Microsoft Azure Training - United Kingdom

Microsoft Azure Security Technologies AZ500 Course Outline

Within this Microsoft training course, you will learn the following modules:

Module 1: Manage Identity and Access

This module covers Azure Active Directory, Azure Identity Protection, Enterprise Governance, Azure AD PIM, and Hybrid Identity.

Lessons

• Azure Active Directory
• Hybrid Identity
• Azure Identity Protection
• Azure AD Privileged Identity Management
• Enterprise Governance

Lab: Role-Based Access Control

Lab: Azure Policy

Lab: Resource Manager Locks

Lab: MFA, Conditional Access and AAD Identity Protection

Lab: Azure AD Privileged Identity Management

Lab: Implement Directory Synchronization

After completing this module, students will be able to:

• Implement enterprise governance strategies including role-based access control, Azure policies, and resource locks.
• Implement an Azure AD infrastructure including users, groups, and multi-factor authentication.
• Implement Azure AD Identity Protection including risk policies, conditional access, and access reviews.
• Implement Azure AD Privileged Identity Management including Azure AD roles and Azure resources.
• Implement Azure AD Connect including authentication methods and on-premises directory synchronization.

Module 2: Implement Platform Protection

This module covers perimeter, network, host, and container security.

Lessons

• Perimeter Security
• Network Security
• Host Security
• Container Security

Lab: Configuring and Securing ACR and AKS

Lab: Azure Firewall

Lab: Network Security Groups and Application Security Groups

After completing this module, students will be able to:

• Implement perimeter security strategies including Azure Firewall.
• Implement network security strategies including Network Security Groups and Application Security Groups.
• Implement host security strategies including endpoint protection, remote access management, update management, and disk encryption.
• Implement container security strategies including Azure Container Instances, Azure Container Registry, and Azure Kubernetes.

Module 3: Secure Data and Applications

This module covers Azure Key Vault, application security, storage security, and SQL database security.

Lessons

• Azure Key Vault
• Application Security
• Storage Security
• SQL Database Security

Lab: Key Vault (Implementing Secure Data by setting up Always Encrypted)

Lab: Securing Azure SQL Database

Lab: Service Endpoints and Securing Storage

After completing this module, students will be able to:

• Implement Azure Key Vault including certificates, keys, and secretes.
• Implement application security strategies including app registration, managed identities, and service endpoints.
• Implement storage security strategies including shared access signatures, blob retention policies, and Azure Files authentication.
• Implement database security strategies including authentication, data classification, dynamic data masking, and always encrypted.

Module 4: Manage Security Operations

This module covers Azure Monitor, Azure Security Center, and Azure Sentinel.

Lessons

• Azure Monitor
• Azure Security Center
• Azure Sentinel

Lab: Azure Sentinel

Lab: Azure Security Center

Lab: Azure Monitor

After completing this module, students will be able to:

• Implement Azure Monitor including connected sources, log analytics, and alerts.
• Implement Azure Security Center including policies, recommendations, and just in time virtual machine access.
• Implement Azure Sentinel including workbooks, incidents, and playbooks.

Designing and Implementing Microsoft DevOps solutions AZ400 Course Outline

Within this Microsoft training course, you will learn the following modules:

Module 1: Get started on a DevOps transformation journey

Lessons

• Introduction to DevOps
• Choose the right project
• Describe team structures
• Choose the DevOps tools
• Plan Agile with GitHub Projects and Azure Boards
• Introduction to source control
• Describe types of source control systems
• Work with Azure Repos and GitHub

Lab: Agile planning and portfolio management with Azure Boards

Lab: Version controlling with Git in Azure Repos

After completing this module, students will be able to:

• Understand what DevOps is and the steps to accomplish it
• Identify teams to implement the process
• Plan for the transformation with shared goals and timelines
• Plan and define timelines for goals
• Understand different projects and systems to guide the journey
• Select a project to start the DevOps transformation
• Identify groups to minimize initial resistance
• Identify project metrics and Key Performance Indicators (KPI's)
• Understand agile practices and principles of agile development
• Create a team and agile organizational structure

Module 2: Development for enterprise DevOps

Lessons

• Structure your Git Repo
• Manage Git branches and workflows
• Collaborate with pull requests in Azure Repos
• Explore Git hooks
• Plan foster inner source
• Manage Git repositories
• Identify technical debt

Lab: Version controlling with Git in Azure Repos

After completing this module, students will be able to:

• Understand Git repositories
• Implement mono repo or multiple repos
• Explain how to structure Git Repos
• Implement a change log
• Describe Git branching workflows
• Implement feature branches
• Implement GitFlow
• Fork a repo
• Leverage pull requests for collaboration and code reviews
• Give feedback using pull requests

Module 3: Implement CI with Azure Pipelines and GitHub Actions

Lessons

• Explore Azure Pipelines
• Manage Azure Pipeline agents and pools
• Describe pipelines and concurrency
• Explore Continuous integration
• Implement a pipeline strategy
• Integrate with Azure Pipelines
• Introduction to GitHub Actions
• Learn continuous integration with GitHub Actions
• Design a container build strategy

Lab: Configuring agent pools and understanding pipeline styles

Lab: Enabling continuous integration with Azure Pipelines

Lab: Integrating external source control with Azure Pipelines

Lab: Implementing GitHub Actions by using DevOps Starter

Lab: Deploying Docker Containers to Azure App Service web apps

After completing this module, students will be able to:

• Describe Azure Pipelines
• Explain the role of Azure Pipelines and its components
• Decide Pipeline automation responsibility
• Understand Azure Pipeline key terms
• Choose between Microsoft-hosted and self-hosted agents
• Install and configure Azure pipelines Agents
• Configure agent pools
• Make the agents and pools secure
• Use and estimate parallel jobs

Module 4: Design and implement a release strategy

Lessons

• Introduction to continuous delivery
• Create a release pipeline
• Explore release strategy recommendations
• Provision and test environments
• Manage and modularize tasks and templates
• Automate inspection of health

Lab: Creating a release dashboard

Lab: Controlling deployments using Release Gates

After completing this module, students will be able to:

• Explain continuous delivery (CD)
• Implement continuous delivery in your development cycle
• Understand releases and deployment
• Identify project opportunities to apply CD
• Explain things to consider when designing your release strategy
• Define the components of a release pipeline and use artifact sources
• Create a release approval plan
• Implement release gates
• Differentiate between a release and a deployment

