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. Tailspin Toys' data engineering team is investigating a slow Lakeflow Job that performs a join between a 450 GB Delta table and a 50 GB lookup table. The job launches 200 tasks in the join stage, but 195 tasks complete in under 30 seconds while 5 tasks each take over 45 minutes. A data engineer opens the Spark UI Stage Details view to determine the root cause. Which metric most definitively identifies data skew as the cause of the slow tasks? (Select one!)
ALow CPU utilization on all executors shown in the Hardware Metrics tab, indicating the cluster is undersized for the workload
BHigh GC time values across all executor threads in the Executors tab, indicating JVM heap pressure across the cluster
CA large discrepancy between the minimum and maximum input data size per task within the Stage Details task list
DAn abnormally high total shuffle write size in the Stage summary metrics, indicating excessive data movement between executors
Explanation
Data skew occurs when certain partition key values contain a disproportionately large number of rows, causing specific tasks to receive and process far more data than others. In the Spark UI Stage Details view, this pattern is visible as a large variance between the minimum and maximum input bytes and record counts across tasks in the same stage. When the majority of tasks finish in seconds while a small number take tens of minutes, and the Stage Details confirms those slow tasks have input sizes orders of magnitude larger than the median task, data skew is definitively the root cause. High GC time across all executors points to a general memory pressure issue affecting the whole cluster rather than an imbalanced distribution across individual tasks. A high total shuffle write size indicates a large shuffle operation but does not isolate the problem to specific skewed partitions. Low CPU utilization across all executors suggests blocking I/O or an underpowered cluster configuration, not an uneven distribution of data across partition keys.
2. Relecloud's compliance team discovers that a data engineer who currently owns the Unity Catalog table gold.finance.sensitive_ledger can view all column values without restriction, even after the team applies a column mask to the pii_data column. The compliance team expected the mask to hide values from users who are not members of the finance_admins group. Which statement explains this behavior and describes the correct action the compliance team must take? (Select one!)
AThe table owner always bypasses all Unity Catalog access control policies including column masks and row filters, and the only way to restrict the engineer's access is to transfer table ownership to another principal
BColumn masks are only enforced on SQL warehouses; the data engineer is accessing the table from an all-purpose cluster that bypasses masking policies regardless of configuration
CThe compliance team must first revoke the USE CATALOG and USE SCHEMA privileges from the data engineer before the column mask policy can take effect for the current owner
DThe column mask function must explicitly list the data engineer's user principal name in the exclusion logic to override the group-based membership condition
Explanation
In Unity Catalog, a table owner unconditionally bypasses all access control policies — including GRANT and REVOKE statements, column masks, and row filters — regardless of how those policies are configured. The owner always has full, unrestricted access to the objects they own. The only mechanism to restrict an owner's access is to transfer table ownership to a different user or service principal using ALTER TABLE ... OWNER TO. Once ownership is transferred, the former owner loses the ownership bypass and becomes subject to normal Unity Catalog privilege evaluation and data security policies. Column mask enforcement is not affected by cluster type — masks apply consistently across SQL warehouses and shared-mode cluster access. Explicitly listing the engineer in the mask function would still be overridden by the ownership bypass. Revoking USE CATALOG and USE SCHEMA prevents namespace navigation for regular users but has no effect on an object owner who bypasses the entire ACL system.
3. Contoso's data engineering team processes files deposited to an ADLS Gen2 container on a periodic schedule. They run a Structured Streaming job and need it to process all currently available files across multiple parallel micro-batches and then automatically shut down to minimize cluster costs, with no manual termination step required. Which trigger configuration meets this requirement? (Select one!)
Atrigger(continuous="1 second")
Btrigger(processingTime="1 hour")
Ctrigger(availableNow=True)
Dtrigger(once=True)
Explanation
trigger(availableNow=True) processes all data currently available in the source using multiple parallel micro-batches and then automatically terminates the stream. This provides faster throughput than Trigger.Once while still achieving automatic shutdown, making it ideal for cost-optimized scheduled ingestion. trigger(processingTime="1 hour") schedules micro-batches at a fixed cadence but runs indefinitely and never stops on its own, requiring manual intervention. trigger(once=True) also stops automatically after processing available data, but it uses only a single micro-batch rather than parallel micro-batches, which is significantly slower for large data volumes. trigger(continuous="1 second") provides sub-millisecond end-to-end latency but is designed for always-on real-time pipelines and never stops automatically.
