Implement data engineering solutions using Azure Databricks (DP-750T00)
Training plan
Module 1: Set up and configure an Azure Databricks environment
- Explore Azure Databricks
- Understand Azure Databricks architecture
- Understand Azure Databricks Integrations
- Select and Configure Compute in Azure Databricks
- Create and organize objects in Unity Catalog
Module 2: Secure and govern Unity Catalog objects in Azure Databricks
- Secure Unity Catalog objects
- Govern Unity Catalog objects
Module 3: Prepare and process data with Azure Databricks
- Design and implement data modeling with Azure Databricks
- Ingest data into Unity Catalog
- Cleanse, transform, and load data into Unity Catalog
- Implement and manage data quality constraints with Azure Databricks
Module 4: Deploy and maintain data pipelines and workloads with Azure Databricks
- Design and implement data pipelines with Azure Databricks
- Implement Lakeflow Jobs with Azure Databricks
- Implement development lifecycle processes in Azure Databricks
- Monitor, troubleshoot and optimize workloads in Azure Databricks
Recommended prerequisite knowledge
- Proficiency in SQL (queries, joins, aggregations) and understanding of data transformation principles
- Experience with Python for data engineering (notebooks, scripts, data manipulation)
- Basic understanding of analytics and data engineering concepts (data ingestion, processing, data quality)
- Understanding of data warehouses and/or lakehouse architectures (access models, data layers)
- Basic knowledge of cloud and cloud storage
- Familiarity with Azure Databricks (workspaces, notebooks) and ideally with Unity Catalog (governance, permissions)
- Fundamental knowledge of Azure security, particularly Microsoft Entra ID
- Familiarity with SDLC practices and Git version control (branches, commits, pull requests)
Credentials and certification
Exam features
- Code: DP-750
- Title: Implementing Data Engineering Solutions Using Azure Databricks
- Duration: 100 minutes
- Number of Questions: 40 to 60
- Question Format: Multiple choice, multiple response, scenario-based
- Passing Score: 700 out of 1000
- Cost: $0 (included in your training)
Exam topics
- Implement and configure an Azure Databricks environment for data engineering
- Set up governance and security with Unity Catalog
- Design and deploy scalable lakehouse solutions that meet organizational requirements
- Create robust and industrial-grade ingestion pipelines
- Prepare, transform, and process data with SQL and Python
- Deploy and maintain pipelines and workloads in production
- Monitor, diagnose, and resolve workload issues
- Optimize workload performance and efficiency
Azure Databricks Data Engineer training
The Azure Databricks Data Engineer training program is designed for professionals who want to develop expertise in data engineering with Azure Databricks and Unity Catalog. This course prepares you for the DP-750 exam and the Microsoft Certified: Azure Databricks Data Engineer Associate certification, which validates your skills in integrating and modeling data, designing and deploying optimized pipelines, and securing, governing, and maintaining production workloads on Azure Databricks.
Why Take This Training?
The DP-750 certification allows you to demonstrate your ability to design, deploy, and operate data engineering solutions with Azure Databricks. You will learn to ingest, transform, and structure data using SQL and Python, and then implement enterprise-grade governance and security with Unity Catalog.
By completing this training, you will be able to build more reliable pipelines, deploy optimized workloads, and maintain scalable lakehouse solutions to maximize data value and support faster, more informed decision-making.
Skills Developed During the Training
- Data Engineering Solution Design and Implementation
Learn how to design lakehouse solutions and implement end-to-end pipelines with Azure Databricks. - Data Ingestion and Integration
Master the creation of robust ingestion pipelines to integrate diverse data sources and feed reliable workloads. - Data Preparation and Transformation (SQL & Python)
Develop your skills to clean, enrich, and transform data using SQL and Python in Databricks notebooks. - Governance and Security with Unity Catalog
Implement governance, access control, and data security practices with Unity Catalog, tailored to enterprise requirements. - Performance Monitoring, Maintenance, and Optimization
Learn how to deploy, monitor, diagnose, and optimize pipelines and workloads to improve performance, reliability, and operational efficiency.
Instructor-led DP-750 training for comprehensive learning
Our instructor-led, Microsoft Certified: Azure Databricks Data Engineer Associate (DP-750) course offers interactive, live sessions with Microsoft Certified trainers. Through hands-on exercises and real-world scenarios, you’ll learn to design, implement, and optimize data engineering solutions with Azure Databricks and Unity Catalog (governance, security, pipelines, and production deployment).