Module 5: Implement a secure continuous deployment using Azure Pipelines

Lessons

• Introduction to deployment patterns
• Implement blue-green deployment and feature toggles
• Implement canary releases and dark launching
• Implement A/B testing and progressive exposure deployment
• Integrate with identity management systems
• Manage application configuration data

Lab: Configuring pipelines as code with YAML

Lab: Setting up and running functional tests

Lab: Integrating Azure Key Vault with Azure DevOps

After completing this module, students will be able to:

• Explain the terminology used in Azure DevOps and other Release Management Tooling
• Describe what a Build and Release task is, what it can do, and some available deployment tasks
• Implement release jobs
• Differentiate between multi-agent and multi-configuration release job
• Provision and configure target environment
• Deploy to an environment securely using a service connection
• Configure functional test automation and run availability tests
• Setup test infrastructure
• Use and manage task and variable groups

Module 6: Manage infrastructure as code using Azure and DSC

Lessons

• Explore infrastructure as code and configuration management
• Create Azure resources using Azure Resource Manager templates
• Create Azure resources by using Azure CLI
• Explore Azure Automation with DevOps
• Implement Desired State Configuration (DSC)
• Implement Bicep

Lab: Azure deployments using Azure Resource Manager templates

After completing this module, students will be able to:

• Understand how to deploy your environment
• Plan your environment configuration
• Choose between imperative versus declarative configuration
• Explain idempotent configuration
• Create Azure resources using ARM templates
• Understand ARM templates and template components
• Manage dependencies and secrets in templates
• Organize and modularize templates
• Create Azure resources using Azure CLI

Module 7: Implement security and validate code bases for compliance

Lessons

• Introduction to Secure DevOps
• Implement open-source software
• Software Composition Analysis
• Static analyzers
• OWASP and Dynamic Analyzers
• Security Monitoring and Governance

Lab: Implement security and compliance in Azure Pipelines

Lab: Managing technical debt with SonarQube and Azure DevOps

After completing this module, students will be able to:

• Identify SQL injection attack
• Understand DevSecOps
• Implement pipeline security
• Understand threat modeling
• Implement open-source software
• Explain corporate concerns for open-source components
• Describe open-source licenses
• Understand the license implications and ratings
• Work with Static and Dynamic Analyzers
• Configure Microsoft Defender for Cloud

Module 8: Design and implement a dependency management strategy

Lessons

• Explore package dependencies
• Understand package management
• Migrate, consolidate, and secure artifacts
• Implement a versioning strategy
• Introduction to GitHub Packages

Lab: Package management with Azure Artifacts

After completing this module, students will be able to:

• Define dependency management strategy
• Identify dependencies
• Describe elements and componentization of a dependency management
• Scan your codebase for dependencies
• Implement package management
• Manage package feed
• Consume and create packages
• Publish packages
• Identify artifact repositories
• Migrate and integrate artifact repositories

Module 9: Implement continuous feedback

Lessons

• Implement tools to track usage and flow
• Develop monitor and status dashboards
• Share knowledge within teams
• Design processes to automate application analytics
• Manage alerts, Blameless retrospectives and a just culture

Lab: Monitoring application performance with Application Insights

Lab: Integration between Azure DevOps and Microsoft Teams

Lab: Sharing Team Knowledge using Azure Project Wikis

After completing this module, students will be able to:

• Implement tools to track feedback
• Plan for continuous monitoring
• Implement Application Insights
• Use Kusto Query Language (KQL)
• Implement routing for mobile applications
• Configure App Center Diagnostics
• Configure alerts
• Create a bug tracker
• Configure Azure Dashboards
• Work with View Designer in Azure Monitor

Migrate SQL Workloads to Azure DP050​ Course Outline

Module 1: Introducing Data Platform Modernisation

• Understand Data Platform Modernisation
• Understanding the Stages of Migration
• Data Migration Paths

Lab: Introducing Data Platform Modernisation

• Understand Data Platform Modernisation
• Understand the Stages of Migration
• Data Migration Paths

Module 2: Choose the Right Tools for Data Migration

• Discover the Database Migration Guide
• Build Your Data Estate Inventory Using Map Toolkit
• Identify Migration Candidates Using Data Migration Assistant
• Evaluate a Data Workload Using Database Experimentation Assistant
• Data Migration using Azure Database Migration Service
• Migrate Non-SQL Server Workloads to Azure Using SQL Migration Assistant

Lab: Choose the Right Tools for Data Migration

• Identify Migration Candidates using Data Migration Assistant
• Evaluate a Data Workload using Database Experimentation Assistant

Module 3: Migrating SQL Workloads to Azure Virtual Machines

• Considerations of SQL Server to Azure VM Migrations
• SQL Workloads to Azure VM Migration Options
• Implementing High Availability and Disaster Recovery Scenarios

Lab: Migrating SQL Workloads to Azure Virtual Machines

Module 4: Migrate SQL Workloads to Azure SQL Databases

• Choose the Right SQL Server Instance Option in Azure
• Migrate SQL Server to Azure SQL DB Offline
• Migrate SQL Server to Azure SQL DB Online
• Load and Move Data to Azure SQL Database

Lab: Migrate SQL Workloads to Azure SQL Databases

Module 5: Migrate SQL Workloads to Azure SQL Database Managed Instance

• Evaluate Migration Scenarios to SQL Database Managed Instance
• Migrate to SQL Database Managed Instance
• Load and Move Data to SQL Database Managed Instance
• Application Configuration and Optimisation

Lab: Migrate SQL Workloads to Azure SQL Database Managed Instance

Microsoft Azure Administrator MAZ104 Course Outline

Within this Microsoft Azure training course, you will learn the following modules:

Module 1: Identity

In this module, you will learn how to secure identities with Azure Active Directory and implement users and groups.

Lessons

• Azure Active Directory
• Users and Groups

Lab: Manage Azure Active Directory Identities

After completing this module, students will be able to:

• Secure and manage identities with Azure Active Directory.
• Implement and manage users and groups.

Module 2: Governance and Compliance

In this module, you will learn about managing your subscriptions and accounts, implementing Azure policies, and using Role-Based Access Control.

Lessons

• Subscriptions and Accounts
• Azure Policy
• Role-based Access Control (RBAC)

Lab: Manage Subscriptions and RBAC

Lab: Manage Governance via Azure Policy

After completing this module, students will be able to:

• Implement and manage Azure subscriptions and accounts.

