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Data Engineering on Microsoft Azure (DP-203T00) Certification Training
DP-203 Certification Training
The instructor-led DP-203T00: Data Engineering on Microsoft Azure training in the USA gives professionals the necessary abilities to build, design and maintain data solutions on Microsoft Azure. The course teaches students about essential data storage along with processing and security and integr
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Data Engineering on Microsoft Azure (DP-203T00) Certification Training
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DP-203T00: Data Engineering on Microsoft Azure Course Overview
The DP-203T00: Data Engineering on Microsoft Azure training in the USA provides professionals the essential skills to build and deploy data solutions through Microsoft Azure cloud infrastructure. The training provides students with knowledge about controlling data processing flows while optimizing storage management and securing data throughout Azure systems.
The program provides practical training in using Azure Synapse Analytics together with Azure Data Lake and Azure Data Factory and real-time data streaming solutions. The program teaches students methods to bring in data while teaching them how to transform it and orchestrate it through batch and real-time processing operations. The core curriculum includes lessons about best practices in the industry and compliance requirements and methods to optimize cloud-based data solution performance.
The training will show learners how to construct resilient data architectures by using multiple data sources. The training system focuses on three main aspects which include Azure Apache Spark optimization and encryption solution development and Azure Data Factory workflow automation.
The certification preparation section within the program equips data engineers and IT professionals along with analysts with necessary Azure data solution expertise for validation purposes. This training serves as an optimal choice for data engineers who want to develop their career in cloud-based data engineering through enterprise-level data platform development.
This course provides students with full knowledge of Microsoft Azure data ecosystem capabilities to design enterprise-level business data solutions that perform at high speeds while maintaining security and scalability.
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Course Objectives
- Understand the fundamentals of Microsoft Azure data engineering, including storage, processing, and security.
- Develop expertise in building and managing data pipelines using Azure Data Factory, Azure Synapse Analytics, and Azure Data Lake.
- Work with Apache Spark and Azure Stream Analytics to handle both batch and real-time data processing.
- Apply data transformation techniques for structured and unstructured data across various Azure platforms.
- Optimize data storage, retrieval, and performance with Azure SQL, Cosmos DB, and other storage solutions.
- Implement encryption, access control, and monitoring strategies to ensure data security, compliance, and governance.
- Automate data workflows with Azure Logic Apps, Data Factory pipelines, and Power BI for seamless analytics integration.
- Design scalable, cloud-based architectures by integrating data solutions with enterprise applications.
- Gain hands-on experience through labs, industry-relevant projects, and real-world case studies.
- Prepare for the DP-203 certification by mastering the design and deployment of Azure data solutions.
Audience
- Solution Architects
- Technical Consultants
- Data Analysts
- IT Professionals
- Database Administrators
- Cloud Engineers
- Data Engineers
- Software Developers
- Business Intelligence Professionals
- Machine Learning Engineers
Prerequisite
Recommended:
- DP-900T00: Microsoft Azure Data Fundamentals
- AI+ Executive™
- AZ-900T00: Microsoft Azure Fundamentals
- AI+ Prompt Engineer™: Level 1
Course Outline
Introduction to Data Engineering on Azure
- Understand key data engineering concepts and their role in modern data solutions
- Identify common data engineering tasks and best practices
- Explore Azure services designed for data engineering
Azure Data Lake Storage Gen2 Overview
- Learn the features, benefits, and use cases of Azure Data Lake Storage Gen2
- Configure and enable Azure Data Lake Storage Gen2 in an Azure Storage account
- Compare Azure Data Lake Storage Gen2 with Azure Blob Storage
- Understand its role in analytical processing and data workflows
- Explore real-world applications of Azure Data Lake Storage Gen2 in analytical workloads
Overview of Azure Synapse Analytics
- Identify business challenges addressed by Azure Synapse Analytics
- Explore its core features and capabilities for data integration and analytics
- Determine when to implement Azure Synapse Analytics in data solutions
Building Data Analytics Solutions Using Azure Synapse Serverless SQL Pools
- Querying Data in a Data Lake with Serverless SQL Pools
- Understand the capabilities and use cases of serverless SQL pools in Azure Synapse Analytics
- Query data stored in CSV, JSON, and Parquet files using serverless SQL pools
