Microsoft · DP-750
Microsoft Certified: Azure Databricks Data Engineer Associate (DP-750) Practice Tests
Validates expertise in implementing data engineering solutions using Azure Databricks, including integrating and modeling data, building and deploying optimized pipelines, and applying data quality and governance best practices with Unity Catalog.
Exam Details
Practice Questions
593
≈ 11 practice exams
Duration
120 minutes
Passing Score
700/1000
Difficulty
AssociateLast Updated
May 2026
Topics Covered
Set Up and Configure Azure Databricks EnvironmentSecure and Govern Unity Catalog ObjectsPrepare and Process DataDeploy and Maintain Data Pipelines and Workloads
Microsoft Certified: Azure Databricks Data Engineer Associate (DP-750) Practice Exam Preparation
Use this DP-750 practice exam to prepare for Microsoft Certified: Azure Databricks Data Engineer Associate (DP-750) with realistic questions, detailed explanations, and focused study modes. The practice bank includes 593 questions for Microsoft DP-750, so you can review the exam steadily instead of relying on one long cram session.
As you practice, pay extra attention to recurring topics such as Set Up and Configure Azure Databricks Environment, Secure and Govern Unity Catalog Objects, Prepare and Process Data, and Deploy and Maintain Data Pipelines and Workloads. Start with short sessions to identify weak areas, then move into timed quizzes once your accuracy is consistent.
The explanations are especially useful when you want to connect exam wording to the responsibilities and scenarios described in the official certification guidance. Use the free preview first, then unlock the full question bank when you are ready to build a complete study routine.
Exam Domain Breakdown
Set up and configure an Azure Databricks environment18%
Secure and govern Unity Catalog objects18%
Prepare and process data33%
Deploy and maintain data pipelines and workloads33%
Exam Overview
The Microsoft Certified: Azure Databricks Data Engineer Associate (Exam DP-750) validates subject matter expertise in implementing end-to-end data engineering solutions on the Azure Databricks platform. The certification covers the full lakehouse engineering lifecycle, from configuring workspaces and compute resources to ingesting, transforming, and modeling data using Delta Lake, then deploying and maintaining production-grade pipelines with Lakeflow Jobs and Lakeflow Spark Declarative Pipelines. A core emphasis is placed on Unity Catalog, Microsoft and Databricks' unified governance layer, which candidates must know how to use for securing objects, managing data lineage, enforcing row- and column-level access controls, and applying data quality expectations.
This certification was introduced in beta in March 2026 and reached general availability in May 2026, reflecting the rapid enterprise adoption of Azure Databricks as a foundational data and AI platform. Certified engineers are expected to work proficiently in both SQL and Python, apply software development lifecycle (SDLC) practices including Git-based version control and Databricks Asset Bundles, and integrate Azure services such as Microsoft Entra for identity management, Azure Data Factory for orchestration, and Azure Monitor for observability. The exam tests not only implementation skills but also the ability to troubleshoot Spark jobs, resolve performance bottlenecks such as skewing and spilling, and optimize Delta tables using techniques like liquid clustering and OPTIMIZE/VACUUM commands.
Who Should Take This Exam
This certification is designed for data engineers who design, build, and maintain data pipelines and lakehouse architectures on Azure Databricks in production environments. Ideal candidates hold roles such as Azure Databricks Data Engineer, Cloud Data Engineer, or Analytics Engineer, and collaborate closely with platform architects, solution architects, data scientists, and data analysts. The certification is positioned at the associate (intermediate) level, making it appropriate for professionals who have hands-on experience building data solutions in the cloud but are not yet operating at an expert or architect level.
Candidates should be comfortable writing data transformation logic in both SQL and Python, managing version control with Git, and working within the Azure ecosystem. Engineers currently using Azure Synapse Analytics, Azure Data Factory, or other cloud data platforms who are transitioning to or expanding into Azure Databricks will find this certification a strong validation of their upskilled capabilities.