• Implement Azure Policy, including custom policies.

• Use RBAC to assign permissions.

Module 3: Azure Administration

In this module, you will learn about the tools an Azure Administrator uses to manage their infrastructure. This includes the Azure Portal, Cloud Shell, Azure PowerShell, CLI, and Resource Manager Templates. This module includes:

Lessons

• Azure Administrator Tools
• ARM Templates

Lab: Manage Azure resources by Using ARM Templates

Lab: Manage Azure resources by Using Azure PowerShell (optional)

Lab: Manage Azure resources by Using Azure CLI (optional)

Lab: Manage Azure resources by Using the Azure Portal

After completing this module, students will be able to:

• Use the Azure Portal and Cloud Shell.
• Use Azure PowerShell and CLI.
• Use ARM Templates to deploy resources.

Module 4: Virtual Networking

In this module, you will learn about basic virtual networking concepts like virtual networks and subnetting, IP addressing, network security groups, Azure Firewall, and Azure DNS.

Lessons

• Virtual Networks
• Network Security groups
• Azure Firewall
• Azure DNS

Lab: Implement Virtual Networking

After completing this module, students will be able to:

• Implement virtual networks and subnets.
• Configure network security groups.
• Configure Azure Firewall.
• Configure private and public DNS zones.

Module 5: Intersite Connectivity

In this module, you will learn about intersite connectivity features including VNet Peering, Virtual Network Gateways, and Site-to-Site Connections.

Lessons

• VNet Peering
• VPN Gateway Connections
• ExpressRoute and Virtual WAN

Lab: Implement Intersite Connectivity

After completing this module, students will be able to:

• Configure VNet Peering.
• Configure VPN gateways.
• Choose the appropriate intersite connectivity solution.

Module 6: Network Traffic Management

In this module, you will learn about network traffic strategies including network routing and service endpoints, Azure Load Balancer, and Azure Application Gateway.

Lessons

• Network Routing and Endpoints
• Azure Load Balancer
• Azure Application Gateway
• Network Watcher

Lab: Implement Traffic Management

After completing this module, students will be able to:

• Configure network routing including custom routes and service endpoints.
• Configure an Azure Load Balancer.
• Configure an Azure Application Gateway.
• Configure Network Watcher.

Module 7: Azure Storage

In this module, you will learn about basic storage features including storage accounts, blob storage, Azure files and File Sync, storage security, and storage tools.

Lessons

• Storage Accounts
• Blob Storage
• Storage Security
• Azure Files and File Sync
• Managing Storage

Lab: Manage Azure storage

After completing this module, students will be able to:

• Create Azure storage accounts.
• Configure blob containers.
• Secure Azure storage.
• Configure Azure files shares and file sync.
• Manage storage with tools such as Storage Explorer.

Module 8: Azure Virtual Machines

In this module, you will learn about Azure virtual machines including planning, creating, availability and extensions.

Lessons

• Creating Virtual Machines
• Virtual Machine Availability
• Virtual Machine Extensions

Lab: Manage virtual machines

After completing this module, students will be able to:

• Plan for virtual machine implementations.
• Create virtual machines.
• Configure virtual machine availability, including scale sets.
• Use virtual machine extensions.

Module 9: PaaS Compute Options

In this module, you will learn how to administer serverless computing features like Azure App Service, Azure Container Instances, and Kubernetes.

Lessons

• Azure App Service Plans
• Azure App Service
• Container Services
• Azure Kubernetes Service

Lab: Implement Web Apps

Lab: Implement Azure Kubernetes Service

Lab: Implement Azure Container Instances

After completing this module, students will be able to:

• Create an app service plan.
• Create a web app.
• Implement Azure Container Instances.
• Implement Azure Kubernetes Service.

Module 10: Data Protection

In this module, you will learn about backing up files and folders, and virtual machine backups.

Lessons

• File and Folder Backups
• Virtual Machine Backups

Lab: Implement Data Protection

After completing this module, students will be able to:

• Backup and restore file and folders.
• Backup and restore virtual machines.

Module 11: Monitoring

In this module, you will learn about monitoring your Azure infrastructure including Azure Monitor, alerting, and log analytics.

Lessons

• Azure Monitor
• Azure Alerts
• Log Analytics

Lab: Implement Monitoring

After completing this module, students will be able to:

• Use Azure Monitor.
• Create Azure alerts.
• Query using Log Analytics.

Microsoft Azure AI Fundamentals AI900 Course Outline

Within this Microsoft training course, you will learn the following modules:

Module 1: Explore Fundamentals of Artificial Intelligence

In this module, you'll learn about common uses of artificial intelligence (AI), and the several types of workloads associated with AI. You'll then explore considerations and principles for responsible AI development.

Lessons

• Introduction to Artificial Intelligence
• Artificial Intelligence in Microsoft Azure

After completing this module, students will be able to:

• Describe Artificial Intelligence workloads and considerations

Module 2: Explore Fundamentals of Machine Learning

Machine learning is the foundation for modern AI solutions. In this module, you'll learn about some fundamental machine learning concepts, and how to use the Azure Machine Learning service to create and publish machine learning models.

Lessons

• Introduction to Machine Learning
• Azure Machine Learning

After completing this module, students will be able to:

• Describe fundamental principles of machine learning on Azure

Module 3: Explore Fundamentals of Computer Vision

Computer vision is the area of AI that deals with understanding the world visually, through images, video files, and cameras. In this module you'll explore multiple computer vision techniques and services.

Lessons

• Computer Vision Concepts
• Creating Computer Vision solutions in Azure

After completing this module, students will be able to:

• Describe features of computer vision workloads on Azure

Module 4: Explore Fundamentals of Natural Language Processing

This module describes scenarios for AI solutions that can process written and spoken language. You'll learn about Azure services that can be used to build solutions that analyze text, recognize, and synthesize speech, translate between languages, and interpret commands.

Lessons

• Introduction to Natural Language Processing
• Building Natural Language Solutions in Azure

After completing this module, students will be able to:

• Describe features of Natural Language Processing (NLP) workloads on Azure

Designing and Implementing a Data Science Solution on Azure DP100 Course Outline

Within this training course you will learn the following modules:

Module 1: Getting Started with Azure Machine Learning

In this module, you will learn how to provision an Azure Machine Learning workspace and use it to manage machine learning assets such as data, compute, model training code, logged metrics, and trained models. You will learn how to use the web-based Azure Machine Learning studio interface as well as the Azure Machine Learning SDK and developer tools like Visual Studio Code and Jupyter Notebooks to work with the assets in your workspace.

