DP-750 is the exam for Microsoft Certified: Azure Databricks Data Engineer Associate. Its published blueprint covers Azure Databricks setup, Unity Catalog security and governance, data preparation, and pipeline deployment and operations. The two data-focused domains each account for 30–35% of the skills measured, so they deserve the largest share of your preparation time. Microsoft requires a score of 700 or greater to pass, according to its DP-750 study guide.
This guide reflects the English exam objectives in effect on October 4, 2026. Microsoft says the English version will be updated on October 19, 2026; if your exam is on or after that date, check the official study guide again and prepare against the updated objectives.
What DP-750 tests
Microsoft describes the credential as intended for data engineers who integrate and model data, build and deploy optimized pipelines, and troubleshoot and maintain Azure Databricks workloads. The role also involves Unity Catalog data quality and governance, with related skills in SQL, Python, Git, Microsoft Entra, Azure Data Factory, and Azure Monitor. The four domain weights below are Microsoft Learn’s published ranges for the current blueprint; they are percentages of assessed skills, not a promised number or sequence of exam questions.
| Exam domain | Published weight | What to prepare for |
|---|---|---|
| Set up and configure an Azure Databricks environment | 15–20% | Compute choices and configuration; Unity Catalog object organization |
| Secure and govern Unity Catalog objects | 15–20% | Access, identity, policies, lineage, auditing, retention, and sharing |
| Prepare and process data | 30–35% | Ingestion, transformations, modeling, loading, quality, and schemas |
| Deploy and maintain data pipelines and workloads | 30–35% | Pipeline and job design, deployment practices, monitoring, troubleshooting, and optimization |
Microsoft’s full task statements and objective-level details are in the official DP-750 study guide. Use that guide as the definitive checklist rather than relying on a course outline or an older summary.
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What to study in each domain
1. Set up and configure an Azure Databricks environment — 15–20%
Be ready to match the compute option to the workload and explain the trade-offs in a scenario. The guide includes job compute, serverless, SQL warehouses, classic compute, and shared compute. Review performance-related settings such as CPU, node count, autoscaling, auto-termination, node type, cluster sizing, and pools, along with Photon, Databricks Runtime and Spark versions, machine-learning settings, library installation, and compute permissions.
For Unity Catalog setup, study how catalogs, schemas, volumes, tables, views, materialized views, and foreign catalogs are organized and named. Know the distinction between managed and external tables, including their DDL, and review the guide’s coverage of AI/BI Genie instructions.
Rank #2
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2. Secure and govern Unity Catalog objects — 15–20%
Prepare for access-control scenarios involving users, groups, and service principals, including table- and column-level access and row-level security. Know how identities and secrets fit into Azure Databricks workflows: the objectives include Azure Key Vault secrets as well as service-principal and managed-identity authentication.
Governance is broader than granting privileges. Review descriptions and definitions that help people discover data; tag- and policy-based attribute-based access control (ABAC); row filters and column masks; retention; lineage; audit logging; and strategies for secure Delta Sharing.
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Rank #3
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3. Prepare and process data — 30–35%
This is one of the two highest-weighted domains. Study how to choose a source and extraction approach, then select an ingestion method. Microsoft’s objectives name Lakeflow Connect, notebooks, and Azure Data Factory, as well as batch and streaming workloads.
- Formats and ingestion: Understand the listed Parquet, Delta, CSV, JSON, and Iceberg formats, and how ingestion choices relate to the destination and processing pattern.
- Modeling and loading: Review slowly changing dimension (SCD) types, data granularity, temporal history, and when to use merge, insert, or append patterns.
- Layout and performance: Be able to reason about partitioning, liquid clustering, and Z-ordering in the context of the data and query workload.
- Quality and schema: Practice reasoning about validation, nullability, cardinality, ranges, data types, schema enforcement, and schema drift. The guide also includes expectations for Lakeflow Spark Declarative Pipelines.
4. Deploy and maintain data pipelines and workloads — 30–35%
This is the other highest-weighted domain. For pipeline design, review task ordering and error handling, and the situations that call for notebooks versus Lakeflow Spark Declarative Pipelines. For Lakeflow Jobs, study task logic and setup, triggers and schedules, alerts, and restart behavior.
Rank #4
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Deployment objectives cover the development lifecycle as well as the production workload. Review Git workflows, branching, pull requests, conflict resolution, testing levels, Declarative Automation Bundles, and deployment through the CLI or API.
For operations, know how to investigate consumption and cost, repair or restart jobs, and diagnose Spark resource bottlenecks. Microsoft specifically lists using the DAG, Spark UI, and query profile to investigate cache behavior, skew, spill, and shuffle. Also review Delta OPTIMIZE and VACUUM, streaming logs to Log Analytics, and Azure Monitor alerts.
Best Value
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How to prioritize your preparation
Use the published domain weights to allocate study time: begin with data preparation and pipeline deployment and maintenance, then cover environment setup and governance. The percentages are ranges, so treat this as a way to prioritize coverage—not as a prediction of the number of questions. Within each domain, turn the official task statements into a checklist and mark whether you can explain a choice, carry it out, and troubleshoot a failure.
- Read the current blueprint. Open the DP-750 study guide and map each listed skill to a practical example or exercise.
- Build hands-on coverage around the larger domains. Practice data ingestion, transformations, schema and quality checks, job or pipeline deployment, and operational diagnosis. Include the Azure and Databricks services named in the objectives rather than studying isolated definitions.
- Fill the setup and governance gaps. Work through compute selection and configuration, Unity Catalog organization, permissions, identity, policy, lineage, and audit scenarios.
- Check recall under exam conditions. Use Microsoft’s practice assessment and exam sandbox linked from the certification page. Treat practice results as a way to find weak areas, not as a guarantee of an exam result.
- Recheck the blueprint for your test date. The official page announces an English-version update on October 19, 2026. Candidates testing on or after that date should consult the live guide for the effective objectives.
Official preparation resources and exam logistics
Microsoft recommends both training and hands-on experience. Its study guide points candidates to self-paced learning paths and modules, instructor-led training, product documentation, Microsoft Q&A, community support, and videos such as Exam Readiness Zone and Data Exposed. The documentation areas it identifies include Azure Databricks, Azure Data Factory, Microsoft Entra, and Azure Monitor.
The Microsoft certification page links the study guide, practice assessment, exam sandbox, and Pearson VUE scheduling. The practice assessment is available through AI Skills Navigator, and sign-in may be required to launch it. Exam price depends on the country or region where it is proctored, so consult the live certification page for current scheduling and regional details.
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