The short version
The exam guide dated October 9, 2026 lists nine sections. Section 1, Developing Code, is 23%; Cost & Performance is 15%; the rest range from 5% to 12%. Below, each section gets what it covers, a typical trap, and what to practice. The traps are our general observations about how such questions tend to work, not Databricks statements.
Section 1: Developing Code for Data Processing using Python and SQL (23%)
What it covers. Python project structure for Declarative Automation Bundles (formerly Databricks Asset Bundles), fixing dependency and installation problems, UDFs (pandas, Python, SQL, and Unity Catalog functions), streaming pipelines with Lakeflow Pipelines and Auto Loader, Lakeflow Jobs through the UI, API, and CLI, streaming table versus materialized view, CDC with AUTO CDC (formerly APPLY CHANGES) including SCD Type 1 and 2, Structured Streaming versus declarative pipelines, If/Else and For Each in Jobs, compute choices including serverless, tests with `assertDataFrameEqual` and `assertSchemaEqual`, and stateful streaming (watermarks, output modes, `foreachBatch`, checkpoints).
Typical trap. Picking the familiar tool instead of the one the constraint points to. A question that stresses “full history including late-arriving updates” is steering you toward a materialized view, not a streaming table.
Practice. Build one pipeline end to end, then break it: change a schema, replay late data, and see what the checkpoint and watermark do.
Section 2: Data Ingestion & Acquisition (12%)
What it covers. Ingesting Delta Lake, Parquet, JSON, Iceberg, CSV, and binary data from message buses (Kafka, Kinesis, Pub/Sub) and cloud storage; incremental CDC into Delta or Iceberg targets; CDC from SQL Server, MySQL, and PostgreSQL with Lakeflow Connect; OpenSharing (Databricks-to-Databricks and Databricks-to-open) and Clean Rooms; Lakehouse Federation with Unity Catalog permissions and connection-level credentials.
Typical trap. Mixing up sharing and federation. Sharing publishes your data outward; federation lets Databricks query an outside database.
Practice. Create an open share, read it from a plain client, and write down which recipient types each sharing mode serves.
Section 3: Data Manipulation (12%)
What it covers. Window functions, joins, and aggregations in Spark SQL and PySpark; modeling and querying semi-structured data with VARIANT (`parse_json`, `variant_get`); AI functions such as `ai_query` for enrichment and classification inside pipelines; data quality expectations that quarantine, drop, or fail on bad records.
Typical trap. Expectations. Drop, fail, and warn behave differently, and “never lose a rejected record” points to quarantine, not drop.
Practice. Write the same rule three ways and compare what lands in the target and in the metrics.
Section 4: Monitoring and Alerting (10%)
What it covers. System tables (billing, compute, access, lakeflow) for cost, auditing, and workload monitoring; REST APIs, CLI, and SDK for jobs and pipelines; pipeline event logs for health and quality; Lakehouse alerts; Jobs UI and API for status and performance.
Typical trap. Choosing a UI-only answer when the stem asks for something queryable, repeatable, or joinable. System tables and event logs are tables, so you can query them.
Practice. Write a query that attributes last month’s usage to jobs, and another that reads expectation results from an event log.
Section 5: Cost & Performance Optimization (15%)
What it covers. How managed tables, Predictive Optimization, and liquid clustering lower maintenance; choosing among deletion vectors, liquid clustering, and `CLUSTER BY AUTO`; Delta cache; Change Data Feed for row-level changes; reading the query profile for skipping problems, join strategies, and shuffles; liquid clustering versus partitioning and ZORDER.
Typical trap. Applying a fix to the wrong symptom. Deletion vectors help frequent small updates and deletes; clustering helps selective reads. Both can sound right in a stem about “slow table”.
Practice. Take one table, run the same filtered query under different layouts, and compare the query profile each time.
Section 6: Ensuring Data Security and Compliance (8%)
What it covers. Least-privilege ACLs on Unity Catalog objects; attribute-based access control with governed tags driving row filters and column masks; hashing, tokenization, suppression, and generalization; PII detection and masking in batch and streaming pipelines; purging data to meet retention and erasure rules such as GDPR.
Typical trap. Forgetting that deleted data may live on in older table versions. A purge strategy must think about history, not only the current rows.
Practice. Delete a record from a table, then check what time travel still returns and what it takes to remove it for good.
Section 7: Data Governance (5%)
What it covers. Tags and comments as metadata for discoverability, and the permission inheritance model across catalogs, schemas, and objects.
Typical trap. Assuming a grant applies only to existing objects. A privilege granted at a higher level covers objects created later.
Practice. Grant at catalog level, create a new table, and confirm who can read it.
Section 8: Debugging and Deploying (10%)
What it covers. Using Spark UI, cluster logs, system tables, and query profiles to find errors; repairing failed job runs with parameter overrides; deploying with Declarative Automation Bundles; Git-based CI/CD with Databricks Git folders.
Typical trap. Rerunning everything when a repair of only the failed and dependent tasks would do.
Practice. Force a task to fail in a multi-task job, then repair it.
Section 9: Data Modeling (5%)
What it covers. Delta or Iceberg table layouts: partitioning mapped to data grain, clustering aligned with access patterns, balanced file sizes through compaction; dimensional models using materialized views for pre-computed aggregates and Unity Catalog metric views for governed metric definitions.
Typical trap. Using a metric view when the problem is speed, or a materialized view when the problem is inconsistent definitions. They solve different problems and often work together.
Practice. Define one metric once, then build the aggregate that dashboards read.
More guides for DB-DE-PRO
- Databricks Data Engineer Pro Exam Format
- Is the Databricks Data Engineer Pro Hard?
- Databricks Data Engineer Pro Prerequisites
- DE Pro Renewal and Retakes Explained
- Is Databricks Data Engineer Pro Worth It?
- DE Pro Cheat Sheet: Key Facts to Review
- DE Pro vs Spark Associate: Which First?
- DP-750 vs Databricks Data Engineer Pro
Next step
Turn this list into a calendar with the study plan, or try five questions in sample questions. Our practice sets report your score by domain.