Course Overview
Overview
This course will provide foundational level knowledge of cloud services and how those services are provided with Microsoft Azure. The course can be taken as an optional first step in learning about cloud services and Microsoft Azure, before taking further Microsoft Azure or Microsoft cloud services courses.
The course will cover general cloud computing concepts as well as general cloud computing models and services such as Public, Private and Hybrid cloud and Infrastructure-as-a-Service (IaaS), Platform as-a Service(PaaS) and Software-as-a-Service (SaaS). It will also cover some core Azure services and solutions, as well as key Azure pillar services concerning security, privacy, compliance and trust. It will finally cover pricing and support services available with Azure.
Target Audience
- It is targeted for anyone that wants to learn more about Microsoft Azure.
- At Course Completion
- Understand general cloud computing concepts
- Understand core services available with Microsoft Azure
- Understand security, privacy, compliance and trust with Microsoft Azure
- Understand pricing and support models available with Microsoft
Prerequisites
There are no pre-requisites for taking this course. Technical IT experience is not required however some general IT knowledge or experience would be beneficial.
Course Outline
Module 1: Cloud Concepts
Module 2: Core Azure Services
Module 3: Security, Privacy, Compliance and Trust
Module 4: Azure Pricing and Support
Exam Code
AZ-900
| 2026 CLASSES | |||||
|---|---|---|---|---|---|
| Jan | Feb | Mar | Apr | May | Jun |
| 19 – 11 | – | 2 – 4 | – | 25 – 26 | – |
| Jul | Aug | Sep | Oct | Nov | Dec |
| 13 – 14 | – | 7 – 8 | – | 9 – 10 | – |
Overview
In this course, students will gain foundational knowledge of core data concepts and related Microsoft Azure data services. Students will learn about core data concepts such as relational, non-relational, big data, and analytics, and build their foundational knowledge of cloud data services within Microsoft Azure.
Students will explore fundamental relational data concepts and relational database services in Azure. They will explore Azure storage for non-relational data and the fundamentals of Azure Cosmos DB. Students will learn about large scale data warehousing, real-time analytics, and data visualization.
Target Audience
The audience for this course is individuals who want to learn the fundamentals of database concepts in a cloud environment, get basic skilling in cloud data services, and build their foundational knowledge of cloud data services within Microsoft Azure.
Course Outline
Module 1: Explore core data concepts
Module 2: Explore data roles and services
Module 3: Explore fundamental relational data concepts
Module 4: Explore relational database services in Azure
Module 5: Explore Azure Storage for non-relational data
Module 6: Explore fundamental of Azure Cosmos DB
Module 7: Explore fundamentals of large-scale data warehousing
Module 8: Explore fundamentals of real-time analytics
Module 9: Explore fundamentals of data visualization
Exam Code
DP-900
| 2026 CLASSES | |||||
|---|---|---|---|---|---|
| Jan | Feb | Mar | Apr | May | Jun |
| – | 9 | – | 6 | – | 22 |
| Jul | Aug | Sep | Oct | Nov | Dec |
| – | 24 | – | 26 | – | 21 |
Overview
This course introduces fundamentals concepts related to artificial intelligence (AI), and the services in Microsoft Azure that can be used to create AI solutions. The course is not designed to teach students to become professional data scientists or software developers, but rather to build awareness of common AI workloads and the ability to identify Azure services to support them. The hands-on exercises in the course are based on Learn modules, and students are encouraged to use the content on Learn as reference materials to reinforce what they learn in the class and to explore topics in more depth.
Target Audience
The Azure AI Fundamentals course is designed for anyone interested in learning about the types of solution artificial intelligence (AI) makes possible, and the services on Microsoft Azure that you can use to create them. You don’t need to have any experience of using Microsoft Azure before taking this course, but a basic level of familiarity with computer technology and the Internet is assumed. Some of the concepts covered in the course require a basic understanding of mathematics, such as the ability to interpret charts. The course includes hands on activities that involve working with data and running code, so a knowledge of fundamental programming principles will be helpful.
