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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 UAE teaches professionals to create and operate and maintain data solutions on Microsoft Azure platform. The training provides extensive knowledge about data storage and processing security and integration features thro
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DP-203T00: Data Engineering on Microsoft Azure Training Course
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DP-203T00: Data Engineering on Microsoft Azure Course Overview
The DP-203T00: Data Engineering on Microsoft Azure training in UAE shows professionals the process of building and managing data solutions using Microsoft Azure cloud infrastructure. Students learn to handle data flow management and storage optimization as well as Azure-based system security implementation through the program.
Participants enrolled in the program will learn how to use Azure Synapse Analytics and Azure Data Lake and Azure Data Factory as well as real-time data streaming tools. The training demonstrates data uptake strategies and teaches students to adjust and activate data processing by using batch and real-time execution approaches. The program provides necessary training about best practices and compliance requirements and performance improvement techniques for the cloud environment.
Students learn how to build diverse data systems by receiving instruction about Azure Apache Spark performance optimization as well as data protection methods and Azure Data Factory workflow management.
The certification preparation segment of this course enables IT professionals and data engineers and analysts to show their capabilities with Azure data solutions. This training meets the needs of individuals who need to advance their professional standing in either cloud data engineering or enterprise data platform development using cloud technology.
The Microsoft Azure data ecosystem training delivered in the course provides learners with complete mastery that enables them to build data solutions that scale and deliver high performance while securing contemporary businesses.
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Course Objectives
- Design and implement scalable storage architectures using Azure Synapse Analytics, Azure Data Lake, and Azure Data Factory for efficient data management.
- Identify and troubleshoot operational bottlenecks to ensure continuous and efficient data pipeline performance.
- Implement data governance frameworks to maintain consistency, integrity, and compliance across cloud environments.
- Prepare thoroughly for the DP-203 certification by mastering key principles, industry best practices, and real-world applications.
- Develop and optimize data ingestion pipelines to handle structured, semi-structured, and real-time data streams effectively.
- Enhance data transformation processes by utilizing batch processing and real-time processing strategies.
- Reinforce data protection and regulatory adherence through encryption methods, access management, and Azure security mechanisms.
- Optimize ETL workflows and automate data processing using Azure Data Factory and orchestration solutions.
- Improve performance and cost efficiency by leveraging advanced methodologies for cloud-based data handling.
- Connect Azure-based data ecosystems with AI and analytics solutions to extract meaningful insights.
Audience
- Solution Architects
- Technical Consultants
- Data Engineers
- IT Professionals
- Database Administrators
- Cloud Engineers
- Software Developers
- Business Intelligence Professionals
- Machine Learning Engineers
- Data Analysts
Prerequisite
Recommended:
- AZ-900T00: Microsoft Azure Fundamentals
- AI+ Executive™
- 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 credential is designed for professionals skilled in architecting and deploying data analytics solutions by aggregating and refining information from multiple sources. It is ideal for data engineers, business intelligence experts, and data architects, validating their ability to structure, integrate, and process structured, semi-structured, and streaming data for analytical insights.
As an Azure Data Engineer, your responsibilities include assisting stakeholders in data exploration while building and maintaining secure, optimized, and regulation-compliant data workflows. Leveraging various Azure data services, you manage storage, data cleansing, and transformation to ensure datasets are properly structured for analytical use. Based on organizational requirements, data architectures may include:
- Advanced Data Management Systems
- High-Volume Data Processing Solutions
- Unified Data Lake Architectures
Beyond data operations, the emphasis is on ensuring that data storage and pipelines remain scalable, fault-tolerant, and high-performing. Core duties involve troubleshooting technical issues, improving data consistency, and enhancing system efficiency to meet large-scale business demands.
Required Skills and Knowledge:
Candidates must possess expertise in data processing languages such as:
- SQL
- Python
- Scala
Moreover, a solid grasp of parallel computing techniques and data architecture frameworks is crucial. This certification validates skills in developing and deploying solutions utilizing:
- 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 remain valid for one year.
- Renewal is free and can be done online through Microsoft Learn.
- The renewal period begins six months before the certification expires.
- Certifications obtained before June 2021 are valid for two years and can be renewed under the revised policy.
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FAQ’s
What skills does the Azure Data Engineer Associate certification confirm?
This credential certifies proficiency in designing, managing, and optimizing data solutions on Azure. It is aimed at professionals handling structured, unstructured, and real-time streaming data while ensuring secure and efficient data pipelines for analytics. Candidates will also gain expertise in performance tuning, data governance, and compliance to meet enterprise-level data requirements.
Are there any suggested skills before attempting this certification?