Lessons

• Introduction to Azure Machine Learning
• Working with Azure Machine Learning

Lab: Create an Azure Machine Learning Workspace

After completing this module, you will be able to

• Provision an Azure Machine Learning workspace
• Use tools and code to work with Azure Machine Learning

Module 2: Visual Tools for Machine Learning

This module introduces the Automated Machine Learning and Designer visual tools, which you can use to train, evaluate, and deploy machine learning models without writing any code.

Lessons

• Automated Machine Learning
• Azure Machine Learning Designer

Lab: Use Automated Machine Learning

Lab: Use Azure Machine Learning Designer

After completing this module, you will be able to

• Use automated machine learning to train a machine learning model
• Use Azure Machine Learning designer to train a model

Module 3: Running Experiments and Training Models

In this module, you will get started with experiments that encapsulate data processing, model training code, and use them to train machine learning models.

Lessons

• Introduction to Experiments
• Training and Registering Models

Lab: Train Models

Lab: Run Experiments

After completing this module, you will be able to

• Run code-based experiments in an Azure Machine Learning workspace
• Train and register machine learning models

Module 4: Working with Data

Data is a fundamental element in any machine learning workload, so in this module, you will learn how to create and manage datastores and datasets in an Azure Machine Learning workspace, and how to use them in model training experiments.

Lessons

• Working with Datastores
• Working with Datasets

Lab: Work with Data

After completing this module, you will be able to

• Create and use datastores
• Create and use datasets

Module 5: Working with Compute

One of the key benefits of the cloud is the ability to leverage compute resources on demand and use them to scale machine learning processes to an extent that would be infeasible on your own hardware. In this module, you'll learn how to manage experiment environments that ensure consistent runtime consistency for experiments, and how to create and use compute targets for experiment runs.

Lessons

• Working with Environments
• Working with Compute Targets

Lab: Work with Compute

After completing this module, you will be able to

• Create and use environments
• Create and use compute targets

Module 6: Orchestrating Operations with Pipelines

Now that you understand the basics of running workloads as experiments that leverage data assets and compute resources, it's time to learn how to orchestrate these workloads as pipelines of connected steps. Pipelines are key to implementing an effective Machine Learning Operationalization (ML Ops) solution in Azure, so you'll explore how to define and run them in this module.

Lessons

• Introduction to Pipelines
• Publishing and Running Pipelines

Lab: Create a Pipeline

After completing this module, you will be able to:

• Create pipelines to automate machine learning workflows
• Publish and run pipeline services

Module 7: Deploying and Consuming Models

Models are designed to help decision making through predictions, so they're only useful when deployed and available for an application to consume. In this module learn how to deploy models for real-time inferencing, and for batch inferencing.

Lessons

• Real-time Inferencing
• Batch Inferencing
• Continuous Integration and Delivery

Lab: Create a Real-time Inferencing Service

Lab: Create a Batch Inferencing Service

After completing this module, you will be able to

• Publish a model as a real-time inference service
• Publish a model as a batch inference service
• Describe techniques to implement continuous integration and delivery

Module 8: Training Optimal Models

By this stage of the course, you've learned the end-to-end process for training, deploying, and consuming machine learning models; but how do you ensure your model produces the best predictive outputs for your data? In this module, you'll explore how you can use hyperparameter tuning and automated machine learning to take advantage of cloud-scale compute and find the best model for your data.

Lessons

• Hyperparameter Tuning
• Automated Machine Learning

Lab: Use Automated Machine Learning from the SDK

Lab: Tune Hyperparameters

After completing this module, you will be able to

• Optimize hyperparameters for model training
• Use automated machine learning to find the optimal model for your data

Module 9: Responsible Machine Learning

Data scientists have a duty to ensure they analyze data and train machine learning models responsibly, respecting individual privacy, mitigating bias, and ensuring transparency. This module explores some considerations and techniques for applying responsible machine learning principles.

Lessons

• Differential Privacy
• Model Interpretability
• Fairness

Lab: Explore Differential provacy

Lab: Interpret Models

Lab: Detect and Mitigate Unfairness

After completing this module, you will be able to

• Apply differential provacy to data analysis
• Use explainers to interpret machine learning models
• Evaluate models for fairness

Module 10: Monitoring Models

After a model has been deployed, it's important to understand how the model is being used in production, and to detect any degradation in its effectiveness due to data drift. This module describes techniques for monitoring models and their data.

Lessons

• Monitoring Models with Application Insights
• Monitoring Data Drift

Lab: Monitor Data Drift

Lab: Monitor a Model with Application Insights

After completing this module, you will be able to

• Use Application Insights to monitor a published model
• Monitor data drift

Configuring and Operating a Hybrid Cloud with Microsoft Azure Stack Course Outline | Azure Training |

This course includes the following modules:

Module 1: Overview of Azure Stack Hub

In this module, you will learn how Azure Stack Hub is an extension of Azure that provides a way to run apps in an on-premises environment and deliver Azure services in your datacenter.

Lessons

• Azure Stack Hub
• Datacenter integration
• Azure Stack Hub PowerShell
• Module review questions

After completing this module, students will be able to:

• Describe edge and disconnected solutions
• Describe Azure Stack Hub integrated systems architecture
• Explain Azure Stack Hub deployment options
• Define differences between Azure Stack Hub, Azure Stack HCI, and global Azure

Module 2: Provide Services

In this module, you will learn how to populate Azure Stack Hub Marketplace in a disconnected environment, deploy an App Services resource provider, deploy Event Hubs resource provides, create and manage quotas, plans, offers, and subscriptions, and manage usage and billing.

Lessons

• Manage Azure Stack Hub Marketplace
• Offer an App Services resource provider
• Offer an Event Hubs resource provider
• Offer services
• Manage usage and billing
• Module review questions
  • Lab : Manage offers and plans in Azure Stack Hub
  • Lab : Add custom Marketplace Items by using the Azure Gallery Packager
  • Lab : Validate Azure Resource Manager (ARM) Templates with Azure Stack Hub
  • Lab : Optional Lab : Implement SQL Server Resource Provider in Azure Stack Hub

After completing this module, students will be able to:

• Create a custom Azure Stack Hub Marketplace item
• Deploy and update an App Services resource provider
• Plan an Event Hubs resource provider deployment
• Create and manage user subscriptions
• Manage usage and billing in multi-tenant and CSP scenarios

Module 3: Implement Data Center Integration

In this module, you will learn how prepare an Stack Hub deployment, recommend and validate certificates, and register in a connected and disconnected environment.

