Microsoft Certified: Azure Databricks Data Engineer Associate (DP-750) Practice Exam
Microsoft Certified: Azure Databricks Data Engineer Associate (DP-750) Practice Exam
About Microsoft Certified: Azure Databricks Data Engineer Associate (DP-750) Exam
The Microsoft Certified: Azure Databricks Data Engineer Associate (DP-750) certification is designed for professionals who want to validate their expertise in data integration, transformation, pipeline optimization, and workload maintenance using Azure Databricks. This certification focuses on building scalable data engineering solutions while applying data quality, governance, and Unity Catalog best practices. It is ideal for data engineers working with SQL, Python, Delta Lake, Unity Catalog, and enterprise-grade Azure data pipelines.
Skills Validated
This certification validates your ability to:
- Set up and configure Azure Databricks environments
- Secure and govern Unity Catalog objects
- Prepare, ingest, and transform data using SQL and Python
- Build and deploy optimized data pipelines
- Monitor, troubleshoot, and maintain workloads
- Integrate Databricks with Microsoft Entra, Azure Data Factory, and Azure Monitor
The DP-750 certification is highly valuable for professionals building lakehouse architectures, medallion pipelines, and enterprise analytics platforms.
Knowledge Gained
By preparing for the DP-750 certification, you will gain expertise in:
- Configuring Azure Databricks workspaces and compute
- Governing data with Unity Catalog
- Building SQL and Python-based ETL pipelines
- Implementing Delta Lake and medallion architecture
- Optimizing data processing workloads
- Monitoring performance with Azure Monitor
- Managing Git-based SDLC and deployment workflows
- Securing data engineering environments with Microsoft Entra
Skills Required
To succeed in DP-750, candidates should be comfortable with:
- SQL and Python
- Azure Databricks notebooks and workflows
- Delta Lake concepts
- Unity Catalog governance
- Azure Data Factory
- Azure Monitor
- Microsoft Entra fundamentals
- Git and SDLC workflows
- Data modeling and transformation best practices
Recommended Prerequisites
Before attempting the DP-750 certification, candidates should ideally have:
- Hands-on experience with Azure Databricks
- Working knowledge of SQL and Python for data engineering
- Familiarity with Unity Catalog and data governance
- Understanding of data lake and lakehouse concepts
- Basic Azure security and identity knowledge
- Experience with Git-based version control
- Exposure to Azure monitoring and orchestration tools
While there is no mandatory prerequisite certification, practical experience in Databricks-based data pipelines will significantly improve exam readiness.
Who should take the DP-750 Exam?
This certification is best suited for:
- Data Engineers
- Azure Data Platform Engineers
- Databricks Developers
- ETL / ELT Engineers
- Analytics Engineers
- Lakehouse Architects
- Professionals working on Delta Lake and medallion pipelines
It is ideal for professionals looking to validate expertise in Azure Databricks-based enterprise data engineering.
Career Opportunities
This certification supports roles such as:
- Azure Databricks Data Engineer
- Azure Data Engineer
- Lakehouse Engineer
- Data Pipeline Engineer
- Analytics Platform Engineer
- Big Data Engineer
- Delta Lake Specialist
Course Outline
The Microsoft Certified: Azure Databricks Data Engineer Associate (DP-750) Exam covers the following topics -
Domain 1 - Set up and configure an Azure Databricks environment (15–20%)
1.1 Select and configure compute in a workspace
- Choose an appropriate compute type, including job compute, serverless, warehouse, classic compute, and shared compute
- Configure compute performance settings, including CPU, node count, autoscaling, termination, node type, cluster size, and pooling
- Configure compute feature settings, including Photon acceleration, Azure Databricks runtime/Spark version, and machine learning
- Install libraries for a compute resource
- Configure access permissions to a compute resource
1.2 Create and organize objects in Unity Catalog
- Apply naming conventions based on requirements, including isolation, development environment, and external sharing
- Create a catalog based on requirements
- Create a schema based on requirements
- Create volumes based on requirements
- Create tables, views, and materialized views
- Implement a foreign catalog by configuring connections
- Implement data definition language (DDL) operations on managed and external tables
- Configure AI/BI Genie instructions for data discovery
Domain 2 - Secure and govern Unity Catalog objects (15–20%)
2.1 Secure Unity Catalog objects
- Grant privileges to a principal (user, service principal, or group) for securable objects in Unity Catalog