Upon completion of this course, you’ll be ready to tackle the challenges of modern data engineering and pass the DP-750 certification exam.
Who is this training for?
- Data engineers looking to develop or deepen their skills in Azure Databricks (pipelines, workloads, operations)
- Data professionals involved in modernization projects (lakehouse, governance, industrialization of processes)
- Data consultants and specialists seeking a recognized certification in Azure Databricks and governance with Unity Catalog
- Anyone preparing for the DP-750 certification exam and wanting structured and practical preparation
Maximize your data engineering expertise
The Microsoft Certified: Azure Databricks Data Engineer Associate certification allows you to demonstrate your ability to design, secure, and optimize data engineering solutions with Azure Databricks and Unity Catalog. Enroll today to enhance your skills, deploy more reliable pipelines, and accelerate your career advancement.
DP-750 Exam Success Strategies
Passing the DP-750 exam requires more than just technical knowledge: structured preparation, effective time management, and a good grasp of “production” scenarios are equally essential to maximizing your performance on exam day.
DP-750 Exam Statistics & Success Rates
- Average Pass Rate: 65-70% on first attempt (Microsoft Associate level average)
- Most Common Score Range: 720-780 for passing candidates
- Average Study Time: 6-8 weeks for experienced data engineers
- Retake Rate: 25-30% of candidates require a second attempt
- Top Failure Areas: Governance and security with Unity Catalog + access models (35-40%), deployment, maintenance, and reliability of production pipelines/workloads (30-35%), workload performance optimization (25-30%)
Study Method Comparison
| Study Approach | Duration | Pass rate | Best for |
|---|---|---|---|
|
Hands-on Practice Only |
4-6 weeks |
45-60% |
Experienced data engineers |
|
Documentation + Practice |
6-8 weeks |
70-80% |
Methodical learners |
|
Training + Labs + Practice |
6-8 weeks |
85-90% |
Comprehensive preparation |
|
Practice Tests Only |
2-3 weeks |
35-50% |
Not recommended |
Strategic Study Approach
- Create a 6-8 week study schedule – Don’t cram at the last minute for this associate-level certification.
- Follow the 70-20-10 rule – 70% hands-on practice with Azure Databricks (notebooks, pipelines, workloads) and Unity Catalog, 20% reading documentation, and 10% practice with questions/exams.
- Focus on scenario-based learning – DP-750 emphasizes “production” situations (ingestion, governance, security, deployment, maintenance) rather than memorization.
- Study in concentrated 90-minute blocks with 15-minute breaks to maximize retention
Common Exam Pitfalls to Avoid
- Don’t confuse Azure Data Factory with Databricks orchestration mechanisms – know when to use ADF to orchestrate/schedule flows and when to leverage Databricks’ native capabilities to execute and industrialize processes.
- Governance vs. “classic” permissions – master what Unity Catalog offers (catalogs, schemas, tables, access controls, best practices) and avoid thinking solely in terms of “workspace” rights.
- Lakehouse: layers and access models – understand how to structure a lakehouse solution (data layers, access, sharing) and when to favor a lakehouse-oriented approach over a traditional data warehouse.
- Batch vs. streaming ingestion – distinguish scenarios, constraints, and implementation choices based on data velocity (scheduled loads vs. continuous flows).
- Workload optimization and performance – don’t neglect optimization levers (process design, data organization, compute settings): these are common points in production scenarios.
- Monitoring, diagnostics, and reliability – don’t underestimate the “run” aspect: knowing how to monitor, troubleshoot, and maintain production pipelines/workloads is central to DP-750.
- Version control and SDLC practices – don’t neglect Git and good lifecycle practices (collaboration, traceability, deployments): the exam assumes a culture of industrialization.
