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. Adatum's data engineering team is building a Lakeflow Spark Declarative Pipeline to process inbound payment transactions. Business requirements state that if any record arrives with a null `payment_id`, the entire pipeline update must halt immediately to prevent incomplete data from propagating to downstream Gold layer tables. Which expectation decorator should the team apply to the Bronze dataset function? (Select one!)
A@dp.expect_or_quarantine("valid_payment_id", "payment_id IS NOT NULL")
B@dp.expect("valid_payment_id", "payment_id IS NOT NULL")
C@dp.expect_or_fail("valid_payment_id", "payment_id IS NOT NULL")
D@dp.expect_or_drop("valid_payment_id", "payment_id IS NOT NULL")
Explanation
The expect_or_fail decorator implements the ON VIOLATION FAIL UPDATE behavior in DLT expectations. When any record violates the specified constraint, the pipeline update fails immediately and atomically rolls back the entire transaction. No records from the batch are committed, including rows that passed the expectation. This prevents incomplete or corrupted data from propagating to downstream Gold layer tables. The expect decorator (warn mode) retains all records including violating ones and logs violations as metrics. The expect_or_drop decorator silently removes violating rows and continues processing the remaining valid records. There is no expect_or_quarantine decorator in the DLT API.
2. Wingtip Toys' IT administrator needs to ensure that when an employee's account is disabled in Microsoft Entra ID following offboarding, the employee's corresponding Azure Databricks account is automatically deactivated to prevent unauthorized access. The company wants this deactivation to occur without manual intervention from the Databricks workspace administrator. Which configuration should the administrator implement? (Select one!)
AEnable SCIM provisioning between Microsoft Entra ID and the Azure Databricks Premium workspace
BCreate a scheduled Lakeflow Job that queries the Microsoft Graph API daily and deactivates matching Databricks user accounts
CSet up Azure Monitor alerts to notify the Databricks workspace administrator when a disabled user attempts to sign in
DConfigure Entra ID Conditional Access policies to block authentication for disabled accounts from reaching Azure Databricks
Explanation
SCIM (System for Cross-domain Identity Management) provisioning automatically synchronizes the full user and group lifecycle from Microsoft Entra ID to Azure Databricks in near real time. When an employee account is disabled or deleted in Entra ID, SCIM immediately propagates that deactivation to the corresponding Databricks account, revoking access without manual intervention. SCIM provisioning requires an Azure Databricks Premium tier workspace. Conditional Access policies control the conditions under which interactive browser-based authentication is permitted but do not automatically deactivate the Databricks account itself — a disabled Entra ID user who retains a valid Databricks Personal Access Token could still authenticate directly to the API. Azure Monitor alerts notify administrators of events but do not take automated remediation actions. A custom Lakeflow Job requires significant development and operational overhead, introduces up to 24 hours of delay between offboarding and deactivation depending on schedule frequency, and is far more error-prone than the native SCIM integration.
3. Tailspin Toys is optimizing costs for a multi-task Lakeflow Job. A data engineer has built a job with three tasks: a Python wheel task that runs a packaged ETL library, a JAR task that executes a custom Scala data quality framework, and a Spark Submit task that launches a legacy batch processing script. The engineer wants to use serverless compute wherever possible to reduce infrastructure overhead. Which statement accurately describes the serverless compute compatibility for these three task types in Azure Databricks? (Select one!)
