and related functions (e.g., parse_json, variant_get). | 9. VARIANT and AI functions |
| Apply AI functions, including ai_query, to perform model inference within data pipelines for enrichment and classification tasks. | 9. VARIANT and AI functions |
| Implement data quality expectations in Lakeflow Pipelines to quarantine, drop, or fail on bad records. | 5. Lakeflow Pipelines and Auto Loader |
| Section 4: Monitoring and Alerting (10%) | |
| Use Databricks system tables (billing, compute, access, lakeflow) for cost analysis, auditing, and workload monitoring. | 12. Monitoring and alerting |
| Use Databricks REST APIs / CLI and SDK for monitoring and analyzing jobs and pipelines. | 12. Monitoring and alerting |
| Use Pipeline event logs to monitor pipeline health and data quality. | 12. Monitoring and alerting |
| Use Databricks Lakehouse alerts to monitor governed business metrics, data quality and anomaly signals, usage and cost, SQL warehouse/query health, audit and security events, AI agent quality, and Lakeflow Job branching. | 12. Monitoring and alerting |
| Use Lakeflow Jobs UI and Jobs API to monitor job status and performance metrics. | 12. Monitoring and alerting; 10. Lakeflow Jobs |
| Section 5: Cost & Performance Optimization (15%) | |
| Explain how Unity Catalog managed tables, Predictive Optimization, and Liquid Clustering reduce operational and maintenance overhead for a given workload. | 13. Delta table optimisation |
| Choose the appropriate Delta optimization technique (deletion vectors, Liquid Clustering, CLUSTER BY AUTO) for a given table access pattern. | 13. Delta table optimisation |
| Understand how caching (Delta cache) improves query performance for repeated reads of the same data. | 14. Finding performance problems and errors |
| Apply Change Data Feed (CDF) to expose row-level changes (updates/deletes) for efficient incremental downstream processing. | 13. Delta table optimisation |
| Use the query profile to identify performance bottlenecks, such as data-skipping inefficiencies, join strategies, and shuffle operations. | 14. Finding performance problems and errors |
| Compare Liquid Clustering vs partitioning/ZORDER for a given table size and query pattern. | 13. Delta table optimisation |
| Section 6: Ensuring Data Security and Compliance (8%) | |
| Apply least-privilege access control lists (ACLs) to secure Unity Catalog securable objects and workspace resources. | 15. Unity Catalog access and governance |
| Apply attribute-based access control (ABAC) policies with governed tags to enforce row filters and column masks at scale. | 15. Unity Catalog access and governance |
| Apply anonymization and pseudonymization methods — such as hashing, tokenization, suppression, and generalization — to confidential data (e.g., using column masks and related Unity Catalog features). | 16. Privacy, PII and data retention |
| Implement a compliant data pipeline that enforces PII detection and masking controls across batch and streaming workloads using Unity Catalog features. | 16. Privacy, PII and data retention |
| Implement a data purging strategy that satisfies data retention and deletion policies (e.g., GDPR right-to-erasure, GDPR right-to-be-forgotten deletion) using Delta Lake and Unity Catalog features. | 16. Privacy, PII and data retention |
| Section 7: Data Governance (5%) | |
| Demonstrate understanding of Unity Catalog tags and comments as mechanisms for adding metadata to securable objects to improve data discoverability. | 15. Unity Catalog access and governance |
| Demonstrate understanding of the Unity Catalog permission inheritance model as a mechanism for managing access control across catalogs, schemas, and objects. | 15. Unity Catalog access and governance |
| Section 8: Debugging and Deploying (10%) | |
| Identify pertinent diagnostic information using Spark UI, cluster logs, system tables, and query profiles to troubleshoot errors. | 14. Finding performance problems and errors |
| Analyze the errors and remediate the failed job runs with job repairs and parameter overrides. | 10. Lakeflow Jobs |
| Deploy Databricks resources using Declarative Automation Bundles (formerly Databricks Asset Bundles). | 11. Testing, Git folders and bundles |
| Configure Git-based CI/CD workflows using Databricks Git folders (formerly Repos) to deploy notebooks and code. | 11. Testing, Git folders and bundles |
| Section 9: Data Modeling (5%) | |
| Design scalable Delta/Iceberg table layouts for large data assets by mapping partitioning to data grain, aligning clustering to relationship access patterns, and maintaining balanced file sizes through compaction. | 17. Data modelling on the lakehouse |
| Design dimensional models for analytical workloads, leveraging Materialized Views for pre-computed aggregation and Unity Catalog Metric Views for governed, reusable metric definitions, to ensure efficient querying and aggregation. | 17. Data modelling on the lakehouse |
The full list of tasks under each objective is in the official DBX-DEP exam guide. The book is organised by technology, so one chapter often serves several objectives.