Lessons

• Prepare for Azure Stack Hub deployment
• Manage Azure Stack Hub registration
• Module review questions

After completing this module, students will be able to:

• View and retrieve usage data by using the Usage API
• Recommend a name resolution strategy
• Validate identity provider integration
• Validate certificates
• Recommend a registration mode

Module 4: Manage Identity and Access for Azure Stack Hub

In this module, you will learn how to configure the Azure Stack Hub home directory, register the guest tenant directory with Azure Stack Hub, and identify an appropriate method for access (service principal, users, groups).

Lessons

• Manage multi-tenancy
• Manage access
• Module review questions
  • Lab : Delegate Offer Management in Azure Stack Hub
  • Lab : Manage Service Principals in Azure Stack Hub

After completing this module, students will be able to:

• Register the guest tenant directory with Azure Stack Hub
• Update the guest tenant directory
• Configure access in Azure Stack Hub
• Create a custom role

Module 5: Manage the Azure Stack Hub Infrastructure

In this module, you will learn how monitor system health by using the REST API, monitor system health by using Syslog Server, collect diagnostic logs on demand by using Powershell, configure a storage target for infrastructure backup, and download and import update packages manually.

Lessons

• Manage system health
• Azure Monitor on Azure Stack Hub
• Plan and configure business continuity and disaster recovery
• Manage capacity
• Update infrastructure
• Manage Azure Stack Hub by using privileged endpoints
• Module review questions
  • Lab : Connect to Azure Stack Hub via PowerShell
  • Lab : Access the Privileged Endpoint in Azure Stack Hub
  • Lab : Manage Log Collection in Azure Stack Hub
  • Lab : Configure and manage Azure Stack Hub Storage Accounts
  • Lab : Manage Public IP Addresses in Azure Stack Hub
  • Lab : Configure Azure Stack Hub Infrastructure Backup

After completing this module, students will be able to:

• Include resource providers such as Event Hubs
• Manage field replacement or repair
• Configure storage targets for infrastructure backups
• Update Azure Stack Hub
• Unlock a support session
• Connect to a privileged endpoin
• Perform system diagnostics by using Test-AzureStack

Microsoft Azure Data Fundamentals DP900 Course Outline

Within his Microsoft training course, you will learn the following modules:

Module 1: Explore core data concepts

Students will learn about core data concepts such as common data formats, workloads, and roles, and build their foundational knowledge of cloud data services within Microsoft Azure.

Lessons

• Core data concepts
• Data roles and Services

After completing this module, students will be able to:

• Identify common data formats
• Describe options for storing data in files
• Describe options for storing data in databases
• Describe characteristics of transactional data processing solutions
• Describe characteristics of analytical data processing solutions
• Identify common data professional roles
• Identify common cloud services used by data professionals

Module 2: Explore fundamentals of relational data in Azure

Students will explore fundamental relational data concepts and relational database services in Azure.

Lessons

• Explore relational data offerings in Azure
• Explore Azure services for relational data

Lab: Provision Azure relational database services

After completing this module, students will be able to:

• Identify characteristics of relational data
• Define normalization
• Identify types of SQL statement
• Identify common relational database objects
• Identify options for Azure SQL services
• Identify options for open-source databases in Azure
• Provision a database service on Azure

Module 3: Explore fundamentals of non-relational data in Azure

Students will explore Azure storage for non-relational data and the fundamentals of Azure Cosmos DB.

Lessons

• Fundamentals of Azure Storage
• Fundamentals of Azure Cosmos DB

Lab: Explore Azure Storage

Lab: Explore Azure Cosmos DB

After completing this module, students will be able to:

• Describe features and capabilities of Azure blob storage
• Describe features and capabilities of Azure Data Lake Gen2
• Describe features and capabilities of Azure file storage
• Describe features and capabilities of Azure table storage
• Provision and use an Azure Storage account
• Describe key features and capabilities of Azure Cosmos DB
• Identify the APIs supported in Azure Cosmos DB
• Provision and use an Azure Cosmos DB instance

Module 4: Explore fundamentals of data analytics

Students will learn about large-scale data warehousing, real-time analytics, and data visualization.

Lessons

• Large-scale data warehousing
• Streaming and real-time analytics
• Data visualization

Lab: Analyze streaming data

Lab: Visualize data with Power BI

Lab: Explore Azure Synapse Analytics

After completing this module, students will be able to:

• Identify common elements of a large-scale data warehousing solution
• Describe key features for data ingestion pipelines
• Identify common types of analytical data store and related Azure services
• Provision Azure Synapse Analytics and use it to ingest, process, and query data
• Compare batch and stream processing
• Describe common elements of streaming data solutions
• Describe features and capabilities of Azure Stream Analytics
• Describe features and capabilities of Spark Structured Streaming on Azure
• Describe features and capabilities of Azure Synapse Data Explorer
• Describe a high-level process for creating reporting solutions with Microsoft Power BI
• Describe core principles of analytical data modeling
• Identify common types of data visualization and their uses
• Create an interactive report with Power BI Desktop

Implementing a Machine Learning Solution with Microsoft Azure Databricks DP090 Course Outline

Within this training course you will learn the following modules:

Module 1: Introduction to Azure Databricks

In this module, you will learn how to provision an Azure Databricks workspace and cluster and use them to work with data.

Lessons

• Getting Started with Azure Databricks
• Working with Data in Azure Databricks

Lab: Getting Started with Azure Databricks

Lab: Working with Data in Azure Databricks

After completing this module, you will be able to:

• Provision an Azure Databricks workspace and cluster
• Use Azure Databricks to work with data

Module 2: Training and Evaluating Machine Learning Models

In this module, you will learn how to use Azure Databricks to prepare data for modeling, and train and validate a machine learning model.

Lessons

• Preparing Data for Machine Learning
• Training a Machine Learning Model

Lab: Training a Machine Learning Model

Lab: Preparing Data for Machine Learning

After completing this module, you will be able to use Azure Databricks to:

• Prepare data for modeling
• Train and validate a machine learning model

Module 3: Managing Experiments and Models

In this module, you will learn how to use MLflow to track experiments running in Azure Databricks, and how to manage machine learning models.