Prerequisites
Prerequisite certification is not required before taking this course. Successful Azure AI Fundamental
students start with some basic awareness of computing and internet concepts, and an interest in using
Azure AI services.
Specifically:
Experience using computers and the internet.
Interest in use cases for AI applications and machine learning models.
A willingness to learn through hands-on exploration.
Course Outline
Module 1: Get started with AI on Azure
Module 2: Use Automated Machine Learning in Azure Machine Learning
Module 3: Create a regression model with Azure Machine Learning designer
Module 4: Create a classification model with Azure Machine Learning designer
Module 5: Create a clustering model with Azure Machine Learning designer
Module 6: Analyze images with the Computer Vision service
Module 7: Classify images with the Custom Vision service
Module 8: Detect objects in images with the Custom Vision service
Module 9: Detect and analyze faces with the Face service
Module 10: Read text with the Computer Vision service
Module 11: Analyze receipts with the Form Recognizer service
Module 12: Analyze text with the Language service
Module 13: Recognize and synthesize speech
Module 14: Translate text and speech
Module 15: Create a language model with Conversational Language Understanding
Module 16: Build a bot with the Language Service and Azure Bot Service
Exam Code
AI-900
| 2026 CLASSES | |||||
|---|---|---|---|---|---|
| Jan | Feb | Mar | Apr | May | Jun |
| – | 9 | – | 6 | – | 22 |
| Jul | Aug | Sep | Oct | Nov | Dec |
| – | 24 | – | 26 | – | 21 |
Overview
This course teaches IT Professionals how to manage their Azure subscriptions, secure identities, administer the infrastructure, configure virtual networking, connect Azure and on-premises sites, manage network traffic, implement storage solutions, create and scale virtual machines, implement web apps and containers, back up and share data, and monitor your solution.
Target Audience
This course is for Azure Administrators. The Azure Administrator implements, manages, and monitors identity, governance, storage, compute, and virtual networks in a cloud environment. The Azure Administrator will provision, size, monitor, and adjust resources as appropriate.
Prerequisites
Successful Azure Administrators start this role with experience on operating systems, virtualization, cloud infrastructure, storage structures, and networking.
- Understanding of on-premises virtualization technologies, including: VMs, virtual networking, and virtual hard disks.
- Understanding of network configuration, including TCP/IP, Domain Name System (DNS), virtual private networks (VPNs), firewalls, and encryption technologies.
- Understanding of Active Directory concepts, including domains, forests, domain controllers, replication, Kerberos protocol, and Lightweight Directory Access Protocol (LDAP).
- Understanding of resilience and disaster recovery, including backup and restore operations.
Course Outline
Module 1: Identity
Module 2: Governance and Compliance
Module 3: Azure Administration
Module 4: Virtual Networking
Module 5: Intersite Connectivity
Module 6: Network Traffic Management
Module 7: Azure Storage
Module 8: Azure Virtual Machines
Module 9: Serverless Computing
Module 10: Data Protection
Module 11: Monitoring
Exam Code
AZ-104
| 2026 CLASSES | |||||
|---|---|---|---|---|---|
| Jan | Feb | Mar | Apr | May | Jun |
| 5 – 8 | – | 16 – 19 | – | 11 – 14 | – |
| Jul | Aug | Sep | Oct | Nov | Dec |
| 6 – 9 | – | 7 – 11 | – | 2 – 5 | – |
Overview
This course provides students with the knowledge and skills to administer a SQL Server database infrastructure for cloud, on-premises and hybrid relational databases and who work with the Microsoft PaaS relational database offerings. Additionally, it will be of use to individuals who develop applications that deliver content from SQL-based relational databases.