- Create external database objects for querying data in a serverless SQL pool
Transforming Data in a Data Lake with Serverless SQL Pools
- Utilize the CREATE EXTERNAL TABLE AS SELECT (CETAS) statement for data transformation
- Encapsulate CETAS statements within stored procedures for automated processing
- Integrate stored procedures into data pipelines for streamlined transformations
Creating and Managing a Lake Database in Azure Synapse Analytics
- Understand the key concepts and components of lake databases
- Explore database templates in Azure Synapse Analytics
- Create and configure a lake database for data management
Securing Data and Managing Users in Serverless SQL Pools
- Select appropriate authentication methods for Azure Synapse serverless SQL pools
- Manage user access and permissions for secure data operations
- Implement best practices for security and compliance in serverless SQL environments
- Performing Data Engineering with Azure Synapse Apache Spark Pools
Data Analysis with Apache Spark in Azure Synapse Analytics
- Understand core features and capabilities of Apache Spark
- Configure and manage a Spark pool in Azure Synapse Analytics
- Load, analyze, and visualize data using Spark notebooks
Data Transformation with Spark in Azure Synapse Analytics
- Modify and save DataFrames using Apache Spark
- Optimize performance by partitioning data files
- Transform data using SQL within Spark environments
Using Delta Lake in Azure Synapse Analytics
- Explore the key features and benefits of Delta Lake
- Create and manage Delta Lake tables in a Synapse Analytics Spark pool
- Register Delta Lake tables in the Spark catalog for streamlined access
- Enable real-time processing with Delta Lake tables for streaming data
- Query Delta Lake tables from an Azure Synapse Analytics SQL pool
Transferring and Transforming Data with Azure Synapse Analytics Pipelines
- Building Data Pipelines in Azure Synapse Analytics
- Understand core concepts of Azure Synapse Analytics pipelines
- Create and configure a pipeline using Azure Synapse Studio
- Implement data flow activities within pipelines
- Execute and monitor pipeline runs for data processing
Integrating Spark Notebooks with Azure Synapse Pipelines
- Explore notebook and pipeline integration concepts
- Implement Synapse notebook activities within a pipeline
- Use parameters to customize and enhance notebook activities
Implementing a Data Analytics Solution with Azure Synapse Analytics
- Introduction to Azure Synapse Analytics
- Identify key business problems addressed by Azure Synapse Analytics
- Explore core capabilities and features of Azure Synapse Analytics
- Determine the appropriate scenarios for using Azure Synapse Analytics
Querying Data Lake Files Using Azure Synapse Serverless SQL Pool
- Understand the capabilities and use cases of serverless SQL pools
- Query structured and semi-structured files (CSV, JSON, Parquet) using SQL
- Create external database objects for optimized query execution
Analyzing Data with Apache Spark in Azure Synapse Analytics
- Learn key features and functionalities of Apache Spark
- Configure and manage Spark pools within Azure Synapse Analytics
- Execute Spark notebooks to load, analyze, and visualize data
Leveraging Delta Lake in Azure Synapse Analytics
- Explore the core concepts and benefits of Delta Lake
- Create and manage Delta Lake tables within a Synapse Analytics Spark pool
- Register Spark catalog tables for efficient data access
- Utilize Delta Lake for real-time streaming data processing
- Query Delta Lake tables using Synapse Analytics SQL pools
Analyzing Data in a Relational Data Warehouse
- Design schema structures for relational data warehouses
- Develop fact, dimension, and staging tables for optimized storage
- Load data into relational warehouse tables using SQL
- Perform SQL-based queries to extract insights from relational data
Building Data Pipelines in Azure Synapse Analytics
- Understand essential concepts of Azure Synapse Analytics pipelines
- Design and implement pipelines using Azure Synapse Studio
- Incorporate data flow activities for transformation processes
- Execute, monitor, and optimize pipeline runs for data movement and transformation
Working with Data Warehouses Using Azure Synapse Analytics
- Analyzing Data in a Relational Data Warehouse
- Design schemas optimized for data warehousing
- Create and manage fact, dimension, and staging tables
- Load data into warehouse tables using SQL
- Execute SQL queries for data analysis
Loading Data into a Relational Data Warehouse
- Populate staging tables before transformation
- Load data into dimension tables
- Manage time dimensions effectively
- Implement Slowly Changing Dimensions (SCD) strategies
- Load fact tables with structured data
- Perform post-load performance optimizations
Managing and Monitoring Data Warehouse Activities
- Scale compute resources dynamically
- Pause and resume compute operations
- Manage workloads for optimized performance
- Use Azure Advisor for performance recommendations
- Leverage Dynamic Management Views (DMVs) for troubleshooting
Securing a Data Warehouse in Azure Synapse Analytics
- Implement network security configurations
- Set up Conditional Access policies