Lessons

• Using MLflow to Track Experiments
• Managing Models

Lab: Using MLflow to Track Experiments

Lab: Managing Models

After completing this module, you will be able to:

• Use MLflow to track experiments
• Manage models

Module 4: Integrating Azure Databricks and Azure Machine Learning

In this module, you will learn how to integrate Azure Databricks with Azure Machine Learning

Lessons

• Tracking Experiments with Azure Machine Learning
• Deploying Models

Lab: Deploying Models in Azure Machine Learning

Lab: Running Experiments in Azure Machine Learning

After completing this module, you will be able to:

• Run Azure Machine Learning experiments on Azure Databricks compute
• Deploy models trained on Azure Databricks to Azure Machine Learning

Configuring and Operating Microsoft Azure Virtual Desktop AZ140​ Course Outline

Module 1: Plan an Azure Virtual Desktop Implementation 

• Azure Virtual Desktop Architecture 
• Design the Azure Virtual Desktop Architecture 
• Design for User Identities and Profiles 

Lab

• Prepare for Deployment of Azure Virtual Desktop (Azure AD DS) 
• Prepare for Deployment of Azure Virtual Desktop (AD DS) 

Module 2: Implement an Azure Virtual Desktop Infrastructure

• Implement and Manage Networking for Azure Virtual Desktop
• Implement and Manage Storage for Azure Virtual Desktop
• Create and Configure Host Pools and Session Hosts
• Create and Manage Session Host Image

Lab

• Deploy Host Pools and Session Hosts by Using the Azure Portal (AD DS) 
• Implement and Manage Storage for Azure Virtual Desktop (Azure AD DS) 
• Deploy Host Pools and Hosts by Using Azure Resource Manager Templates 
• Deploy and Manage Host Pools and Hosts by Using Powershell 
• Create and Configure Host Pools and Session Hosts (Azure AD DS) 

Module 3: Manage Access and Security 

• Manage Access
• Manage Security

Lab

• Configure Conditional Access Policies for Connections to Azure Virtual Desktop (AD DS)

Module 4: Manage User Environments and Apps

• Implement and Manage FSLogix 
• Configure User Experience Settings 
• Install and Configure Apps on a Session Host 

Lab

• Implement and Manage Azure Virtual Desktop Profiles (Azure AD DS) 
• Package Windows Azure Desktop Applications (AD DS) 

Module 5: Monitor and Maintain an Azure Virtual Desktop Infrastructure 

• Plan and Implement Business Continuity and Disaster Recovery 
• Automate Azure Virtual Desktop Management Tasks 
• Monitor and Manage Performance and Health

Lab

• Implement Autoscaling in Host Pools (AD DS) 

Designing and implementing a Microsoft Azure AI Solution AI102 Course Outline

Within this Microsoft training you will learn the following modules:

Module 1: Introduction to AI on Azure

Artificial Intelligence (AI) is increasingly at the core of modern apps and services. In this module, you'll learn about some common AI capabilities that you can leverage in your apps, and how those capabilities are implemented in Microsoft Azure. You'll also learn about some considerations for designing and implementing AI solutions responsibly.

Lessons

• Introduction to Artificial Intelligence
• Artificial Intelligence in Azure

After completing this module, students will be able to:

• Describe considerations for creating AI-enabled applications
• Identify Azure services for AI application development

Module 2: Developing AI Apps with Cognitive Services

Cognitive Services are the core building blocks for integrating AI capabilities into your apps. In this module, you'll learn how to provision, secure, monitor, and deploy cognitive services.

Lessons

• Getting Started with Cognitive Services
• Using Cognitive Services for Enterprise Applications

Lab: Get Started with Cognitive Services
Lab: Manage Cognitive Services Security
Lab: Monitor Cognitive Services
Lab: Use a Cognitive Services Container

After completing this module, students will be able to:

• Provision and consume cognitive services in Azure
• Manage cognitive services security
• Monitor cognitive services
• Use a cognitive services container

Module 3: Getting Started with Natural Language Processing

Natural Language processing (NLP) is a branch of artificial intelligence that deals with extracting insights from written or spoken language. In this module, you'll learn how to use cognitive services to analyze and translate text.

Lessons

• Analyzing Text
• Translating Text

Lab: Translate Text

Lab: Analyze Text

After completing this module, students will be able to:

• Use the Text Analytics cognitive service to analyze text
• Use the Translator cognitive service to translate text

Module 4: Building Speech-Enabled Applications

Many modern apps and services accept spoken input and can respond by synthesizing text. In this module, you'll continue your exploration of natural language processing capabilities by learning how to build speech-enabled applications.

Lessons

• Speech Recognition and Synthesis
• Speech Translation

Lab: Recognize and Synthesize Speech
Lab: Translate Speech

After completing this module, students will be able to:

• Use the Speech cognitive service to recognize and synthesize speech
• Use the Speech cognitive service to translate speech

Module 5: Creating Language Understanding Solutions

To build an application that can intelligently understand and respond to natural language input, you must define and train a model for language understanding. In this module, you'll learn how to use the Language Understanding service to create an app that can identify user intent from natural language input.

Lessons

• Creating a Language Understanding App
• Publishing and Using a Language Understanding App
• Using Language Understanding with Speech

Lab: Create a Language Understanding Client Application
Lab: Create a Language Understanding App
Lab: Use the Speech and Language Understanding Services

After completing this module, students will be able to:

• Create a Language Understanding app
• Create a client application for Language Understanding
• Integrate Language Understanding and Speech

Module 6: Building a QnA Solution

One of the most common kinds of interaction between users and AI software agents is for users to submit questions in natural language, and for the AI agent to respond intelligently with an appropriate answer. In this module, you'll explore how the QnA Maker service enables the development of this kind of solution.

Lessons

• Creating a QnA Knowledge Base
• Publishing and Using a QnA Knowledge Base

Lab: Create a QnA Solution

After completing this module, students will be able to:

• Use QnA Maker to create a knowledge base
• Use a QnA knowledge base in an app or bot

Module 7: Conversational AI and the Azure Bot Service

Bots are the basis for an increasingly common kind of AI application in which users engage in conversations with AI agents, often as they would with a human agent. In this module, you'll explore the Microsoft Bot Framework and the Azure Bot Service, which together provide a platform for creating and delivering conversational experiences.