While there are no mandatory prerequisites, candidates should be well-versed in data processing languages like SQL, Python, or Scala. Knowledge of Azure-based tools such as Synapse Analytics, Data Factory, and Databricks is highly advantageous. Additionally, familiarity with parallel processing, real-time data ingestion, and cloud security practices will significantly enhance preparation for this certification.
Which test must be cleared to earn this certification?
Candidates need to pass the DP-203: Data Engineering on Microsoft Azure exam, which evaluates expertise in data transformation, integration, security, and performance enhancement in Azure’s data ecosystem. The exam also measures the ability to build scalable data architectures, automate workflows, and ensure data quality for business intelligence and analytics solutions.
How long does the DP-203 exam take?
The test duration is 120 minutes and consists of multiple-choice questions, case studies, and hands-on problem-solving tasks, assessing both conceptual understanding and practical application. Candidates are required to analyze data scenarios, troubleshoot pipeline inefficiencies, and implement optimization techniques, ensuring a well-rounded assessment of their data engineering skills.
What topics are covered in this certification?
The certification focuses on:
- Architecting and implementing scalable data storage solutions
- Managing data workflows using Azure Data Factory and Databricks
- Ensuring compliance, security, and performance optimization in data operations
- Processing structured, unstructured, and real-time streaming data
- Implementing ETL/ELT processes, automating data pipelines, and designing cost-effective data solutions aligned with business needs
Who should pursue this certification?
This certification benefits data engineers, business intelligence specialists, data architects, and analytics professionals responsible for designing and managing cloud-based data infrastructures on Azure. It is particularly valuable for individuals looking to validate their expertise in handling large-scale data systems, improving data quality, and leveraging Azure’s ecosystem for enterprise analytics.
Can candidates use external resources during the DP-203 exam?
No, the exam is strictly monitored and closed book. Candidates must rely on their knowledge and practical experience to solve Azure data engineering scenarios. Online materials, personal notes, or external assistance are not permitted, so thorough hands-on preparation is essential to succeed in this exam.
What is the minimum passing score for DP-203?
A score of at least 700 out of 1000 is required to pass. Evaluation is based on accuracy, efficiency, and the ability to develop structured and optimized data solutions. Scores are calculated based on weighted exam sections, and candidates are encouraged to review official learning paths and practice tests to improve their chances of achieving a passing score.
What languages are available for the DP-203 exam?
The exam is accessible in multiple languages, including English, Japanese, Korean, Simplified Chinese, French, German, and Spanish. Candidates should check language availability before scheduling their test. If a preferred language is not available, candidates can request accommodations or use Microsoft’s official translation support for assistance.
Does this course fully prepare candidates for the DP-203 exam?
Yes, the course is structured around DP-203 exam objectives, featuring practical assignments, real-world case studies, and interactive sessions. Additionally, practice exams and hands-on experience with Azure data services further strengthen exam readiness. Candidates are encouraged to engage in project-based learning, participate in live labs, and explore Microsoft’s official learning resources to reinforce key concepts before taking the exam.
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Reviews
Structured and Insightful! The curriculum follows a systematic approach, covering essential tools like Azure Data Factory, Synapse Analytics, Databricks, and real-time data pipelines. Expert-led sessions ensure that even intricate topics are easy to grasp. Participants gain practical experience in managing diverse data formats, ensuring seamless interoperability across Azure’s data ecosystem. By blending theory with application-based learning, this program is ideal for both entry-level and seasoned professionals.
Jaya KamalSoftware Engineer
Designed for Career Growth! Whether you are a data engineer, analyst, or architect, this program builds proficiency in handling large-scale data infrastructures. The focus on process optimization, data governance, and automation makes it a valuable investment for career advancement. Real-world case studies and expert guidance help participants tackle industry-specific challenges with confidence. Earning this certification validates technical expertise and significantly enhances career prospects in Azure data engineering.
Venkat IyerProject Manager
Engaging and Hands-On Learning! The course balances theoretical foundations with applied techniques, allowing participants to work with structured, semi-structured, and real-time streaming data. In-depth modules on Azure services and data pipeline design help learners develop scalable and efficient architectures. Key areas such as automation and real-time data workflows are emphasized to prepare professionals for high-performance cloud environments. Additionally, optimization strategies are introduced to enhance pipeline performance.
Adnaan al-ShakoorData Scientist
Essential for Data Professionals! Covering everything from data ingestion to security measures, this program empowers professionals to develop highly efficient Azure-based data solutions. Interactive labs and expert mentoring make this an indispensable certification for those working with cloud analytics platforms. Mastering Azure’s advanced data engineering tools allows participants to implement next-generation business intelligence solutions. The detailed curriculum ensures that professionals remain competitive in the rapidly evolving field of cloud data management.
Sangeeta PathakData Analyst
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