Microsoft Certified: Azure Databricks Data Engineer Associate (DP‑750) Practice Exams

About the Azure DP-750 exam

Exam at a glance

DP-750, Implementing Data Engineering Solutions Using Azure Databricks, is Microsoft's associate certification for the Databricks data engineer. Passing it earns the Microsoft Certified: Azure Databricks Data Engineer Associate credential. The exam is built end to end around the Databricks lakehouse on Azure: you stand up compute and a Unity Catalog namespace, secure and govern the data, prepare and process it into trustworthy Delta Lake tables, then deploy and operate the pipelines that keep those tables fresh.

Domain weighting

• Set up and configure an Azure Databricks environment: 15-20%
• Secure and govern Unity Catalog objects: 15-20%
• Prepare and process data: 30-35%
• Deploy and maintain data pipelines and workloads: 30-35%

Who this exam is for

DP-750 targets data engineers who integrate and model data, build and deploy optimized pipelines, and troubleshoot and maintain workloads on Azure Databricks, applying data quality and governance best practices in Unity Catalog. Questions are scenario-based and implementation-focused, mixing SQL, Python, notebooks, Lakeflow pipelines, and the Databricks workspace. You work alongside administrators, platform and solution architects, data scientists, and data analysts.

Prerequisites

There are no formal prerequisites. Microsoft expects you to ingest and transform data with SQL and Python, follow software development lifecycle practices including Git, and be familiar with Microsoft Entra, Azure Data Factory, and Azure Monitor. A working understanding of core Azure services and Apache Spark rounds out the background the exam assumes.

Why take this certification

• The Databricks credential on Azure. DP-750 is Microsoft's role-based certification for data engineering on Azure Databricks, so it maps directly to how lakehouse teams build and run data platforms today.
• Unity Catalog governance front and center. Two of the four domains center on securing and governing data with Unity Catalog, the skill that separates a working notebook from a production, audited data estate.
• Tools teams actually use. The blueprint is built around Delta Lake, Photon, Auto Loader, Lakeflow Declarative Pipelines, Lakeflow Jobs, Databricks Asset Bundles, and the Spark UI, the day-to-day toolkit of a Databricks data engineer.
• Builder and operator altitude. This exam rewards judgment over recall. It proves you can put pipelines into production, keep them healthy, and tune them for cost and performance, not just write a one-off transformation.

What you'll learn in the DP-750 exam

DP-750 is hands-on and scenario-driven. Most questions pair a goal with a constraint and offer options that all technically work, then ask for the one Azure Databricks is built to prefer. A single instinct runs through every domain: when two answers both work, choose the managed, governed, least-privilege, incremental option, and depart from it only when the scenario forces a more manual one. That is masking a sensitive column instead of revoking access, or loading new rows with Auto Loader instead of a full reload.

Core services and tools you'll be tested on

• Environment setup: compute types including job compute, serverless, SQL warehouse, classic, and shared compute; performance settings such as autoscaling, node type, cluster size, pooling, and termination; feature settings including Photon acceleration and the Databricks runtime; compute libraries and access permissions; and Unity Catalog catalogs, schemas, volumes, tables, views, and materialized views, foreign catalogs, managed versus external tables, and AI/BI Genie.
• Security and governance: granting privileges to users, service principals, and groups; table- and column-level access control and row-level security; Azure Key Vault secrets; service principals and managed identities; attribute-based access control with tags and policies; row filters and column masks; data retention; data lineage in Catalog Explorer; audit logging; and a secure Delta Sharing strategy.
• Prepare and process data: data modeling with Delta, Parquet, and Iceberg, partitioning, slowly changing dimensions, temporal history tables, liquid clustering, Z-ordering, and deletion vectors; ingestion with Lakeflow Connect, notebooks, CTAS, COPY INTO, change data capture feeds, Spark Structured Streaming, Azure Event Hubs, and Auto Loader; cleansing and transformation with profiling, deduplication, joins, unions, pivots, and merge, insert, and append loads; and data quality with validation checks, schema enforcement, and Lakeflow pipeline expectations.
• Deploy and maintain: designing pipelines and choosing between notebooks and Lakeflow Declarative Pipelines; Lakeflow Jobs with triggers, schedules, alerts, and automatic restarts; the development lifecycle with Git branching, pull requests, testing, Databricks Asset Bundles, and the Databricks CLI and REST APIs; and monitoring, troubleshooting, and optimization with the Spark UI, the DAG and query profile, OPTIMIZE and VACUUM, and log streaming to Azure Monitor.

Operational judgment patterns you'll need to recognize

• Choosing the managed, governed default, such as serverless compute, a managed table, or a managed identity over a stored key, unless a stated constraint rules it out.
• Protecting sensitive data with a column mask or row filter so a query keeps running, rather than revoking access outright.
• Loading incrementally with Auto Loader, COPY INTO, or a change data capture feed instead of a full reload when only new rows have arrived.
• Declaring a flow as a Lakeflow Declarative Pipeline and letting the platform own dependencies, retries, and lineage, instead of hand-wiring orchestration.
• Enforcing trust at write time with data quality expectations and schema enforcement so bad records never reach a downstream table.
• Diagnosing skew, spill, and shuffle from the Spark UI and query profile, then optimizing Delta tables with liquid clustering, OPTIMIZE, and VACUUM for cost and performance.

How the practice exams help

Each free question and every premium exam mirrors the scenario style Microsoft uses: a short stem with constraints and several plausible options. Detailed explanations cover not just why the right answer is right but why each distractor is wrong, and cite the official Microsoft and Azure Databricks documentation, so you learn the trade-offs rather than memorizing answers.

How to prepare for the DP-750 exam

A successful DP-750 plan combines structured study with daily hands-on time in an Azure Databricks workspace. A recommended approach:

1. Study the skills measured (2 to 3 weeks). Walk the official DP-750 study guide and the DP-750 exam page. Prioritize the two data-and-pipeline domains, Prepare and process data and Deploy and maintain data pipelines and workloads, which each carry 30 to 35% and together make up most of the exam.
2. Hands-on labs (2 to 3 weeks). Stand up a workspace, create a Unity Catalog catalog, schema, and volume, and choose the right compute for the job. Ingest files with Auto Loader, model Delta tables with liquid clustering, and build a Lakeflow Declarative Pipeline with data quality expectations. Secure a table with a column mask and a row filter, then wire lineage and audit logging so you can see governance working.
3. Practice deployment and operations (1 week). Orchestrate your pipeline as a Lakeflow Job with triggers, alerts, and automatic restarts, package it as a Databricks Asset Bundle, and promote it with the Databricks CLI. Practice reading the Spark UI and query profile to diagnose skew and spill, and stream logs to Azure Monitor. Automation and reproducibility run through every domain and are often the difference between two otherwise valid answers.
4. Practice exams (1 to 2 weeks). Start with the free 10-question sample above, then work through the 25 full practice exams. Take them timed to surface weak areas, and lean on the detailed explanations to learn the reasoning rather than memorize answers. Aim for consistent scores above 80% before you schedule, and use the complete interactive DP-750 study guide to close any gaps.

Recommended timeline

Plan on 6 to 10 weeks of focused study (8 to 12 hours per week) for engineers who already have some Spark or Databricks exposure. If Unity Catalog governance or Lakeflow pipelines are new to you, spend the extra time on the two data-and-pipeline domains, which together account for the majority of the exam.

Official resources

Read the official DP-750 study guide for the exact skills measured, review the DP-750 exam page for scheduling and training options, and keep the Azure Databricks documentation open as your reference while you build. Pair those with the complete interactive DP-750 study guide and these practice tests.

Frequently asked questions