- Configure authentication mechanisms
- Apply column- and row-level security
- Use Dynamic Data Masking for sensitive information
- Implement encryption for enhanced security
Hybrid Transactional and Analytical Processing (HTAP) with Azure Synapse Analytics Designing Hybrid Transactional and Analytical Workflows
- Explore methodologies for integrating transactional and analytical processing
- Identify and utilize Azure Synapse Link services for hybrid data solutions
Configuring Azure Synapse Link for Azure Cosmos DB
- Set up Azure Synapse Link within a Cosmos DB account
- Enable analytical storage for seamless data processing
- Establish a linked service for Cosmos DB integration
- Perform data analysis using Apache Spark
- Query integrated datasets using Synapse SQL
Enabling Azure Synapse Link for SQL-Based Solutions
- Understand core features and benefits of Synapse Link for SQL environments
- Configure Synapse Link for Azure SQL Database connectivity
- Set up and integrate Synapse Link with Microsoft SQL Server
Developing a Real-Time Data Streaming Solution with Azure Stream Analytics
- Introduction to Azure Stream Analytics
- Gain insights into data streaming concepts
- Explore event-driven data processing mechanisms
- Understand the role of windowing functions in stream processing
- Set up and configure Azure Stream Analytics
Capturing and Processing Streaming Data in Azure Synapse Analytics
- Examine key use cases for real-time data ingestion in Azure Synapse
- Define and configure input and output sources for stream analytics jobs
- Write queries to capture and process continuous data streams in Synapse Analytics
- Execute streaming jobs and utilize real-time data in Azure Synapse
Building Real-Time Data Visualizations with Power BI
- Establish Power BI as an output destination for Stream Analytics
- Develop Stream Analytics queries to feed real-time data into Power BI
- Design interactive, live dashboards for visualizing streaming data
Enterprise Data Governance with Microsoft Purview
- Understanding Microsoft Purview for Data Management
- Assess whether Microsoft Purview aligns with your data governance and discovery needs
- Explore key functionalities of Microsoft Purview in managing enterprise data
Ensuring Data Reliability with Microsoft Purview
- Navigate, search, and oversee assets in the data catalog
- Integrate data catalog assets with Power BI for enhanced insights
- Utilize Microsoft Purview within Azure Synapse Studio for seamless data management
Organizing Data Assets with Microsoft Purview
- Understand asset classification and organization in Microsoft Purview
Managing Power BI Resources with Microsoft Purview
- Register and scan Power BI tenants for governance
- Utilize search and navigation features to locate data assets
- Explore schema structures and track data lineage for Power BI datasets
Integrating Microsoft Purview with Azure Synapse Analytics
- Register Azure Synapse Analytics database assets in Microsoft Purview
- Enable and configure integration between Microsoft Purview and Azure Synapse
- Perform searches within the Purview catalog directly from Synapse Studio
- Monitor and track data flow across Azure Synapse Analytics pipeline activities
About The Certification
This certification is designed for professionals skilled in developing data analytics solutions by integrating and transforming data from multiple sources. It is ideal for data engineers, business intelligence specialists, and data architects, validating their ability to merge, structure, and process structured, unstructured, and streaming data for analytical use.
As an Azure Data Engineer, you play a key role in supporting stakeholders with data exploration while designing and maintaining secure, compliant, and efficient data pipelines. Using various Azure data services, you manage storage, cleansing, and transformation processes to ensure datasets are optimized for analysis. Based on business needs, data architectures may include:
- Modern Data Warehouse (MDW)
- Big Data Solutions
- Lakehouse Architecture
Beyond data processing, your focus is on ensuring that pipelines and storage solutions are scalable, reliable, and high-performing. Core responsibilities include troubleshooting operational issues, improving data quality, and optimizing system performance to meet enterprise-level requirements.
Required Skills and Knowledge:
Candidates should have a strong command of data processing languages, including:
- SQL
- Python
- Scala
Additionally, a deep understanding of parallel processing and data architecture patterns is essential. This certification confirms proficiency in designing and implementing solutions using:
- Azure Data Factory
- Azure Synapse Analytics
- Azure Stream Analytics
- Azure Event Hubs
- Azure Data Lake Storage
- Azure Databricks
Skills Assessed:
- Design and implement data storage (15–20%)
- Secure, monitor, and optimize data storage and processing (30–35%
- Develop data processing solutions (40–45%)
Exam Details:
- Passing Score: 700 or higher
- Duration: 120 minutes
Certification Validity & Renewal
Previously, Microsoft role-based and specialty certifications had a validity of two years. However, since June 2021:
- Certifications are valid for one year.