Target Audience
The audience for this course is data professionals managing data and databases who want to learn about administering the data platform technologies that are available on Microsoft Azure. This course is also valuable for data architects and application developers who need to understand what technologies are available for the data platform with Azure and how to work with those technologies through applications.
Prerequisites
In addition to their professional experience, students who take this training should have technical knowledge equivalent to the following courses:
- Azure Fundamentals
- Azure Data Fundamentals
Course Outline
Module 1: The Role of the Azure Database Administrator
Module 2: Plan and Implement Data Platform Resources
Module 3: Implement a Secure Environment
Module 4: Monitor and Optimize Operational Resources
Module 5: Optimize Query Performance
Module 6: Automation of Tasks
Module 7: Plan and Implement a High Availability and Disaster Recovery Environment
Exam Code
DP-300
| 2026 CLASSES | |||||
|---|---|---|---|---|---|
| Jan | Feb | Mar | Apr | May | Jun |
| – | 23 – 26 | – | 13 – 16 | – | 22 – 25 |
| Jul | Aug | Sep | Oct | Nov | Dec |
| – | 10 – 13 | – | 19 – 22 | – | 7 – 10 |
Overview
The DP-100 certification is the definitive credential for professionals who apply data science and machine learning (ML) to implement and run workloads on Microsoft Azure. Rather than focusing on abstract theory, this course is a practical validation of your ability to use Azure Machine Learning and MLflow to train, deploy, and manage models at scale.
Key Learning Outcomes
- Azure Machine Learning Workspace: Master the creation and management of the core environment, including compute targets (instances and clusters), datastores, and Git integration for version control.
- No-Code & Pro-Code Training: Learn to use the Azure Machine Learning Designer (drag-and-drop) for rapid prototyping and the Python SDK for sophisticated, custom model development.
- Automated ML (AutoML): Leverage automated workflows to identify the best algorithms and hyperparameters for tabular, vision, and natural language processing tasks.
- Model Optimization & GenAI: Gain specific skills in optimizing language models through Prompt Engineering, Retrieval Augmented Generation (RAG), and fine-tuning base models from the Azure AI model catalog.
- MLOps & Responsible AI: Implement production-grade practices, including model tracking with MLflow, automated retraining pipelines, and ensuring fairness and interpretability using built-in explainers.
Course Outline
- Design and Prepare a Machine Learning Solution: Choosing compute specifications, identifying dataset structures, and managing workspace assets.
- Explore Data and Run Experiments: Using notebooks to wrangle data, tracking training with MLflow, and running automated machine learning experiments.
- Train and Deploy Models: Running training scripts as jobs, implementing ML pipelines, and deploying models to real-time or batch endpoints.
- Optimize Language Models for AI Applications: Preparing data for RAG, configuring vector stores (like Azure AI Search), and fine-tuning language models.
Target Audience
- Data Scientists: Who want to operationalize their Python and ML knowledge in a scalable cloud environment.
- AI Engineers: Responsible for building and deploying AI-driven applications using Azure services.
- Machine Learning Engineers: Focusing on the “Ops” side of ML (MLOps) to ensure reliable model performance in production.
Exam Code
DP-100
| 2026 CLASSES | |||||
|---|---|---|---|---|---|
| Jan | Feb | Mar | Apr | May | Jun |
| 5 – 7 | 9 – 11 | 2 – 4 | 20 – 22 | 18 – 20 | 8 – 10 |
| Jul | Aug | Sep | Oct | Nov | Dec |
| 20 – 22 | 3 – 5 | 7 – 9 | 26 – 28 | 2 – 4 | 14 – 16 |
Overview
This course teaches developers how to create end-to-end solutions in Microsoft Azure. Students will learn how to implement Azure compute solutions, create Azure Functions, implement and manage web apps, develop solutions utilizing Azure storage, implement authentication and authorization, and secure their solutions by using KeyVault and Managed Identities. Students will also learn how to connect to and consume Azure services and third-party services, and include event- and message-based models in their solutions. The course also covers monitoring, troubleshooting, and optimizing Azure solutions.