APython wheel and Spark Submit tasks support serverless compute; only JAR tasks require classic jobs compute
BOnly the Python wheel task supports serverless compute; both JAR and Spark Submit tasks require classic jobs compute
CAll three task types—Python wheel, JAR, and Spark Submit—support serverless compute in Azure Databricks
DPython wheel and JAR tasks support serverless compute; Spark Submit tasks require classic jobs compute
Explanation
Azure Databricks serverless compute for Lakeflow Jobs supports the notebook, Python script, dbt, Python wheel, and JAR task types. JAR task support on serverless compute is currently in Public Preview, meaning the data engineer can assign serverless compute to both the Python wheel and JAR tasks in this job. Spark Submit tasks are the most restricted task type — they cannot run on serverless compute or all-purpose compute for job execution, and must be assigned to a classic jobs compute cluster. The correct approach is to configure the Python wheel and JAR tasks with serverless compute and assign the Spark Submit task to classic jobs compute. Assuming only Python wheel tasks support serverless is incorrect because Databricks has expanded serverless support to include JAR tasks. Assuming Spark Submit supports serverless is incorrect — it is explicitly excluded from serverless and all-purpose compute for scheduled job runs.
4. Northwind Traders' Unity Catalog administrator grants a BI analyst the SELECT privilege on the table reporting.finance.quarterly_results. When the analyst runs SELECT * FROM reporting.finance.quarterly_results in a SQL warehouse, they receive an access error. No other privileges have been assigned to the analyst. Which two additional grants are the minimum required to resolve the error? (Select two!)
Multiple correct answersAGRANT ALL PRIVILEGES ON TABLE reporting.finance.quarterly_results TO the analyst
BGRANT USE CATALOG ON CATALOG reporting TO the analyst
CGRANT MODIFY ON SCHEMA reporting.finance TO the analyst
DGRANT USE SCHEMA ON SCHEMA reporting.finance TO the analyst
EGRANT READ VOLUME ON ALL VOLUMES IN SCHEMA reporting.finance TO the analyst
Explanation
Unity Catalog enforces a three-part privilege chain to access a securable object: the user must hold USE CATALOG on the parent catalog, USE SCHEMA on the parent schema, and the specific data privilege (such as SELECT) on the target table. Granting only SELECT is insufficient — without namespace navigation privileges, the user cannot traverse the catalog hierarchy to resolve the table reference. USE CATALOG on the reporting catalog and USE SCHEMA on the reporting.finance schema are therefore both required as the minimum additional grants. Critically, USE CATALOG and USE SCHEMA do not themselves grant any data access — they only enable namespace navigation. Granting ALL PRIVILEGES is unnecessarily permissive for a read-only analyst. MODIFY grants write access, which is unrelated to resolving a read query error. READ VOLUME applies to Volume objects and is irrelevant to table access.
5. Litware's data engineering team uses COPY INTO to load CSV files from ADLS Gen2 into the Delta table silver.retail.sales. During a development testing cycle, the team wants to reload a set of files that were successfully ingested in a prior run in order to validate changes made to a downstream data quality transformation. They understand this will produce duplicate records in the target table. Which COPY INTO configuration enables reloading the previously processed files? (Select one!)
ADrop and recreate the target Delta table before executing COPY INTO
BInclude COPY_OPTIONS ('mergeSchema' = 'true') to allow schema reprocessing
CInclude COPY_OPTIONS ('force' = 'true') to bypass file tracking
DInclude FORMAT_OPTIONS ('rescuedDataColumn' = '_rescued_data') to reprocess all rows
Explanation
COPY INTO is idempotent by default. It maintains an internal file tracking ledger that records which files have already been successfully loaded, and subsequent executions skip those files to prevent duplicate ingestion. Setting COPY_OPTIONS ('force' = 'true') disables this tracking mechanism for the current run, causing COPY INTO to treat all matching files in the source path as unprocessed and reload them regardless of prior ingestion history. This is the correct option when intentional reprocessing of already-loaded files is required, such as in a development testing workflow. The mergeSchema option governs schema evolution behavior during ingestion and has no effect on whether previously loaded files are re-read. Dropping and recreating the target table permanently deletes all existing data, which is a destructive and unnecessary approach for a testing scenario that only requires reprocessing specific files. The rescuedDataColumn option captures incoming data that does not match the inferred schema into a separate column and is entirely unrelated to file tracking or idempotency behavior.
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