Microsoft DP-750 Free Practice Test | Azure Databricks 91 Real Exam Questions

Microsoft DP-750 Practice Test

The Microsoft DP-750 practice test helps data engineers prepare for questions covering Azure Databricks environments, Unity Catalog governance, ingestion, transformation, data quality, Lakeflow pipelines, jobs, deployment, monitoring, and performance optimization. You can Explore the complete exam practice collection while using this guide to understand the official domains and organize your preparation around practical data-engineering skills.

Microsoft names DP-750 Implementing Data Engineering Solutions Using Azure Databricks. It is aimed at professionals who build, secure, deploy, and maintain data solutions using Azure Databricks and Unity Catalog. Candidates need working knowledge of SQL and Python, batch and streaming patterns, data modeling, Git-based development, Microsoft Entra, Azure Data Factory, and Azure Monitor.

This article follows Microsoft’s official DP-750 study guide. It explains the current objective areas in student-friendly language without turning the page into an overly technical reference manual. The goal is to help learners understand what to practise, how the topics connect, and how to use mock assessments to locate weak areas.

What Is the Microsoft DP-750 Practice Test?

A Microsoft DP-750 practice test is an exam-aligned assessment that checks whether you can make appropriate decisions while implementing data-engineering solutions in Azure Databricks. Good questions do more than ask what a feature is called. They describe a requirement and ask you to select a compute option, data format, ingestion method, security control, pipeline design, deployment method, or troubleshooting action.

Practice scenarios may ask you to:

• Select job, serverless, warehouse, classic, or shared compute.
• Configure compute for performance, cost, workload type, or access.
• Organize catalogs, schemas, volumes, tables, and views in Unity Catalog.
• Grant privileges and implement row- or column-level protection.
• Choose a batch or streaming ingestion approach.
• Select between Lakeflow Connect, notebooks, Azure Data Factory, SQL, or Auto Loader.
• Clean, transform, validate, and load data.
• Design Lakeflow Jobs and declarative pipelines.
• Apply Git, testing, and deployment practices.
• Diagnose Spark performance issues and optimize Delta tables.

The aim is not to reproduce live certification questions. Effective practice uses original scenarios and teaches the reasoning behind each option. That is important because several Azure Databricks features can solve similar problems, but only one may fit the exact requirements for governance, scale, latency, maintainability, or cost.

Who Should Use DP-750 Practice Questions?

DP-750 practice is useful for Azure Databricks data engineers, analytics engineers, lakehouse developers, pipeline developers, and data professionals moving from basic notebook development to production data engineering.

Microsoft expects candidates to have expertise in integrating and modeling data, building optimized pipelines, deploying workloads, troubleshooting failures, and applying data quality and governance practices through Unity Catalog. Candidates should be comfortable using SQL and Python and understand software development lifecycle practices such as Git branching, pull requests, testing, and deployment.

The role also collaborates with administrators, platform architects, solution architects, data scientists, and analysts. A strong candidate therefore understands not only how to write transformations but also how security, governance, operations, monitoring, and downstream use affect the solution.

Official Microsoft DP-750 Domains and Weightings

Microsoft lists the following four domains in its DP-750 study guide for skills measured from October 19, 2026:

The ranges show approximate emphasis rather than a guaranteed question count. Preparation should give the two 30–35% domains the greatest amount of time, but the smaller domains still represent up to 40% when combined. Environment configuration and governance also support nearly every data-processing and deployment decision.

Domain 1: Set Up and Configure an Azure Databricks Environment  15–20%

This domain covers compute selection and configuration plus the creation and organization of Unity Catalog objects.

Select and configure compute

The correct compute choice depends on the workload. Candidates should be able to compare job compute, serverless options, SQL warehouses, classic compute, and shared compute. Rather than memorizing labels, connect each compute type to its typical purpose, management model, isolation needs, start-up expectations, and cost behavior.