Lessons

• Bot Basics
• Implementing a Conversational Bot

Lab: Create a Bot with the Bot Framework SDK
Lab: Create a Bot with Bot Framework Composer

After completing this module, students will be able to:

• Use the Bot Framework SDK to create a bot
• Use the Bot Framework Composer to create a bot

Module 8: Getting Started with Computer Vision

Computer vision is an area of artificial intelligence in which software applications interpret visual input from images or video. In this module, you'll start your exploration of computer vision by learning how to use cognitive services to analyze images and video.

Lessons

• Analyzing Images
• Analyzing Videos

Lab: Analyze Video
Lab: Analyze Images with Computer Vision

After completing this module, students will be able to:

• Use the Computer Vision service to analyze images
• Use Video Analyzer to analyze videos

Module 9: Developing Custom Vision Solutions

While there are many scenarios where pre-defined general computer vision capabilities can be useful, sometimes you need to train a custom model with your own visual data. In this module, you'll explore the Custom Vision service, and how to use it to create custom image classification and object detection models.

Lessons

• Image Classification
• Object Detection

Lab: Classify Images with Custom Vision
Lab: Detect Objects in Images with Custom Vision

After completing this module, students will be able to:

• Use the Custom Vision service to implement image classification
• Use the Custom Vision service to implement object detection

Module 10: Detecting, Analyzing, and Recognizing Faces

Facial detection, analysis, and recognition are common computer vision scenarios. In this module, you'll explore the user of cognitive services to identify human faces.

Lessons

• Detecting Faces with the Computer Vision Service
• Using the Face Service

Lab: Detect, Analyze, and Recognize Faces

After completing this module, students will be able to:

• Detect faces with the Computer Vision service
• Detect, analyze, and recognize faces with the Face service

Module 11: Reading Text in Images and Documents

Optical character recognition (OCR) is another common computer vision scenario, in which software extracts text from images or documents. In this module, you'll explore cognitive services that can be used to detect and read text in images, documents, and forms.

Lessons

• Reading text with the Computer Vision Service
• Extracting Information from Forms with the Form Recognizer service

Lab: Read Text in Images
Lab: Extract Data from Forms

After completing this module, students will be able to:

• Use the Computer Vision service to read text in images and documents
• Use the Form Recognizer service to extract data from digital forms

Module 12: Creating a Knowledge Mining Solution

Many AI scenarios involve intelligently searching for information based on user queries. AI-powered knowledge mining is an increasingly important way to build intelligent search solutions that use AI to extract insights from large repositories of digital data and enable users to find and analyze those insights.

Lessons

• Implementing an Intelligent Search Solution
• Developing Custom Skills for an Enrichment Pipeline
• Creating a Knowledge Store

Lab: Create a Custom Skill for Azure Cognitive Search
Lab: Create an Azure Cognitive Search solution
Lab: Create a Knowledge Store with Azure Cognitive Search

After completing this module, students will be able to:

• Create an intelligent search solution with Azure Cognitive Search
• Implement a custom skill in an Azure Cognitive Search enrichment pipeline
• Use Azure Cognitive Search to create a knowledge store

Administering Microsoft Azure SQL Solutions DP300 Course Outline

Within this Microsoft Azure training course, you will learn the following modules:

Module 1: Introduction to Azure Database Administration

This module explores the role of a database administrator in the world of Azure SQL. It also provides some foundational information relevant to the overall content. This includes a review of the various SQL Server-based options (SQL Server in a VM, SQL Managed Instance, and Azure SQL Database).

Lessons

• Prepare to maintain SQL databases on Azure

After completing this module, students will be able to:

• Understand the role of Azure Database Administrator as it fits in with other data platform roles
• Be able to describe the key differences between the SQL Server-based database options
• Describe other features for Azure SQL platforms available

Module 2: Plan and Implement Data Platform Resources

This module introduces methods for deploying data platform resources in Azure SQL. You will learn about options for both upgrading and migrating existing SQL databases to Azure. You will learn how to set up Azure resources to host SQL Server on a Virtual Machine, a SQL Managed Instance, and SQL Database. You will learn how to determine which options are best based on specific requirements including the High Availability and Disaster Recovery (HADR) needs. They will learn to calculate resource requirements and understand hybrid approaches.

Lessons

• Deploy IaaS solutions with Azure SQL
• Deploy PaaS solutions with Azure SQL
• Evaluate strategies for migrating to Azure SQL
• Migrate SQL workloads to Azure SQL Databases
• Migrate SQL workloads to Azure Managed Instances

Lab: Provision SQL Server on an Azure Virtual Machine

• Explore the Azure Portal
• Deploy a SQL Server on an Azure Virtual Machine
• Connect to SQL Server on an Azure Virtual Machine

Lab: Provision an Azure SQL Database

• Create a Virtual Network
• Deploy an Azure SQL Database
• Connect to an Azure SQL Database using Azure Data Studio
• Query an Azure SQL Database using SQL Notebook

After completing this module, students will be able to:

• Explore the basics of SQL Server in an Infrastructure as a Service (IaaS) offering
• Understand PaaS provisioning and deployment options
• Evaluate migration scenarios to SQL Managed Instance and SQL Database
• Evaluate and implement a strategy for moving a database to Azure

Module 3: Implement a Secure Environment for a Database Service

This module explores the practices of securing your SQL Server Database as well as an Azure SQL database. This includes a review of the various SQL Server-based options as well as the various Azure options for securing Azure SQL Database. Students will lean why security is crucial when working with databases and explain authentication options for Azure SQL Database.

Lessons

• Configure database authentication and authorization
• Protect data in-transit and at rest
• Implement compliance controls for sensitive data

Lab: Configure a server-based firewall rule using the Azure portal

• Configure Azure SQL Database firewall rules
• Validate access

Lab: Authorize Access to Azure SQL Database with Azure Active Directory

• Create users
• Manage access to database objects
• Validate access

Lab: Enable Microsoft Defender for SQL and Data Classification

• Enable Microsoft Defender for Azure SQL Database
• Configure Data Classification for Azure SQL Database

After completing this module, students will be able to:

• Understand the differences between Windows, SQL Server, and Azure Active Directory Authentication
• Describe and configure both data-at-rest encryption solutions as well as data-in-transit encryption
• Implement a data sensitivity solution

Module 4: Monitor and Optimize Operational Resources

This module will teach you about resource optimization for your databases created using either IaaS or PaaS services. The module also covers monitoring server and hardware resources. It will familiarize you with the various tools available for monitoring performance and establishing a baseline. You will learn how to interpret performance metrics for the most critical resources. You will also learn how to troubleshoot database performance using Azure SQL Insights.