Prerequisites
Microsoft does not enforce formal prerequisites for Exam DP-750, but the official study guide makes clear that candidates should arrive with meaningful hands-on experience. Specifically, candidates are expected to know how to ingest and transform data using SQL and Python, apply SDLC practices including Git branching and pull request workflows, and be familiar with Microsoft Entra (for authentication via service principals and managed identities), Azure Data Factory, and Azure Monitor. A solid understanding of Apache Spark concepts—including DataFrames, Structured Streaming, and the Spark execution model (DAGs, shuffle, caching)—is essential for the performance troubleshooting and optimization portions of the exam.
Practical familiarity with Unity Catalog concepts (catalogs, schemas, volumes, managed vs. external tables, privileges, and data lineage) is strongly recommended, as governance topics account for 15–20% of the exam. Candidates who have completed the official instructor-led course DP-750T00-A or equivalent self-paced Microsoft Learn paths will be well-positioned. Prior experience with the Databricks Certified Data Engineer Associate exam from Databricks itself provides useful conceptual overlap, though the DP-750 places greater emphasis on Azure-native integrations and Unity Catalog governance.
Exam Format
Exam DP-750 is a proctored assessment delivered through Pearson VUE, available online (at-home proctoring) or at a testing center. Candidates have 120 minutes to complete the assessment. A passing score of 700 out of 1000 is required; Microsoft uses a scaled scoring system where question difficulty factors into the final score, so the passing threshold does not correspond directly to a fixed percentage of correct answers. The exam is currently offered in English only, though candidates who take the exam in a non-primary language can request an additional 30 minutes.
The exam may include a variety of question types such as multiple choice, multiple select, drag-and-drop, and interactive lab-style components (as noted in the official exam policy). Microsoft does not publish an exact question count for DP-750. The certification renews annually and can be renewed at no cost by passing a free online assessment on Microsoft Learn, typically available within eight weeks of the exam reaching general availability.
Skills Measured
- 1.Set up and configure an Azure Databricks environment (15–20%): Covers selecting and configuring compute types (job compute, serverless, SQL warehouse, classic, shared), performance settings (autoscaling, node type, pooling, Photon acceleration), Databricks Runtime/Spark version selection, library installation, and access permissions. Also includes creating and organizing Unity Catalog objects such as catalogs, schemas, volumes, tables, views, materialized views, and foreign catalogs via Delta Sharing connections.
- 2.Secure and govern Unity Catalog objects (15–20%): Covers granting privileges to users, service principals, and groups; implementing table-, column-, and row-level access controls; accessing Azure Key Vault secrets; authenticating data access via service principals and managed identities; configuring attribute-based access control (ABAC) with tags; applying row filters and column masks; managing data retention policies; tracking data lineage via Catalog Explorer; configuring audit logging; and designing secure Delta Sharing strategies.
- 3.Prepare and process data (30–35%): Covers designing and implementing data models in Unity Catalog, including choosing ingestion tools (Lakeflow Connect, notebooks, Azure Data Factory), loading methods (batch vs. streaming), table formats (Delta, Parquet, Iceberg, CSV, JSON), partitioning schemes, SCD types, temporal tables, and clustering strategies (liquid clustering, Z-ordering, deletion vectors). Also includes ingesting data via Lakeflow Connect, Auto Loader, Spark Structured Streaming, Azure Event Hubs, CDC feeds, and SQL methods (CTAS, COPY INTO). Cleansing, transforming (filtering, grouping, joins, pivoting, denormalizing), and loading data (merge, insert, append) are included, along with implementing data quality constraints such as nullability checks, schema enforcement, and pipeline expectations in Lakeflow Spark Declarative Pipelines.
- 4.Deploy and maintain data pipelines and workloads (30–35%): Covers designing and implementing data pipelines using notebooks and Lakeflow Spark Declarative Pipelines, implementing Lakeflow Jobs (triggers, schedules, alerts, automatic restarts, error handling), applying Git-based version control (branching, pull requests, conflict resolution), implementing testing strategies (unit, integration, end-to-end, UAT), packaging and deploying Databricks Asset Bundles via CLI and REST APIs, monitoring cluster consumption, troubleshooting Spark jobs using the DAG, Spark UI, and query profile, resolving caching/skewing/spilling/shuffle issues, optimizing Delta tables with OPTIMIZE and VACUUM, and configuring Azure Monitor alerts and Log Analytics log streaming.