Important performance and cost settings include:

• CPU and node type
• Minimum and maximum node count
• Autoscaling
• Automatic termination
• Cluster size
• Pools
• Photon acceleration
• Databricks Runtime or Apache Spark version
• Machine-learning runtime requirements

A practice question may describe an unpredictable workload, a short scheduled job, an interactive SQL use case, or several users sharing resources. Identify the workload pattern before choosing compute.

Candidates should also know how libraries are installed for a compute resource and how access permissions control who can attach to or manage compute. Selecting powerful compute without proper access control or termination settings can create both security and cost problems.

Create and organize Unity Catalog objects

Unity Catalog provides a governance layer for data and AI assets. Its main hierarchy includes catalogs, schemas, and objects such as tables, views, models, and volumes. Naming and organization should reflect requirements for isolation, environments, ownership, and external sharing.

You should understand when to create:

• A catalog as a top-level organizational and governance boundary.
• A schema to organize related objects within a catalog.
• A volume for governed non-tabular files.
• A table for stored tabular data.
• A view for reusable query logic.
• A materialized view when stored, refreshed results support the use case.

The official outline also covers foreign catalogs created through connections. These help users discover and query external data systems through supported federation features.

Candidates should understand data definition language operations for managed and external tables. A managed table allows the platform to manage both metadata and underlying data lifecycle. An external table keeps data lifecycle responsibility outside the table’s managed storage behavior.

AI/BI Genie instructions are also included in the objectives. These instructions help guide business-friendly data discovery and natural-language interactions by providing useful context about the data.

Domain 2: Secure and Govern Unity Catalog Objects  15–20%

This domain focuses on privileges, identities, fine-grained access, secrets, lineage, metadata, retention, audit logging, and secure sharing.

Secure Unity Catalog objects

Privileges in Unity Catalog are granted to principals such as users, groups, and service principals. A well-designed access model normally assigns permissions to groups rather than managing every user separately. It should follow least privilege and separate responsibilities between platform administration, data ownership, engineering, and consumption.

Candidates should understand access at the catalog, schema, table, view, column, and row levels. Fine-grained controls include:

• Table-level permissions
• Column-level access controls
• Row-level security
• Row filters
• Column masks
• Attribute-based access control using tags and policies

Microsoft Entra service principals can authenticate automated workloads. Managed identities can provide Azure resource access without requiring manually stored credentials. Practice questions may ask which identity is most appropriate for a scheduled process or how to reduce secret management.

Azure Key Vault secrets can be accessed from Azure Databricks for supported scenarios. Even when a secret store is used, avoid hard-coding sensitive values in notebooks or source control.

Govern Unity Catalog objects

Governance includes more than access. Clear table and column names, definitions, descriptions, ownership, and tags help users understand and discover data. Good metadata reduces duplicated work and makes policies easier to apply.

Data lineage shows how data moves between sources, tables, notebooks, queries, and downstream objects. Catalog Explorer can help users review owners, history, dependencies, and lineage. Lineage is valuable for impact analysis, troubleshooting, compliance, and trust.

The official objectives include data-retention policies and audit logging. Retention requirements may be driven by regulation, business needs, cost, or data lifecycle rules. Audit logs support investigation and monitoring by recording relevant activity.

Delta Sharing enables controlled data sharing with recipients. A secure strategy should consider the data being shared, recipient identity, permissions, scope, revocation, monitoring, and whether the design exposes more information than necessary.

Domain 3: Prepare and Process Data  30–35%

This is one of the two largest domains. It covers modeling, ingestion, transformation, loading, and data quality.

Design data modeling in Unity Catalog

A data-engineering solution begins with source and consumption requirements. Candidates should design ingestion logic based on the extraction type, source system, file type, expected volume, latency, update pattern, and failure behavior.

Microsoft lists several ingestion tools and approaches:

• Lakeflow Connect
• Azure Databricks notebooks
• Azure Data Factory
• SQL-based loading methods
• Spark Structured Streaming
• Lakeflow Spark Declarative Pipelines
• Auto Loader

The correct choice depends on the source, supported connector, orchestration needs, batch or streaming requirement, operational ownership, and expected scale.