4. Fabrikam's platform engineering team needs to ensure that all data engineer-created clusters use only approved instance types, always apply a 60-minute auto-termination timeout, and can only select from a fixed list of approved Databricks Runtime versions. The team wants to enforce these constraints at the workspace level without modifying individual user permissions in Azure. What should the platform team configure? (Select one!)
ADeploy Databricks Asset Bundles with hardcoded cluster configurations that override user selections when engineers create clusters
BCreate an Azure RBAC role scoped to approved instance types and assign it to the data engineers group in the Azure portal
CCreate a cluster policy with fixed values for instance type, auto-termination, and runtime version, then assign the policy to the data engineers group
DConfigure workspace-level cluster settings in the Azure portal to restrict instance type selection and enforce auto-termination globally
Explanation
Cluster policies in Azure Databricks allow administrators to define fine-grained constraints on cluster configuration parameters, including fixed values that cannot be modified, allowable ranges, and enforced defaults. By creating a policy with fixed values for instance type, auto-termination duration, and runtime version, the platform team ensures every cluster created under that policy conforms to organizational standards. The policy is then assigned to the data engineers group so all cluster creation goes through these constraints. Azure RBAC roles control access to Azure resource management operations but cannot enforce Databricks-internal cluster configuration constraints such as runtime version or auto-termination timeout. Workspace-level cluster settings in the Azure portal provide broad controls but do not offer the granular per-parameter enforcement that policies provide. Databricks Asset Bundles define job and pipeline infrastructure as code but do not restrict or govern interactive cluster creation performed by data engineers outside of bundle deployments.
5. Adventure Works' data engineering team manages an existing Unity Catalog managed Delta table containing hundreds of millions of rows. The table has a column named cust_id. Following a company-wide naming convention audit, they must rename the column to customer_id without rewriting any underlying Parquet data files. Which action must be performed before running the ALTER TABLE RENAME COLUMN command? (Select one!)
AEnable column mapping by executing ALTER TABLE SET TBLPROPERTIES with delta.columnMapping.mode set to name
BRun CONVERT TO DELTA on the table to upgrade it to the latest Delta protocol version before the rename
CRun VACUUM with RETAIN 0 HOURS to purge all old file versions prior to the column rename
DRun OPTIMIZE on the table to compact all small files before performing the schema change
Explanation
Delta Lake column mapping must be enabled before renaming or dropping columns. By default, Delta Lake stores physical column names directly inside Parquet files, meaning a column rename without column mapping enabled would require rewriting every underlying data file — an operation that is prohibitively expensive at hundreds of millions of rows. Enabling delta.columnMapping.mode = name switches to name-based mapping, which records a logical-to-physical column name translation in the Delta transaction log rather than in the data files themselves. This allows column renames and drops to be recorded as metadata-only operations without touching the Parquet files, satisfying the constraint. Running OPTIMIZE compacts small files for query performance but does not enable column rename capability. Running VACUUM purges old file versions for storage reclamation and has no bearing on whether column renames are supported. CONVERT TO DELTA migrates plain Parquet tables into Delta format and is not applicable to tables already registered as Delta tables in Unity Catalog.
More Microsoft Practice Exams
Microsoft Certified: AI Business Professional (AB-730)
AB-730 · 699 questions
Microsoft Certified: AI Transformation Leader (AB-731)
AB-731 · 700 questions
Microsoft Certified: Azure AI Cloud Developer Associate (AI-200)
AI-200 · 600 questions
Microsoft Certified: Intelligent Applications Builder Associate (AB-410)
AB-410 · 600 questions
Microsoft Certified: Machine Learning Operations (MLOps) Engineer Associate (AI-300)
AI-300 · 583 questions
Microsoft Certified: SQL AI Developer Associate (DP-800)
DP-800 · 600 questions
$17.99
One-time access to this exam
593 questions (11 practice exams' worth)
Unlimited timed exam simulations
Yours forever — no subscription
Detailed explanations
Free preview stays available