Study Tips
- Start with the official DP-750 Study Guide on Microsoft Learn (aka.ms/DP750-StudyGuide), which maps every exam objective to specific sub-skills with percentage weights. Use it as your master checklist to identify gaps rather than studying topics uniformly.
- Complete the official instructor-led course DP-750T00-A or its self-paced equivalent on Microsoft Learn. The course is structured around the four exam domains and includes hands-on labs covering Unity Catalog setup, Lakeflow Jobs, Lakeflow Spark Declarative Pipelines, and pipeline monitoring—all high-weight exam areas.
- Get hands-on with Unity Catalog in a real Azure Databricks workspace. The exam heavily tests governance tasks such as granting privileges, configuring row filters and column masks, setting up data lineage tracking, and applying ABAC tags—skills that require practical experience, not just conceptual reading.
- Practice building and deploying Databricks Asset Bundles using the Databricks CLI and REST APIs, as the deploy-and-maintain domain (30–35%) includes bundle configuration and deployment. Use the official Azure Databricks documentation on Asset Bundles to work through realistic deployment scenarios.
- Use the exam sandbox at go.microsoft.com/fwlink/?linkid=2226877 to familiarize yourself with the exam interface and interactive question types before test day. Knowing the UI reduces cognitive load during the actual exam.
- Deep-dive into Spark performance troubleshooting using the Spark UI and query profile. The exam tests your ability to identify and resolve skewing, spilling, shuffling, and caching issues—review the DAG visualization and understand how stages and tasks map to physical execution.
- Review the Azure Databricks documentation for Lakeflow Spark Declarative Pipelines (formerly Delta Live Tables) with a focus on pipeline expectations for data quality, Auto Loader configuration for incremental ingestion, and CDC feed handling—these topics appear across both the data processing and pipeline deployment domains.
Career Benefits
Azure Databricks data engineers in the US command average salaries of approximately $137,000 per year, with senior and lead roles on the Azure platform typically ranging from $150,000 to $190,000. Databricks appeared in 16.8% of data engineering job postings in 2026, and the broader data engineering field has added over 20,000 new roles in the past year with projected growth of 34% through 2034 according to U.S. Bureau of Labor Statistics data. The DP-750 targets the intersection of Microsoft Azure infrastructure and the Databricks lakehouse platform, making it directly relevant for roles such as Azure Databricks Data Engineer, Cloud Data Engineer, Analytics Engineer, and Data Platform Engineer at organizations running Azure-native data stacks.
Compared to the vendor-neutral Databricks Certified Data Engineer Associate exam, the DP-750 provides stronger validation of Azure-specific integrations—Microsoft Entra, Azure Monitor, Azure Data Factory, and Delta Sharing in Unity Catalog—making it the more compelling choice for engineers working within Microsoft-centric enterprise environments. The certification renews annually via a free online assessment, keeping credentialed professionals current as the platform evolves. Microsoft has positioned DP-750 as part of a broader wave of AI- and data-focused credentials, signaling continued investment in the Azure Databricks certification path.
Sample Questions
5 sample questions with answers and explanations. The full bank has 593 questions, enough for 11 full-length practice exams.
Preview — answers shown1. Wide World Importers' data engineering team enables Change Data Feed on a Unity Catalog Delta table and builds a Structured Streaming pipeline to process incremental changes downstream. Which three metadata columns are automatically included in every row of the change feed output? (Select three!)
Multiple correct answersA_change_type
B_row_hash
C_commit_version
D_operation_sequence
E_commit_timestamp
F_source_table_name
Explanation
Change Data Feed automatically adds exactly three metadata columns to each row in the feed output. The _change_type column identifies the operation performed on the row, with possible values of insert, update_preimage, update_postimage, and delete — allowing downstream consumers to distinguish inserts from updates and deletions. The _commit_version column records the Delta table transaction version at which the change was committed, enabling ordered replay and version-based filtering. The _commit_timestamp column records the wall-clock timestamp of the transaction commit, supporting time-based filtering and auditing. Together, these three columns give downstream pipelines full context about what changed, in what order, and when. The columns _row_hash, _operation_sequence, and _source_table_name are not part of the Change Data Feed specification and do not appear in the output.