Target Audience
- Students in this course are interested in Azure development or in passing the Microsoft Azure Developer
- Associate certification exam.
Prerequisites
Students should have 1-2 years professional development experience and experience with Microsoft Azure. They must be able to program in an Azure Supported Language.
Course Outline
Module 1: Creating Azure App Service Web Apps
Module 2: Implement Azure functions
Module 3: Develop solutions that use blob storage
Module 4: Develop solutions that use Cosmos DB storage
Module 5: Implement IaaS solutions
Module 6: Implement user authentication and authorization
Module 7: Implement secure cloud solutions
Module 8: Implement API Management
Module 9: Develop App Service Logic Apps
Module 10: Develop event-based solutions
Module 11: Develop message-based solutions
Module 12: Monitor and optimize Azure solutions
Module 13: Integrate caching and content delivery within solutions
Exam Code
AZ-204
| 2026 CLASSES | |||||
|---|---|---|---|---|---|
| Jan | Feb | Mar | Apr | May | Jun |
| 5 – 8 | – | 16 – 19 | – | 11 – 14 | – |
| Jul | Aug | Sep | Oct | Nov | Dec |
| 6 – 9 | – | 21 – 25 | – | 2 – 5 | – |
Exam Code
AI-102 Designing and Implementing an Azure AI Solution is intended for software developers wanting to build AI infused applications that leverage Azure Cognitive Services, Azure Cognitive Search, and Microsoft Bot Framework. The course will use C# or Python as the programming language.
Target Audience
Software engineers concerned with building, managing and deploying AI solutions that leverage Azure Cognitive Services, Azure Cognitive Search, and Microsoft Bot Framework. They are familiar with C# or Python and have knowledge on using REST-based APIs to build computer vision, language analysis, knowledge mining, intelligent search, and conversational AI solutions on Azure.
Prerequisites
Before attending this course, students must have:
- Knowledge of Microsoft Azure and ability to navigate the Azure portal
- Knowledge of either C# or Python
- Familiarity with JSON and REST programming semantics
- If you are new to artificial intelligence, and want an overview of AI capabilities on Azure, consider completing the Azure AI Fundamentals certification before taking this one..
Course Outline
Module 1: Prepare to develop AI solutions on Azure
Module 2: Create and consume Cognitive Services
Module 3: Secure Cognitive Services
Module 4: Monitor Cognitive Services
Module 5: Deploy cognitive services in containers
Module 6: Extract insights from text with the Language service
Module 7: Translate text with the Translator service
Module 8: Create speech-enabled apps with the Speech service
Module 9: Translate speech with the speech service
Module 10: Build a Language Understanding model
Module 11: Publish and use a Language Understanding app
Module 12: Build a question answering solution
Module 13: Create a bot with the Bot Framework SDK
Module 14: Create a Bot with the Bot Framework Composer
Module 15: Analyze images
Module 16: Analyze video
Module 17: Classify images
Module 18: Detect objects in images
Module 19: Detect, analyze, and recognize faces
Module 20: Read Text in Images and Documents with the Computer Vision Service
Module 21: Extract data from forms with Form Recognizer
Module 22: Create an Azure Cognitive Search solution
Module 23: Create a custom skill for Azure Cognitive Search
Module 24: Create a knowledge store with Azure Cognitive Search
Exam Code
AI-102
| 2026 CLASSES | |||||
|---|---|---|---|---|---|
| Jan | Feb | Mar | Apr | May | Jun |
| 5 – 7 | 9 – 11 | 2 – 4 | 20 – 22 | 18 – 20 | 8 – 10 |
| Jul | Aug | Sep | Oct | Nov | Dec |
| 20 – 22 | 3 – 5 | 7 – 9 | 26 – 28 | 2 – 4 | 14 – 16 |
Overview
In this course students will gain the knowledge and skills needed to implement security controls, maintain the security posture, and identify and remediate vulnerabilities by using a variety of security tools. The course covers scripting and automation, virtualization, and cloud N-tier architecture.
