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. A data analytics team at Northwind Traders shares a single all-purpose cluster among six analysts. The cluster must enforce Unity Catalog fine-grained access controls for each analyst individually, including column masks and row filters, so that each analyst sees only the data they are authorized to access. Analysts use a combination of Python and SQL in their notebooks. Which cluster access mode should the workspace administrator configure? (Select one!)
ASingle User (Dedicated)
BNo Isolation Shared
CHigh Concurrency (Legacy)
DShared
Explanation
Shared access mode is the correct choice when multiple users must share a cluster while maintaining per-user Unity Catalog security enforcement including column masks and row filters. In Shared mode, each query is evaluated against the identity of the individual user who submitted it, ensuring that column masks and row filters are applied correctly for every analyst on the cluster. Shared mode supports both Python and SQL workloads. No Isolation Shared mode allows multiple users on the same cluster but does not enforce Unity Catalog fine-grained security controls, so column masks and row filters are not applied and all users see unfiltered data. Single User (Dedicated) mode enforces Unity Catalog security but is restricted to a single user at a time, making it unsuitable for a shared team environment. High Concurrency mode is a legacy cluster configuration predating Unity Catalog and does not support modern fine-grained access controls such as column masks and row filters.
2. Litware's data engineering team is building a Structured Streaming pipeline that computes running total sales revenue grouped by product category. The downstream reporting system queries the output Delta table at any time and must see complete, up-to-date totals for every product category in a single read. New data arrives continuously. Which Structured Streaming output mode should the team configure? (Select one!)
AOverwrite mode, which replaces all existing rows in the output table with the latest aggregation results each trigger
BUpdate mode, which writes only the product category rows whose totals changed since the last trigger
CAppend mode, which adds only new aggregated rows to the output table on each trigger interval
DComplete mode, which rewrites the entire aggregated result table on every trigger interval
Explanation
Complete mode rewrites the entire result table on every trigger, ensuring that all product categories and their current running totals are always present and consistent when the reporting system queries the output. This satisfies the requirement that a single table read returns a complete, current snapshot of all categories. Append mode cannot be used with stateful aggregations that update previously written values — it is restricted to scenarios where output rows are immutable after being written, making it unsuitable for running totals that grow with every batch. Update mode writes only the rows that changed in the most recent trigger, meaning the output table reflects an incomplete state until all categories have been updated at least once and consumers would not see a full result set in one read. Overwrite mode is not a valid Structured Streaming output mode; it is a batch write mode and cannot be used in a streaming context.
3. Woodgrove Bank's security team plans to implement row-level security using Unity Catalog row filters across multiple data assets in their Azure Databricks workspace. The team wants to understand which asset types support row filter application before designing their governance model. Which two asset types support the application of Unity Catalog row filters? (Select two!)
Multiple correct answersAA managed Delta table registered in Unity Catalog
BA Unity Catalog view defined on top of a managed Delta table
CAn external table stored using an ICEBERG REST catalog format
DA managed Delta table accessed directly through the Delta Lake APIs
EAn external Delta table registered in Unity Catalog
Explanation
Unity Catalog row filters can be applied to managed Delta tables and external Delta tables that are registered in Unity Catalog. Both table types support row filter functions that evaluate user identity or tag conditions at query time to restrict which rows a caller can see. Unity Catalog views explicitly do not support row filter application — attempting to attach a row filter to a view will fail. External tables stored using an ICEBERG REST catalog format are not supported for row filters or column masks due to catalog compatibility restrictions. Accessing a Delta table directly through the Delta Lake APIs bypasses the Unity Catalog enforcement layer entirely, meaning row filters are not applied through this access path and cannot be attached to it.
4. Northwind Traders' data platform team observes that a Lakeflow Job task has been running for over four hours with no progress. The job logs show no errors, tasks remain in a running state, and CPU utilization on the cluster appears near zero. A data engineer suspects executor threads are deadlocked or waiting indefinitely on a blocking operation. Which Spark UI capability should the engineer use to diagnose the root cause? (Select one!)
AThread dumps captured from individual executor and driver JVM processes
BThe SQL and DataFrame tab displaying the physical query plan and operator-level execution metrics
CThe Storage tab listing cached RDD partitions and DataFrame memory footprint per executor
DThe Stages tab filtered to show tasks with the highest input bytes read
Explanation
When Spark tasks are stuck with no progress and no error — a pattern consistent with deadlocks, infinite loops, or blocking I/O — thread dumps provide a full snapshot of the call stack for every active thread in the executor and driver JVM at the moment of capture. This reveals exactly what each thread is waiting on, including lock acquisition attempts, blocked network calls, or recursive loops, without needing the job to fail first. The Stages tab reports I/O, shuffle, and duration metrics for tasks that are actively processing data, but it cannot surface why an executor thread is not advancing at all. The SQL and DataFrame tab shows physical plan execution metrics and operator timing for completed query stages; it does not expose thread-level blocking state. The Storage tab shows memory usage of cached data structures and is unrelated to diagnosing execution hangs caused by concurrency or blocking conditions.
5. Adatum Corporation's data engineering team enables Change Data Feed on a Unity Catalog managed Delta table and reads the changes using Structured Streaming to propagate them to a downstream Silver table. The Silver transformation logic must apply different rules depending on whether each row was inserted, updated, or deleted. When a single row is updated in the source table, what rows appear in the CDF stream for that update operation? (Select one!)
ATwo rows: one with _change_type equal to delete containing the old values, and one with _change_type equal to insert containing the new values
BTwo rows: one with _change_type equal to update_preimage containing the original values before the update, and one with _change_type equal to update_postimage containing the new values after the update
COne row with _change_type equal to update, containing only the new column values after the update
DThree rows: one with _change_type equal to update, one with _change_type equal to update_before, and one with _change_type equal to update_after
Explanation
When Change Data Feed is enabled and a row is updated in a Delta table, the CDF stream emits exactly two rows for each updated record: an update_preimage row containing the original column values before the update, and an update_postimage row containing the new column values after the update. This preimage and postimage pair allows downstream consumers to compare the before and after state of each changed row, enabling accurate change propagation and SCD Type 2 implementations. A single generic update row does not exist in Delta CDF — the preimage and postimage distinction is fundamental to how the feature works and how consumers can reconstruct history. Using delete and insert pairs is a pattern used by some external CDC systems but is not how Delta Lake CDF represents updates. The naming convention update_before and update_after does not exist in Delta Lake's Change Data Feed specification.
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