The short version
DP-750 has four domains. Two are lighter (15-20% each) and two carry the most weight (30-35% each). The topics below follow Microsoft’s skills measured as of October 19, 2026. The traps are our own experience-based advice, not vendor statements, and the Microsoft study guide stays the authority.
Domain 1: Set up and configure an Azure Databricks environment (15-20%)
Select and configure compute. You choose between job compute, serverless, SQL warehouses, classic compute, and shared compute, then tune CPU, node count, autoscaling, termination, node type, pooling, Photon, runtime version, and libraries. You also set who may use the compute.
Create and organize Unity Catalog objects. Catalogs, schemas, volumes, tables, views, and materialized views, plus naming conventions for isolation, environments, and sharing. Foreign catalogs through connections, DDL on managed and external tables, and Genie instructions are included.
Typical traps. Questions often give one deciding requirement: results may be a few hours old (think materialized view), a workload runs once a week (think job compute or serverless over an always-warm pool), or a team needs isolation (think separate catalogs).
What to practice. Create one of each object, compare a standard view with a materialized view, and change compute settings to see the effect on cost and startup.
Domain 2: Secure and govern Unity Catalog objects (15-20%)
Secure objects. Granting privileges to users, groups, and service principals; table-, column-, and row-level control; reading Azure Key Vault secrets; authenticating with service principals and managed identities.
Govern objects. Descriptions for discovery, attribute-based access control with tags and policies, row filters and column masks, retention, lineage in Catalog Explorer, audit logging, and secure Delta Sharing.
Typical traps. Privileges inherit downward, so a grant on a schema also covers tables created later. Candidates also mix up a row filter (which rows) with a column mask (what a column shows), and a view-based approach with a policy-based one.
What to practice. Grant and revoke at catalog, schema, and table level and test with a second principal. Set up a secret scope and a column mask and query as a restricted user.
Domain 3: Prepare and process data (30-35%)
Design the data model. Ingestion design, ingestion tool (Lakeflow Connect, notebooks, Azure Data Factory), batch versus streaming, table format (Parquet, Delta, CSV, JSON, Iceberg), partitioning, SCD type, history tables, liquid clustering versus Z-ordering, and managed versus unmanaged tables.
Ingest data. Lakeflow Connect, notebooks, SQL (CTAS, CREATE OR REPLACE TABLE, COPY INTO), change data capture feeds, Structured Streaming, Azure Event Hubs, and Auto Loader inside declarative pipelines.
Cleanse, transform, and load. Profiling, data types, duplicates and nulls, filtering, grouping, joins and set operators, pivots, and merge, insert, and append loads.
Data quality. Validation checks, type checks, schema enforcement and drift, and pipeline expectations.
Typical traps. A one-time bulk load usually favors a simple batch command, while continuous arrival favors Auto Loader. SCD Type 1 overwrites history and Type 2 keeps it. A “dedupe” question may ask for the extra copies, not the unique rows.
What to practice. Load the same files with COPY INTO and Auto Loader and compare. Write a MERGE that handles inserts, updates, and deletes. Break a schema on purpose and watch enforcement react.
Domain 4: Deploy and maintain data pipelines and workloads (30-35%)
Design pipelines. Order of operations, notebook versus declarative pipeline, task logic for jobs, and error handling.
Lakeflow Jobs. Job creation, triggers, schedules, alerts, and automatic restarts.
Development lifecycle. Git, branching, pull requests, conflict resolution, a testing strategy (unit, integration, end-to-end, UAT), and Declarative Automation Bundles deployed with the CLI or REST APIs.
Monitor, troubleshoot, and optimize. Cluster consumption, repair and restart of failed runs, Spark UI and query profile for caching, skew, spill, and shuffle, OPTIMIZE and VACUUM, log streaming with Log Analytics, and Azure Monitor alerts.
Typical traps. Know what each setting does to runs that overlap or fail: queue, skip, cancel, retry, repair. Read the symptom carefully: high CPU with modest shuffle points somewhere different from heavy spill.
What to practice. Build a job with two tasks, fail one on purpose, and repair it. Read a Spark UI stage page for skew. Run OPTIMIZE and VACUUM and read the history.
More guides for DP-750
- DP-750 Exam Format, Scoring and Retakes
- Is DP-750 Hard? An Honest Difficulty Check
- DP-750 Prerequisites and Skills Check
- DP-750 Renewal and Retakes Explained
- Is DP-750 Worth It? An Honest Look
- DP-750 Cheat Sheet: Key Facts to Review
- DP-750 vs DP-700: Which Exam First?
- DP-750 vs Databricks Data Engineer Pro
Next step
Put the domains on a calendar with the study plan. To see how these topics appear in questions, try the sample questions or the DP-750 practice tests.
Frequently asked questions
Which domains matter most?
Prepare and process data, and deploy and maintain pipelines. Each is 30-35%, together roughly two thirds of the exam.
Do I need to memorize syntax?
Know the common SQL and PySpark patterns well enough to read them and spot errors. Microsoft Learn is available inside the exam for lookups, but time is tight.