- Implement table- and column-level access control and row-level security
- Access Azure Key Vault secrets from within Azure Databricks
- Authenticate data access by using service principals
- Authenticate resource access by using managed identities
2.2 Govern Unity Catalog objects
- Create, implement, and preserve table and column definitions and descriptions for data discovery
- Configure attribute-based access control (ABAC) by using tags and policies
- Configure row filters and column masks
- Apply data retention policies
- Set up and manage data lineage tracking by using Catalog Explorer, including owner, history, dependencies, and lineage
- Configure audit logging
- Design and implement a secure strategy for Delta Sharing
Domain 3 - Prepare and process data (30–35%)
3.1 Design and implement data modeling in Unity Catalog
- Design logic for data ingestion and data source configuration, including extraction type and file type
- Choose an appropriate data ingestion tool, including Lakeflow Connect, notebooks, and Azure Data Factory
- Choose a data loading method, including batch and streaming
- Choose a data table format, such as Parquet, Delta, CSV, JSON, or Iceberg
- Design and implement a data partitioning scheme
- Choose a slowly changing dimension (SCD) type
- Choose granularity on a column or table based on requirements
- Design and implement a temporal (history) table to record changes over time
- Design and implement a clustering strategy, including liquid clustering, Z-ordering, and deletion vectors
- Choose between managed and unmanaged tables
3.2 Ingest data into Unity Catalog
- Ingest data by using Lakeflow Connect, including batch and streaming
- Ingest data by using notebooks, including batch and streaming
- Ingest data by using SQL methods, including CREATE TABLE … AS (CTAS), CREATE OR REPLACE TABLE, and COPY INTO
- Ingest data by using a change data capture (CDC) feed
- Ingest data by using Spark Structured Streaming
- Ingest streaming data from Azure Event Hubs
- Ingest data by using Lakeflow Spark Declarative Pipelines, including Auto Loader
3.3 Cleanse, transform, and load data into Unity Catalog
- Profile data to generate summary statistics and assess data distributions
- Choose appropriate column data types
- Identify and resolve duplicate, missing, and null values
- Transform data, including filtering, grouping, and aggregating data
- Transform data by using join, union, intersect, and except operators
- Transform data by denormalizing, pivoting, and unpivoting data
- Load data by using merge, insert, and append operations
3.4 Implement and manage data quality constraints in Unity Catalog
- Implement validation checks, including nullability, data cardinality, and range checking
- Implement data type checks
- Implement schema enforcement and manage schema drift
- Manage data quality with pipeline expectations in Lakeflow Spark Declarative Pipelines
Domain 4 - Deploy and maintain data pipelines and workloads (30–35%)
4.1 Design and implement data pipelines
- Design order of operations for a data pipeline
- Choose between notebook and Lakeflow Spark Declarative Pipelines
- Design task logic for Lakeflow Jobs
- Design and implement error handling in data pipelines, notebooks, and jobs
- Create a data pipeline by using a notebook, including precedence constraints
- Create a data pipeline by using Lakeflow Spark Declarative Pipelines
4.2 Implement Lakeflow Jobs
- Create a job, including setup and configuration
- Configure job triggers
- Schedule a job
- Configure alerts for a job
- Configure automatic restarts for a job or a data pipeline
4.3 Implement development lifecycle processes in Azure Databricks
- Apply version control best practices using Git
- Manage branching, pull requests, and conflict resolution
- Implement a testing strategy, including unit tests, integration tests, end-to-end tests, and user acceptance testing (UAT)
- Configure and package Databricks Asset Bundles
- Deploy a bundle by using the Azure Databricks command-line interface (CLI)
- Deploy a bundle by using REST APIs
4.4 Monitor, troubleshoot, and optimize workloads in Azure Databricks
- Monitor and manage cluster consumption to optimize performance and cost
- Troubleshoot and repair issues in Lakeflow Jobs, including repair, restart, stop, and run functions
- Troubleshoot and repair issues in Apache Spark jobs and notebooks, including performance tuning, resolving resource bottlenecks, and cluster restart
- Investigate and resolve caching, skewing, spilling, and shuffle issues by using a Directed Acyclic Graph (DAG), the Spark UI, and query profile
- Optimize Delta tables for performance and cost, including OPTIMIZE and VACUUM commands
- Implement log streaming by using Log Analytics in Azure Monitor
- Configure alerts by using Azure Monitor