Target Audience
Students should have at least one year of hands-on experience securing Azure workloads and experience with security controls for workloads on Azure.
Prerequisites
Before attending this course, students must have knowledge of Microsoft Azure Administrator Associate
After completing this course, students will be able to:
- Describe specialized data classifications on Azure
- Identify Azure data protection mechanisms
- Implement Azure data encryption methods
- Secure Internet protocols and how to implement them on Azure
- Describe Azure security services and features
Course outline
Module 1: Identity and Access
Module 2: Platform Protection
Module 3: Security Operations
Module 4: Data and applications
Exam Code
AZ-500
| 2026 CLASSES | |||||
|---|---|---|---|---|---|
| Jan | Feb | Mar | Apr | May | Jun |
| – | 23 – 26 | – | 13 – 16 | – | 22 – 25 |
| Jul | Aug | Sep | Oct | Nov | Dec |
| – | 10 – 13 | – | 19 – 22 | – | 7 – 10 |
Overview
The Microsoft Azure Solutions Architect Expert is the highest-level certification for professionals who design cloud and hybrid solutions on Azure. It is important to note that the older “Architect Technologies” exam (AZ-303) was retired in 2022. It has been replaced by AZ-305: Designing Microsoft Azure Infrastructure Solutions, which focuses purely on the “design” aspect of architecture.
A Solutions Architect translates business requirements into secure, scalable, and reliable cloud designs. They don’t just build, they strategize how compute, network, storage, and security work together to meet a company’s goals while optimizing for cost and performance.
Prerequisites
To earn the Expert title in 2026, passing the AZ-305 exam is only half the battle. You must also hold the:
- Microsoft Certified: Azure Administrator Associate (AZ-104)
- Pro Tip: While you can take the exams in any order, the certification is only granted once both are active.
- Most candidates find that the hands-on skills learned in AZ-104 are essential to understanding the design principles in AZ-305.
Key Learning Outcomes
- Identity & Governance Design: Implement advanced identity solutions with Microsoft Entra ID, define hierarchical structures with Management Groups and Subscriptions, and enforce compliance via Azure Policy.
- Data Storage Strategy: Choose the right data platform (SQL, Cosmos DB, or Blob Storage) based on workload needs like latency, consistency, and global scale.
- Business Continuity & Resilience: Design for the “worst-case scenario” using Azure Site Recovery, multi-region failover, and high-availability structures like Availability Zones.
- Infrastructure Design: Architect robust networking (VNet peering, ExpressRoute), compute (AKS, Serverless, VMs), and application integration (Logic Apps, Service Bus).
- Migration Design: Plan complex migrations from on-premises to the cloud using the Cloud Adoption Framework (CAF) and tools like Azure Migrate.
Course outline
- Design Identity, Governance, and Monitoring (25–30%): Logging with Azure Monitor, RBAC strategies, and resource tagging.
- Design Data Storage Solutions (20–25%): Relational (SQL) vs. Non-relational (Cosmos DB) and lifecycle management for unstructured data.
- Design Business Continuity Solutions (15–20%): Backup and recovery objectives (RTO/RPO) and high availability for all tiers.
- Design Infrastructure Solutions (30–35%): Compute sizing, network security (NSGs, Firewalls), and application delivery (Front Door, App Gateway).
Exam Code
AZ-305
| 2026 CLASSES | |||||
|---|---|---|---|---|---|
| Jan | Feb | Mar | Apr | May | Jun |
| – | 23 – 26 | – | 13 – 16 | – | 22 – 25 |
| Jul | Aug | Sep | Oct | Nov | Dec |
| – | 10 – 13 | – | 19 – 22 | – | 7 – 10 |