Candidates should compare file and table formats such as Parquet, Delta, CSV, JSON, and Iceberg. Delta tables add capabilities that support reliable lakehouse processing. CSV and JSON are common exchange formats but often require more careful schema and quality handling.

Data layout also matters. Partitioning can improve performance when it matches access patterns, but excessive or poorly chosen partitions create overhead. Liquid clustering and Z-ordering are optimization approaches for different requirements. Do not select an optimization only because it sounds more advanced; match it to the data size, query patterns, and table behavior.

The objectives include slowly changing dimensions, temporal history, and granularity. You should recognize when a business needs current values only, preserved history, or a specific level of detail. Managed versus unmanaged tables is another design decision involving data lifecycle and governance responsibility.

Ingest data into Unity Catalog

Batch processing handles bounded data, while streaming handles continuously arriving records. Some solutions combine both. Questions may describe latency requirements, source capabilities, data volume, or recovery expectations and ask you to choose the appropriate method.

Lakeflow Connect supports ingestion through managed connectors. Notebooks can implement custom batch and streaming logic. Azure Data Factory can orchestrate movement and integrate Azure data sources and services.

SQL ingestion methods in the official outline include:

• CREATE TABLE AS SELECT for creating and populating a table from query results
• CREATE OR REPLACE TABLE for replacing a table definition and content through supported logic
• COPY INTO for idempotent file ingestion patterns

Change data capture focuses on processing inserts, updates, and deletes from a source rather than repeatedly loading every record. Spark Structured Streaming supports scalable stream processing. Azure Event Hubs can serve as a streaming source. Auto Loader efficiently discovers and processes new cloud files through Lakeflow Spark Declarative Pipelines or supported streaming patterns.

Cleanse, transform, and load data

Before transforming data, profile it. Summary statistics and distributions reveal unexpected values, missing data, duplicates, skew, and possible quality issues. Choose column data types deliberately because incorrect types can cause errors, poor performance, or inaccurate calculations.

Transformation skills in the official outline include:

• Filtering, grouping, and aggregation
• Joins and unions
• Intersect and except operations
• Denormalization
• Pivoting and unpivoting
• Merge, insert, and append loading patterns

A merge supports matching source and target records for insert or update logic. Append adds new rows, while insert behavior depends on the target and statement. Choose the loading operation based on whether existing records may change.

Implement and manage data quality

Data quality checks can validate nullability, cardinality, ranges, and data types. Schema enforcement rejects unexpected structures, while schema evolution or drift management controls how changes are handled.

Lakeflow Spark Declarative Pipelines supports expectations that define data-quality rules. Depending on the requirement, invalid records may be retained, dropped, quarantined, or cause processing to fail. A strong design makes quality outcomes visible and measurable instead of silently accepting unreliable data.

As you Sharpen your skills with Microsoft exam-style questions, practise tracing data from source to governed target: choose the ingestion method, define the schema, transform records, enforce quality, and load them into the correct Unity Catalog object.

Practice Support That Turns Mistakes Into Data-Engineering Skills

A strong DP-750 preparation experience should make it easy to identify why a solution works. Useful support can include:

• Questions organized by all four Microsoft domains
• Separate practice for SQL, Python, streaming, governance, and operations
• Scenario-based decisions rather than definition-only questions
• Explanations for correct and incorrect choices
• Domain-level scoring and topic-level weak-area reporting
• Review lists for guessed or missed questions
• Fresh question sets to reduce memorization
• Progress tracking across repeated attempts

For every missed question, record four things: the requirement, your chosen answer, the correct decision, and the clue you overlooked. For example, a scenario requiring file discovery at scale and incremental ingestion may point toward Auto Loader, while a supported packaged source connector may point toward Lakeflow Connect.

