Microsoft Certified: Azure Databricks Data Engineer Associate (DP-750)
Intermediate Level
Demonstrate expertise in integrating and modeling data, building and deploying optimized pipelines, and troubleshooting and maintaining workloads in Azure Databricks.
As a data engineer, you understand that AI performance depends directly on the quality of its data. If the data isn’t clean, well-managed, and accessible at scale, even the most sophisticated AI models won’t perform as expected.
Azure Databricks Data Engineer Associate certification is designed for professionals who want to validate their expertise in data engineering using Azure Databricks.
This certification focuses on building, optimizing, and managing data pipelines, working with big data solutions, and implementing scalable data processing workflows.
Learn how to design, secure, and manage Azure Databricks environments using Unity Catalog, Delta Lake, Lakeflow Jobs, SQL, Python, and Azure security services.
The DP-750T00: Implement Data Engineering Solutions using Azure Databricks course is a 4-day intermediate course for data engineers and data professionals.The course covers Azure Databricks environment setup, Unity Catalog governance, data ingestion and processing, Delta Lake pipelines, Lakeflow Jobs, and production workload deployment.
- Building data ingestion and transformation pipelines using SQL, Python, Delta Lake, and Databricks capabilities.
- Setting up and configuring Azure Databricks environments, including workspaces, compute, and foundational architecture.
- Applying data governance and security practices using Unity Catalog to manage access, compliance, and data quality.
- Developing and automating workflows using Lakeflow Jobs to support scalable and repeatable data processing.
- Deploying, monitoring, and optimising data pipelines to support production-ready workloads.
By the end of the course, learners should be able to implement, secure, and maintain scalable lakehouse solutions using Azure Databricks.
Microsoft Certified: Azure Databricks Data Engineer Associate (DP-750)
Category
Intermediate
Exam Duration
120 minutes
Product
Azure Databricks
Required Exam
Exam DP-750: Implementing Data Engineering Solutions Using Azure Databricks
Role
Data Engineer
Cost
$83 USD
Language Offered
English, Chinese, French, German, Italian, Japanese, Korean, Portuguese-Brazilian, Spanish
What are the key skills measured:
- Set up and configure an Azure Databricks environment (15–20%)
- Prepare and process data (30–35%)
- Secure and govern Unity Catalog objects (15–20%)
- Deploy and maintain data pipelines and workloads (30–35%)
Who is this for?
- ETL Developers
- AI Engineers
- Data platform professionals with experience
Position yourself to design and manage scalable data pipelines and lakehouse solutions using Azure Databricks to enable reliable, data‑driven analytics and AI outcomes.
Microsoft-commissioned IDC research found that 39% of line-of-business leaders identified data management as an important technical skill for professional success. Among IT leaders, the figure was 35%.
Microsoft / IDC, Thriving in an AI-Driven Future
Databricks and Economist Impact research found that security and governance ranked as the #1 data engineering challenge across organization sizes, cited by 50% of data engineers at medium organizations, 42% at large organizations and 57% at very large organizations.
Databricks / Economist Impact, Unlocking Enterprise AI
The World Economic Forum projects a 30% to 35% increase in demand for a group of data-related roles by 2030, including Data Engineers, Big Data Specialists, Data Analysts and Scientists, Business Intelligence Analysts, and Database and Network Professionals.
World Economic Forum, Future of Jobs Report 2025
Validate Azure Databricks data engineering skills
Demonstrate your ability to integrate and model data, build optimised pipelines and operate production workloads using Azure Databricks. Microsoft positions these capabilities at the core of the Azure Databricks engineer role.
Build and deploy optimized pipelines
Develop practical capabilities for ingesting data from diverse sources, transforming raw data into analytics-ready formats and designing schemas and partitioning strategies for analytical workloads.
Apply governance with Unity Catalog
Develop the ability to secure and govern Unity Catalog objects while applying data quality and data governance best practices.
Develop Production Data Engineering Skills
Go beyond building pipelines by learning how to deploy, monitor, troubleshoot and maintain data workloads. DP-750 also expects familiarity with SDLC practices, Git, Microsoft Entra, Azure Data Factory and Azure Monitor.
Why choose Trainocate?
Train with Trainocate, a Microsoft training partner with 30+ years of technology training expertise, extensive Microsoft learning capabilities, 30+ authorized technology partnerships and a global training footprint spanning 24 countries.
As a Microsoft Solutions Partner and 2024 Microsoft Partner of the Year Training Services Award Finalist, Trainocate helps individuals and organizations develop practical, job-ready skills through Microsoft’s globally recognized certifications, Applied Skills, and role-based learning pathways.
Ready to build your Azure Databricks skills?
Complete the form below to register your interest.
Frequently Asked Questions (FAQs)
1. What defines the Microsoft Certified: Azure Databricks Data Engineer Associate credential?
It validates expertise in integrating and modeling data, building and deploying optimized pipelines, and troubleshooting and maintaining workloads in Azure Databricks.
2. Who should pursue Microsoft Azure Databricks Data Engineer Associate?
The Microsoft Certified: Azure Databricks Data Engineer Associate credential is designed for data engineers who build, deploy, secure and maintain data engineering solutions using Azure Databricks.
Microsoft expects candidates to have expertise in integrating and modelling data, developing optimised pipelines and troubleshooting workloads. Candidates should also understand SQL, Python and software development lifecycle practices such as Git.
3. What core responsibilities and skills does the certification validate?
The Microsoft Certified: Azure Databricks Data Engineer Associate (Exam DP‑750) is designed for professionals who will:
- – Set up and configure Azure Databricks environments, including workspaces, compute resources, and overall platform configuration.
- – Ingest, prepare, and transform data using SQL and Python to build scalable data pipelines for analytics and AI workloads.
- – Apply data governance, security, and quality practices using Unity Catalog to manage access control and ensure compliant data usage.
- – Deploy, monitor, and maintain data pipelines and workloads to ensure performance, reliability, and production readiness.
These competencies are critical as organisations increasingly rely on scalable lakehouse architectures and data pipelines to support analytics and AI initiatives. The certification emphasises end‑to‑end data engineering, from ingestion to governance and deployment, to ensure reliable and secure data operations.
Qualified candidates are expected to demonstrate proficiency in Azure Databricks, SQL, Python, data pipeline development, and data governance practices to build and maintain enterprise‑grade data engineering solutions.
4. What job roles is DP-750 relevant to?
DP-750 is primarily aligned with the Data Engineer role, particularly professionals working with Azure Databricks, lakehouse architectures and cloud data pipelines.
Microsoft also notes that these professionals work closely with platform architects, solution architects, data scientists, data analysts and administrators.
The certification can therefore be particularly relevant to:
Data Engineer → Azure Data Engineer → Databricks Data Engineer → Senior Data Engineer / Data Platform Engineer
5. 4. Does DP-750 cover data governance and Unity Catalog?
Yes. Data governance is a core part of DP-750. Microsoft specifically expects candidates to apply data quality and data governance best practices using Unity Catalog.
This is important for modern data engineering because Unity Catalog provides governance across data and AI assets, including access control, lineage, auditing, data classification, quality monitoring and AI governance.