- Renewal is free and can be completed online via Microsoft Learn.
- The renewal window opens six months before expiration.
- Certifications earned before June 2021 remain valid for two years and can be renewed under the updated policy.
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FAQ’s
What does the Microsoft Certified: Azure Data Engineer Associate certification validate?
This certification confirms expertise in building, managing, and optimizing data solutions on Microsoft Azure. It is designed for professionals who work with structured, unstructured, and streaming data, ensuring secure and scalable data pipelines for analytics and business intelligence.
Are there any recommended skills before taking this certification?
There are no strict prerequisites, but candidates should have a strong grasp of data processing languages such as SQL, Python, or Scala. Familiarity with Azure-based data storage, integration, and analytics services—like Azure Synapse Analytics and Data Factory—is highly beneficial.
Which exam must be passed to earn this certification?
To obtain the certification, candidates must pass the DP-203: Data Engineering on Microsoft Azure exam, which assesses skills in data transformation, integration, security, and performance tuning within Azure’s data environment.
What is the duration of the DP-203 exam?
The exam lasts 120 minutes and includes multiple-choice questions, scenario-based case studies, and hands-on problem-solving. It tests both theoretical understanding and practical proficiency in Azure data engineering.
What are the key topics covered in this certification?
The certification emphasizes:
- Designing and implementing data storage architectures
- Managing data pipelines using Azure Data Factory and Databricks
- Ensuring security, governance, and performance optimization in data workflows
- Handling structured, unstructured, and real-time streaming data
Who should consider this certification?
This certification is ideal for data engineers, BI specialists, data architects, and analytics professionals aiming to design and maintain large-scale, cloud-based data solutions using Azure.
Can I refer to notes or external materials during the DP-203 exam?
No, the exam is proctored and closed book. Candidates must rely on their knowledge and hands-on experience to solve data engineering challenges within Azure.
What score is required to pass the DP-203 exam?
To pass, candidates must achieve a minimum score of 700 out of 1000. Performance is assessed based on accuracy, efficiency, and the ability to develop well-structured data solutions.
In which languages is the DP-203 exam offered?
The exam is available in English, Japanese, Korean, Chinese (Simplified), French, German, and Spanish. Candidates should verify language availability before scheduling their exam.
Will this course fully prepare me for the DP-203 exam?
Yes, this course is designed to align with DP-203 exam objectives, featuring hands-on projects, real-world case studies, and interactive exercises. Additionally, using practice tests and gaining direct experience with Azure’s data tools will further enhance exam readiness.
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Reviews
Highly Practical and Industry-Relevant! This course provides in-depth knowledge of Azure data engineering, covering key areas such as data integration, transformation, and security. The hands-on labs and real-world projects help professionals apply concepts effectively, making it a great learning experience. The structured learning path ensures a deep understanding of Azure’s data ecosystem, making it easy to implement real-world solutions. The training also covers best practices for scalability and compliance, preparing learners for enterprise-level challenges.
Nishita SamalProject Manager
Perfect for Career Growth! Whether you're a data engineer, analyst, or architect, this course equips you with practical skills for handling large-scale data solutions. The focus on performance tuning, governance, and automation makes it an excellent choice for professionals aiming to advance their careers. With real-world case studies and expert mentoring, participants develop the expertise to tackle real business challenges. The certification not only validates skills but also opens doors to high-demand Azure data engineering roles.
Debalina DebData Engineer
Engaging and Hands-On Learning! The course provides an ideal blend of theory and practice, enabling participants to work with structured, semi-structured, and streaming data. The detailed insights into Azure services and best practices ensure that learners can confidently design and implement scalable data solutions. The emphasis on automation and real-time data streaming prepares professionals for high-performance cloud environments. Additionally, the course includes performance optimization techniques to maximize efficiency in data pipelines.
Rohit NingannavarData Scientist
Essential for Data Professionals! Covering everything from data ingestion to security, this course prepares professionals to build highly efficient and optimized Azure data platforms. The hands-on experience and expert guidance make it a must-have certification for anyone working with cloud-based data solutions. By mastering Azure's advanced data engineering tools, participants gain the ability to implement cutting-edge analytics solutions. The well-rounded curriculum ensures professionals stay ahead in the evolving field of cloud-based data management.
Vivekanand MunisamySenior Project Manager
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