This method develops reusable judgment. It also prevents the common problem of remembering a feature name without knowing when to use it.

Domain 4: Deploy and Maintain Data Pipelines and Workloads  30–35%

The final domain covers pipeline design, Lakeflow Jobs, development lifecycle processes, deployment, monitoring, troubleshooting, and optimization.

Design and implement data pipelines

Pipeline design starts with task order and dependencies. Each stage should have clear inputs, outputs, failure handling, restart behavior, observability, and ownership.

Candidates should know when to use a notebook-based pipeline and when Lakeflow Spark Declarative Pipelines offers a better managed, declarative approach. A notebook provides flexible procedural logic, while declarative pipelines can simplify dependency management, data quality, and operational behavior for supported data-processing patterns.

Lakeflow Jobs can coordinate notebooks, pipelines, scripts, and other tasks. Task logic should define dependencies and suitable compute. Error handling should address retries, idempotency, logging, bad records, partial failures, and notifications.

Notebook pipelines may use precedence constraints so that a task runs only after required earlier tasks succeed or meet a particular condition. Declarative pipelines express data dependencies through definitions rather than manually sequencing every operation.

Implement Lakeflow Jobs

Candidates should be able to create and configure jobs, define triggers, schedule executions, configure alerts, and set automatic restarts where appropriate.

Common triggers include scheduled time, file arrival, or other supported events. The design should match business latency and cost requirements. Running a job too frequently can waste compute, while running too slowly can fail service expectations.

Alerts should reach the people or systems that can act. Automatic restart can recover from transient failure, but repeated restart without investigation may hide a deeper issue. Practice questions often test when automation is helpful and when an operator should investigate.

Implement development lifecycle processes

Production data engineering needs controlled change. Git supports version history, collaboration, branching, pull requests, and conflict resolution. Candidates should understand how changes move from development through testing and release.

The official outline includes several testing levels:

• Unit tests for small pieces of logic
• Integration tests for interactions between components
• End-to-end tests for the complete data flow
• User acceptance testing for business validation

Declarative Automation Bundles package Databricks resources and configuration for repeatable deployment. Candidates should know how bundles can be deployed through the Databricks CLI or REST APIs. The aim is consistent, reviewable deployment rather than manual configuration that differs between environments.

Monitor, troubleshoot, and optimize workloads

Monitoring covers reliability, performance, and cost. Cluster consumption should be reviewed so resources match workload needs. Over-sized compute wastes money, while under-sized compute causes delays and instability.

Lakeflow Jobs support repair, restart, stop, and run operations. Candidates should identify whether to rerun the entire workflow or repair only failed tasks. For notebooks and Apache Spark jobs, troubleshooting may involve code, compute, storage, dependencies, data volume, or resource bottlenecks.

Important Spark performance issues include:

• Skew, where some partitions contain far more data than others
• Spilling, where processing exceeds available memory and writes to disk
• Shuffle overhead, caused by redistributing data between tasks
• Caching problems, such as caching unnecessary or stale data

The Directed Acyclic Graph, Spark UI, and query profile help diagnose execution. Rather than changing random settings, use evidence to identify the expensive stage, imbalance, or resource constraint.

Delta table optimization includes OPTIMIZE for improving file layout and VACUUM for removing data files that are no longer required after the retention safety period. Candidates should understand the operational and recovery implications instead of running these commands blindly.

Azure Monitor and Log Analytics support centralized observability. Log streaming and alerts can help operations teams identify failures, resource problems, and unusual behavior.

Use Visit Edurely and plan your next exam within a broader study routine that includes Microsoft Learn, Azure Databricks documentation, notebook practice, Unity Catalog exercises, and hands-on pipeline troubleshooting.

How to Use a DP-750 Practice Test Effectively

Begin with a mixed diagnostic assessment before extensive revision. Answer without searching documentation, but mark guessed and slow responses. The goal is to discover your starting point.

