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Microsoft’s DP-750 exam is the assessment for the intermediate Microsoft Certified: Azure Databricks Data Engineer Associate credential. Its announced blueprint takes effect October 19, 2026, and concentrates most of its weight on data preparation and pipeline deployment and maintenance. Microsoft lists the exam at 120 minutes and requires a score of at least 700 to pass.
The assignment calls DP-750 a beta exam, but Microsoft’s sources confirm its beta launch on March 10, 2026—not whether it remains in beta as of October 7, 2026. Check the current credential page before scheduling. The detailed scope below is the blueprint Microsoft labels effective October 19, not a claim about the blueprint governing an earlier test date.
What the DP-750 exam covers
Microsoft frames the role around integrating and modeling data, building and deploying optimized pipelines, and troubleshooting and maintaining Azure Databricks workloads. Candidates are expected to work with data quality and Unity Catalog governance, use SQL and Python for ingestion and transformation, follow software development lifecycle practices such as Git, and be familiar with Microsoft Entra, Azure Data Factory, and Azure Monitor.
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The study guide’s October 19, 2026 blueprint divides the exam into four domains. Microsoft notes that objective bullets are examples of how a skill may be assessed, and related topics may also appear. Most questions cover generally available features, though commonly used preview features may be included.
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| Domain | Weight |
|---|---|
| Set up and configure an Azure Databricks environment | 15–20% |
| Secure and govern Unity Catalog objects | 15–20% |
| Prepare and process data | 30–35% |
| Deploy and maintain data pipelines and workloads | 30–35% |
These percentages are Microsoft’s published domain weights, not a promise of a particular number of questions. Allocate substantial study time to processing and pipeline operations, while reserving deliberate review for configuration and governance.
Study the four skill domains
1. Set up and configure the environment
Be ready to select compute based on workload and access needs: job compute, serverless, warehouses, classic compute, or shared compute. Review CPU and node choices, node counts, autoscaling, termination, cluster sizing, pooling, Photon, runtime and Spark versions, ML features, library installation, and compute permissions.
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For data organization, learn how catalogs, schemas, volumes, tables, views, and materialized views fit together in Unity Catalog. Practice choosing naming conventions that support isolation, environments, and external sharing; understand foreign catalogs; and recognize DDL differences for managed and external tables. The blueprint also includes configuring AI/BI Genie instructions.
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Review how grants apply to users, service principals, and groups, and how access can be controlled at table, column, or row level. Know the roles of Key Vault secrets, service-principal authentication for data access, and managed identities for resource access.
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Governance coverage includes table and column definitions, tags, attribute-based access control policies, row filters, column masks, retention, lineage in Catalog Explorer, audit logging, and secure Delta Sharing. Study these as related controls: who can access an object, what data they can see, and how use is documented and governed.
3. Prepare and process data
This is one of the two largest domains. Start with ingestion choices: extraction method, file type, tool, and whether data arrives in batches or continuously. The guide names Lakeflow Connect, notebooks, and Azure Data Factory, along with Parquet, Delta, CSV, JSON, and Iceberg. Know when design decisions involve partitioning, slowly changing dimensions, data granularity, temporal history tables, liquid clustering, Z-ordering, or managed versus unmanaged tables.
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Be able to recognize and work through the listed ingestion approaches: Lakeflow Connect, notebooks, SQL CTAS, CREATE OR REPLACE TABLE, COPY INTO, change data capture (CDC), Structured Streaming, Azure Event Hubs, Lakeflow Spark Declarative Pipelines, and Auto Loader.
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4. Deploy and maintain pipelines and workloads
Understand how to order pipeline work and choose between notebooks and Lakeflow Spark Declarative Pipelines. For Lakeflow Jobs, study task logic, setup, triggers, schedules, alerts, automatic restarts, error handling, notebook pipelines, and precedence constraints.
For development and release, cover Git, branches, pull requests, conflict resolution, and testing at the unit, integration, end-to-end, and user acceptance (UAT) levels. Know the role of Declarative Automation Bundles and how deployment can use the Databricks CLI or REST APIs.
Operations objectives include monitoring consumption and cost; repairing, restarting, stopping, and running jobs; troubleshooting Spark and notebooks; identifying resource bottlenecks; and investigating caching, skew, spilling, and shuffle through DAGs, Spark UI, and query profiles. Review Delta OPTIMIZE and VACUUM, plus Azure Monitor Log Analytics and alerts.
How to prepare for Microsoft DP-750
- Confirm the applicable exam version. Check Microsoft’s credential page and DP-750 study guide close to your test date. The guide updated September 18, 2026 labels its detailed blueprint effective October 19; the available official information does not establish which earlier blueprint applies before that date or whether the exam remains beta on October 7.
- Use the study guide as a coverage checklist. Map each objective to a concrete task you can perform or explain, then prioritize weak areas. Use the weights to guide time allocation rather than trying to predict question counts.
- Get hands-on practice. Build or inspect workflows that cover ingestion, transformations, Unity Catalog permissions, job orchestration, deployment, and troubleshooting. Microsoft’s guide explicitly recommends training and hands-on experience before taking the exam.
- Try the official practice assessment and sandbox. Microsoft links a practice assessment hosted in AI Skills Navigator and an exam sandbox from its exam resources; sign-in is required to launch the practice assessment. Use the sandbox to familiarize yourself with the exam experience, not as a substitute for practical work.
- Review adjacent Azure services. Follow the guide’s resources for Azure Databricks, Azure Data Factory, Microsoft Entra, and Azure Monitor, especially where your experience is less developed.
- Schedule and prepare your account. The exam is proctored and may include interactive components. Microsoft recommends registering with a personal Microsoft account because exam records associated with a work or school account may become unrecoverable if you leave that organization.
Exam length, languages, and passing score
Microsoft lists a 120-minute exam and a passing score of 700 or greater. The credential page lists English, Japanese, Chinese (Simplified and Traditional), Korean, German, French, Italian, Portuguese (Brazil), and Spanish. Exam pricing depends on region, so check the live registration page for the amount that applies to you.
Microsoft identifies the credential as intermediate. Its official preparation resources include the study guide, training options, hands-on experience, an exam sandbox, and a practice assessment. The assessment requires sign-in; its availability does not establish any particular question count.
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