Topic Weight Distribution
| Exam Domain | Weight | Focus Areas | Priority |
|---|---|---|---|
|
Setting up and governing the Azure Databricks environment |
25-30% |
Workspace/compute configuration, access models, Unity Catalog (catalogs/schemas/tables), enterprise governance |
Critical |
|
Ingest, prepare and transform data |
35-40% |
Data ingestion pipelines, SQL/Python transformations, data quality, structuring for analytics |
Critical |
|
Deploy, monitor, and optimize pipelines and workloads |
30-35% |
Production deployment, maintenance, monitoring/diagnostics, performance & reliability optimization |
Critical |
Exam Day Time Management
- Allocate 90 seconds per question on average – this gives buffer time for complex scenarios
- Read case studies completely first before attempting related questions
- Flag uncertain questions and return to them – don’t get stuck on difficult items
- Reserve 15 minutes at the end for reviewing flagged questions and checking answers
Managing Exam Stress & Performance
- Get 7-8 hours of quality sleep the night before – avoid last-minute cramming
- Arrive 30 minutes early to settle in and complete check-in procedures calmly
- Use deep breathing techniques if you feel overwhelmed during the exam
- Trust your preparation – your first instinct is usually correct on scenario questions
Technical Preparation Tips
- Practice directly in Azure Databricks (workspaces, clusters, notebooks) – learn to develop, run, and debug SQL/Python processes
- Master lakehouse concepts and data structuring best practices – understand how to organize data for ingestion, processing, and analytics at scale
- Work on end-to-end ingestion and transformation – create robust pipelines, validate data quality, and reliably handle errors
- Understand orchestration and industrialization – learn to plan, monitor, and troubleshoot workflows (including integration with tools like Azure Data Factory when relevant)
- Review security and governance – master Unity Catalog (permissions, access models, best practices) and the fundamentals of Microsoft Entra ID
- Practice monitoring and diagnostics – Familiarize yourself with monitoring signals, failure analysis, and production workload optimization.
Final Week Preparation
- Take 2-3 practice exams to identify knowledge gaps and build confidence
- Review Microsoft’s official exam objectives one final time
- Avoid learning new concepts – focus on reinforcing what you already know
- Prepare your exam day logistics – route to test center, required identification, arrival time
Mental Preparation Strategies
- Visualize success scenarios – imagine yourself confidently answering questions
- Remind yourself of your hands-on experience – you’ve likely already implemented pipelines, transformations, and operating practices similar to the scenarios being assessed.
- Stay positive during difficult questions – every candidate faces challenging scenarios
- Remember that 700/1000 passes – you don’t need perfection, just solid competency
How to Schedule Your DP-750 Exam
- Official Testing Provider: Pearson VUE is Microsoft’s authorized testing partner for DP-750
- Scheduling Process: Create a Pearson VUE account, search for “DP-750”, select your preferred test center and date
- Exam Cost: $165 USD (prices may vary by region and currency) – not included with the training
- Scheduling Timeline: Book at least 2-3 weeks in advance for better time slot availability
- Rescheduling Policy: Free rescheduling up to 24 hours before your exam appointment
- Required ID: Government-issued photo ID (passport, driver’s license) matching your registration name exactly
Success mindset: Approach DP-750 as a validation of your actual data engineering skills on Azure Databricks, rather than as a test of facts to memorize. Your hands-on experience with data pipelines, lakehouse approaches, governance (especially with Unity Catalog), and running workloads in production is your greatest asset.
Frequently Asked Questions about Microsoft Certified: Azure Databricks Data Engineer Associate (DP-750) training (FAQ)
You will work with Azure Databricks (workspaces, compute/clusters, notebooks) to perform data engineering tasks using SQL and Python. The training also emphasizes Unity Catalog for governance and security (access models, permissions, best practices). Depending on the scenarios, integration with common Azure services (e.g., Microsoft Entra ID, Azure Data Factory, Azure Monitor) will also be covered to position Databricks within a production data pipeline.
A solid foundation in SQL is recommended, as well as experience with Python (ideally through notebooks). Knowledge of data engineering concepts (ingestion, transformation, quality), cloud computing, and familiarity with Git version control are helpful. Prior exposure to Azure Databricks and/or data governance is a plus, but the training will introduce these concepts progressively.
Yes. The training is field-oriented with practical exercises and scenarios close to reality: data ingestion and transformation, implementation of governance with Unity Catalog, deployment and maintenance of workloads, as well as monitoring/diagnostic activities.
DP-750 helps your organization industrialize data engineering on Azure Databricks: more reliable pipelines, better governance and security with Unity Catalog, more consistent deployment and operating practices, and a better ability to deliver quality datasets for analytics and decision-making.
It helps you, in particular, to structure a lakehouse approach at scale and avoid “fragile” pipelines, implement clear governance (access, permissions, best practices) with Unity Catalog, deploy workloads to production and improve maintainability, and monitor, diagnose, and optimize processes to gain performance and reliability.
Upon completion of the training, you will be able to configure an Azure Databricks environment for data workloads, build ingestion and transformation pipelines with SQL/Python, apply security and governance via Unity Catalog, deploy, maintain, monitor and optimize pipelines/workloads in production and work with SDLC practices (including Git) adapted to data engineering.