DP-750 is the exam for Microsoft Certified: Azure Databricks Data Engineer Associate, an intermediate credential. The announced blueprint effective October 19, 2026, puts the most emphasis on preparing and processing data and on deploying and maintaining pipelines. Use that blueprint as a dated guide: the available official information does not establish whether the exam was still in beta on October 7, 2026, or which blueprint applied before October 19.
What DP-750 covers
Microsoft describes the target candidate as a data engineer who integrates and models data, builds and deploys optimized pipelines, and troubleshoots and maintains Azure Databricks workloads. The role spans environment configuration, Unity Catalog governance, data preparation and processing, and pipeline deployment and maintenance.
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The announced blueprint is labeled effective October 19, 2026. Microsoft notes that its objective bullets illustrate how a skill may be assessed and that related topics may also appear. Most questions cover generally available features, though commonly used preview features may be included.
| Domain | Weight in the announced October 19, 2026 blueprint | What to study |
|---|---|---|
| Set up and configure an Azure Databricks environment | 15–20% | Compute choices and settings, libraries and permissions, Unity Catalog organization, table types, foreign catalogs, and Genie instructions. |
| Secure and govern Unity Catalog objects | 15–20% | Grants, access controls, identities, secrets, policies, lineage, auditing, retention, and Delta Sharing. |
| Prepare and process data | 30–35% | Ingestion, formats, modeling and layout, transformations, data quality, schema handling, and pipeline expectations. |
| Deploy and maintain data pipelines and workloads | 30–35% | Pipeline and job design, software lifecycle and deployment, workload operations, performance troubleshooting, and monitoring. |
The weights indicate relative emphasis, not a guaranteed number of questions. The two 30–35% domains deserve substantial study time, but the blueprint still assigns 15–20% to each of the other areas.
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How to prepare for each domain
1. Environment setup and configuration
Be ready to choose compute based on the workload and its operating requirements, rather than memorizing a single preferred configuration. The blueprint includes job compute, serverless, warehouses, classic compute, and shared compute. Review CPU and node counts, autoscaling, termination, node types and cluster size, pools, Photon, runtime and Spark versions, ML features, library installation, and compute permissions.
Also practice organizing Unity Catalog objects: catalogs, schemas, volumes, tables, views, and materialized views. Know how naming conventions can separate environments, support isolation, and enable external sharing. The guide also includes foreign catalogs, DDL for managed and external tables, and configuring AI/BI Genie instructions.
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2. Unity Catalog security and governance
Study how privileges are granted to users, groups, and service principals, and how table- and column-level controls differ from row-level security. The blueprint also covers Key Vault secrets, service-principal authentication for data access, and managed identities for resource access.
Governance extends beyond permissions. Prepare to work with table and column definitions, tags, attribute-based access control policies, row filters, column masks, retention, lineage in Catalog Explorer, audit logs, and secure Delta Sharing.
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3. Data preparation and processing
This is one of the two largest weighted domains. Start by learning to choose an ingestion approach for the source, data format, and whether the workload is batch or streaming. Microsoft names Lakeflow Connect, notebooks, and Azure Data Factory among the tools, and the blueprint includes Parquet, Delta, CSV, JSON, and Iceberg.
For ingestion, review SQL CTAS, CREATE OR REPLACE TABLE, and COPY INTO; change data capture (CDC); Structured Streaming; Azure Event Hubs; Lakeflow Spark Declarative Pipelines; and Auto Loader. Understand the choices as alternatives for different requirements, not as a universal ranking.
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For modeling and layout, cover partitioning, granularity, slowly changing dimensions, temporal history tables, liquid clustering, Z-ordering, and managed versus unmanaged tables. Then practice transformations and quality work: profiling, data types, duplicate and null handling, filtering, aggregation, joins, set operations, pivoting, merge, insert, and append. Include validation constraints, schema enforcement and drift, and pipeline expectations in your review.
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The other largest weighted domain combines design, delivery, and operations. Review pipeline ordering and the distinction between notebook pipelines and Lakeflow Spark Declarative Pipelines. For Lakeflow Jobs, study task logic, setup, triggers and schedules, alerts, automatic restarts, error handling, and notebook precedence constraints.
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For the development lifecycle, prepare for Git workflows involving branches, pull requests, and conflict resolution, as well as unit, integration, end-to-end, and user acceptance testing. The blueprint includes Declarative Automation Bundles and deployment through the Databricks CLI or REST APIs.
Operational topics include consumption and cost, repairing, restarting, stopping, and running jobs, plus Spark and notebook troubleshooting. Know how to investigate resource bottlenecks and use the DAG, Spark UI, or query profile to examine caching, skew, spilling, and shuffle. Review Delta OPTIMIZE and VACUUM, and Azure Monitor Log Analytics and alerts.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.A practical study sequence
- Start with Microsoft’s DP-750 study guide. Use the announced October 19 blueprint to make a checklist of its four domains and their objectives. If your exam date is before October 19, check Microsoft’s live exam and credential pages for the applicable blueprint; the available information does not establish the preceding blueprint’s weights.
- Allocate time by weight, then by gaps. Give substantial time to data preparation and pipeline operations because each has a 30–35% weighting, while reserving focused review for the two 15–20% domains. Adjust based on your experience rather than treating the percentages as a prescribed timetable.
- Practice with real workflows. Build hands-on familiarity with ingestion, transformations, Unity Catalog controls, job setup, deployment, and troubleshooting. Microsoft’s study guide recommends training and hands-on experience before taking the exam.
- Use the official practice resources. Microsoft links an exam sandbox and a practice assessment hosted in AI Skills Navigator; sign-in is required to launch the assessment. Training options include self-paced and instructor-led learning. Supplement as needed with the referenced Azure Databricks, Azure Data Factory, Microsoft Entra, and Azure Monitor documentation.
- Recheck the blueprint and exam state close to scheduling. Microsoft Skills Hub introduced DP-750 as a beta exam on March 10, 2026, but the information available as of October 7 does not confirm whether it remained in beta. Check Microsoft’s current credential page and study guide for status, dates, and any updates.
Exam logistics to plan around
Microsoft lists a 120-minute exam, proctored and potentially including interactive components. The study guide states that a score of 700 or greater is required to pass. The listed languages are English, Japanese, Chinese (Simplified and Traditional), Korean, German, French, Italian, Portuguese (Brazil), and Spanish. Price varies by region, so confirm the amount shown for your location when scheduling.
Microsoft recommends registering with a personal Microsoft account. If exam records are attached to a work or school account, they may become unrecoverable if you leave that organization.
Quick Recap
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