2. Proseware's data engineering team enables Change Data Feed on a Unity Catalog Delta table and reads changes using Structured Streaming. When a single existing row is updated, the CDF output contains two rows for that update operation. Which two values of the _change_type metadata column appear in the CDF output to represent this single update? (Select two!)
Multiple correct answersAupdate_preimage
Bupdate_postimage
Cupdate_before
Dupdate_after
Eupdate_modified
Explanation
When a row is updated in a Delta table with Change Data Feed enabled, the CDF output produces exactly two rows for that operation: one with _change_type set to update_preimage containing the full column values before the update, and one with _change_type set to update_postimage containing the full column values after the update. This dual-row representation allows downstream consumers to precisely identify what changed in each update operation. The values update_before, update_after, and update_modified do not exist in the Change Data Feed specification — the correct and only terminology for update tracking is update_preimage and update_postimage.
3. Litware's data platform team is auditing their Lakeflow Job configuration to migrate as much compute usage to serverless as possible in order to reduce operational overhead. Their job currently contains five task types: Notebook, JAR, Python script, Spark Submit, and Pipeline (Lakeflow SDP). Which two task types do NOT support serverless compute? (Select two!)
Multiple correct answersANotebook task
BJAR task
CPython script task
DSpark Submit task
EPipeline (Lakeflow SDP) task
Explanation
JAR tasks and Spark Submit tasks do not support serverless compute in Lakeflow Jobs. Spark Submit tasks are even more restricted — they only support classic jobs compute and cannot run on all-purpose clusters at all, making them the most constrained task type on the platform. Notebook tasks, Python script tasks, and Pipeline tasks can all be configured to use serverless compute, where Databricks fully manages the underlying cluster lifecycle, node provisioning, and scaling without any operator intervention.
4. Litware's data engineering team is building a Structured Streaming pipeline that reads click events and maintains a running count of clicks per product category. The pipeline must write only the rows whose aggregated counts changed during the current micro-batch to the downstream Delta table sink, avoiding unnecessary rewrites of unchanged rows. Which output mode should they configure? (Select one!)
AComplete mode
BUpdate mode
CAppend mode
DOverwrite mode
Explanation
Update mode writes only the rows that were changed during the current micro-batch to the output sink, making it the correct choice for incremental aggregation pipelines where only updated rows should be emitted downstream. Append mode works only for non-aggregation queries or aggregations with event-time watermarks where rows are never modified after being written; it cannot support running aggregations where previously written rows are updated as new events arrive. Complete mode rewrites the entire result table on every trigger regardless of how many rows changed, which is wasteful when only a subset of aggregation rows are updated per batch. Overwrite mode is not a valid Structured Streaming output mode — it is used in batch DataFrame write operations.
5. Northwind Financial's data analyst analyst@northwindfinancial.com reports being unable to query the Unity Catalog table prod.finance.transactions. A Unity Catalog admin confirms that SELECT has already been granted directly on prod.finance.transactions. Which two additional privileges are required to successfully query the table? (Select two!)
Multiple correct answersAUSE CATALOG on prod
BREAD VOLUME on prod.finance
CUSE SCHEMA on prod.finance
DBROWSE on prod
EMODIFY on prod.finance.transactions
Explanation
Unity Catalog enforces a three-level privilege hierarchy. To query a table, a user must hold SELECT on the table, USE SCHEMA on the parent schema, and USE CATALOG on the parent catalog — all three must be present simultaneously. Simply holding SELECT on prod.finance.transactions is insufficient; the user must also be able to navigate the namespace hierarchy to reach it. USE CATALOG grants the ability to navigate the catalog but does not grant access to any data within it. USE SCHEMA similarly grants namespace navigation within a schema without conferring data access. BROWSE allows viewing metadata such as table names and schemas without requiring USE CATALOG or USE SCHEMA, but it does not enable data queries. READ VOLUME applies to volume objects, not tables. MODIFY grants write access and is entirely unrelated to resolving a read query failure.
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