Sort weak questions into a simple review table:

After reviewing a topic, take a short targeted quiz. Then use a new mixed set. Fresh questions show whether you can transfer your understanding to another scenario.

Do not review only incorrect answers. A correct answer selected by guesswork is still a weak area. Add it to your review list and explain the reason in your own words.

A Student-Friendly DP-750 Study Structure

Stage 1: Build the workspace foundation

Review compute choices, runtime settings, libraries, permissions, and Unity Catalog hierarchy. Create sample catalogs, schemas, tables, views, and volumes in a safe environment when available.

Stage 2: Add security and governance

Practise grants, service principals, managed identities, Key Vault access, row filters, column masks, tags, ABAC, lineage, auditing, retention, and Delta Sharing.

Stage 3: Design the data flow

Choose source, extraction type, format, batch or streaming method, ingestion tool, target table design, partitioning, history, and clustering. Draw the flow before writing code.

Stage 4: Transform and validate

Use SQL and Python to profile, cleanse, join, aggregate, reshape, and load data. Add schema enforcement and measurable quality expectations.

Stage 5: Operationalize the solution

Build jobs and pipelines, add dependencies and error handling, version the code, test it, package deployment, and monitor execution.

Stage 6: Troubleshoot with evidence

Use logs, Spark UI, DAG views, query profiles, cluster metrics, and Azure Monitor. Diagnose the cause before changing compute or code.

Common DP-750 Preparation Mistakes

One mistake is treating DP-750 as a notebook syntax exam. Coding matters, but the outline also covers environment design, governance, deployment, monitoring, and optimization.

Another mistake is choosing tools by habit. A notebook is not always the best ingestion or orchestration option. Consider supported connectors, maintainability, latency, governance, and operational responsibility.

Candidates may also confuse access controls. Table grants, row filters, column masks, and ABAC policies operate at different levels. Read exactly what the user must see or be prevented from seeing.

Poor practice plans ignore streaming and change data capture. These topics require understanding state, checkpoints, incremental processing, schema changes, and recovery—not only batch loading.

Finally, avoid “fixing” every performance problem by increasing cluster size. Use the DAG, Spark UI, and query profile to find skew, spill, shuffle, inefficient queries, or table-layout problems first.

Frequently Asked Questions

What is Microsoft DP-750?

DP-750 is the Microsoft exam for implementing data-engineering solutions using Azure Databricks. It supports the Microsoft Certified: Azure Databricks Data Engineer Associate certification path.

Which DP-750 domains have the greatest weight?

Prepare and process data and deploy and maintain data pipelines and workloads each carry 30–35%.

Is DP-750 suitable for beginners?

It is an intermediate, role-based exam. Candidates should already understand data-engineering concepts and have experience with SQL, Python, notebooks, Azure Databricks, Unity Catalog, cloud storage, Microsoft Entra, and Git fundamentals.

Does DP-750 include Unity Catalog?

Yes. Unity Catalog appears throughout the objectives, including object organization, access, governance, modeling, ingestion, quality, lineage, retention, audit, and sharing.

Does the exam cover streaming data?

Yes. The outline includes streaming ingestion, Spark Structured Streaming, Azure Event Hubs, Auto Loader, CDC, and Lakeflow Spark Declarative Pipelines.

What should a DP-750 practice test include?

It should include all four official domains, use original scenario-based questions, provide rationales, and cover design, implementation, governance, deployment, troubleshooting, and optimization.

How much SQL and Python should I know?

You should be comfortable using both for ingestion, transformation, loading, data quality, and troubleshooting. Focus on solving data-engineering requirements rather than memorizing every language feature.

What is the best way to study Spark performance?

Run workloads, inspect the DAG and Spark UI, identify expensive stages, and practise recognizing skew, shuffle, spill, caching, and resource bottlenecks.

Where should I verify the latest DP-750 objectives?

Use Microsoft’s official DP-750 study guide. Check the version that applies to your planned exam date because Microsoft can update skills as Azure Databricks features and role expectations change.