Lessons

• Describe performance monitoring
• Configure SQL Server resources for optimal performance
• Configure databases for optimal performance

Lab: Isolate performance problems through monitoring

• Review CPU utilization in Azure portal
• Identify high CPU queries

Lab: Detect and correct fragmentation issues

• Investigate index fragmentation
• Rebuild fragmented indexes
• Validate performance improvements

After completing this module, students will be able to:

• Monitor activity and compare to a baseline
• Identify major causes of performance problems
• Configure resources for optimal performance
• Configure a user database for optimal performance

Module 5: Optimize Query Performance

Query execution plans are potentially the most important aspect of database performance. Improving bad plans is certainly an area where a small amount of effort can bring huge improvements. While hardware issues can limit query performance, improving hardware usually yields performance improvements in the 10-20% range, at most. More commonly database administrators encounter queries that are not optimized, have stale or missing statistics, have missing indexes, or poor database design choices that lead to the database engine doing more work than is necessary to return results for a given query. Improving the plans can sometimes yield performance improvements in the 100-200% range or even more, meaning that after improving a plan with better indexes or statistics, a query could run twice or three times as fast! This module provides details on how to analyze individual query performance and determine where improvements can be made.

Lessons

• Explore query performance optimization
• Explore performance-based database design
• Evaluate performance improvements

Lab: Identify database design issues

• Examine the query and identify the problem
• Identify ways to fix the warning message
• Improve the code

Lab: Identify and resolve blocking issues

• Run blocked queries report
• Enable Read Commit Snapshot isolation level
• Evaluate performance improvements

Lab: Isolate problem areas in poorly performing queries in a SQL Database

• Generate actual execution plan
• Resolve a suboptimal query plan
• Use Query Store to detect and handle regression
• Examine Top Resource Consuming Queries report
• Force a better execution plan
• Use query hints to impact performance

After completing this module, students will be able to:

• Analyze query plans and identify problem areas
• Evaluate potential query improvements using Query Store
• Review table and index design
• Determine whether query or design changes have had a positive effect

Module 6: Automate database tasks

A common goal for database administrators in many environments is to automate as many of their repetitive tasks. This can be as simple as using scripting to automate a backup process, and as complex as building a fully automated alerting system. This module provides details of automating tasks to simplify the DBA’s job. Methods include scheduling tasks for regular maintenance jobs, as well as how to use elastic jobs and Azure Automation runbooks.

Lessons

• Automate deployment of database resources
• Create and manage SQL Agent jobs
• Manage Azure PaaS tasks using automation

Lab: Deploy an automation runbook to automatically rebuild indexes

• Create an Automation Account
• Connect to an existing Azure SQL Database
• Configure Automation Account assets
• Create a PowerShell runbook
• Create a schedule for a runbook

Lab: Deploy Azure SQL Database using an Azure Resource Manager template

• Explore Azure Resource Manager template

Lab: Create a CPU status alert for a SQL Server

• Create an alert when a CPU exceeds an average of 80 percent

After completing this module, students will be able to:

• Deploy resources using automated deployment scripts
• Create scheduled tasks
• Create notifications and alerts
• Configure automation for PaaS services

Module 7: Plan and Implement a High Availability and Disaster Recovery Solution

Data must be available when the business needs it. That means the solutions hosting the data must be designed with availability and recoverability in mind. Suppose you work for a company that sells widgets both in stores and online. Your main application uses a highly transactional database for orders. What would happen if the server or platform hosting the transactional database had a problem that made it unavailable or inaccessible for some reason? What impact would it have on the business? If the right solution is put in place, the database would come online in a reasonable timeframe with minimal effort, thus allowing business to continue with little-to-no impact. This module and its associated lab cover configuring, testing, and managing a solution for high availability and disaster recovery (HADR) in Azure, for both Infrastructure-as-a-Service (IaaS) and Platform-as-a-Service (PaaS) deployments. This module will not only cover basic requirements, but also the various options available to achieve HADR.

Lessons

• High Availability and Disaster Recovery Strategies
• IaaS Platform and Database Tools for HADR
• PaaS Platform and Database Tools for HADR
• Database Backup and Recovery

Lab: Backup to URL and Restore from URL

• Create a credential
• Backup to URL
• Validate backup through Azure CLI and Storage Explorer
• Restore from URL

Lab: Configure geo-replication for Azure SQL Database

• Enable geo-replication
• Failover to a secondary region

After completing this module, students will be able to:

• The difference between recovery time and recovery point objectives
• The available HADR options for both IaaS and PaaS
• The considerations for planning and configuring HADR solutions including how backup and restore fi
• The factors that comprise a HADR strategy
• How to configure a high availability solution via a hands-on lab

Planning and Administering Microsoft Azure for SAP Workloads Course Outline | MAZ120

Module 1: Introduction to Azure for SAP Workloads

• Partnership Between Microsoft and SAP
• Introduction to Azure for SAP workloads
• How to Evaluate Microsoft Components?
• Common Terms and Meanings

Module 2: Foundations of Azure for SAP Workloads

• Contains Lessons on Azure Compute
• Azure Storage
• Azure Networking
• Identity Services
• Governance and Manageability

Module 3: SAP Certified Offerings On Azure

• General Prerequisites (SAP Support in Public Cloud Environments)
• Deployment Options of Azure for SAP Workloads
• SAP Product-Specific Support for Azure
• Operating System Support of Azure for SAP Workloads
• Storage Support of Azure for SAP Workloads
• Networking Support for SAP
• Database Support for SAP
• High Availability and Disaster Recovery Support for SAP
• Monitoring Requirements for SAP

Lab: Online Lab: Implementing Linux Clustering on Azure VMs

Lab: Online Lab: Implementing Windows Clustering on Azure VMs

Module 4: Azure for SAP Workloads Reference Architecture

• Lessons on SAP NetWeaver with AnyDB on Azure VMs
• SAP S4 HANA on Azure VMs

Module 5: Planning for Implementing SAP Solutions on Azure

• Azure VM Compute, Network, and Storage Considerations
• Azure VM high Availability and Disaster Recovery Considerations
• Azure VM Backup, Monitoring, and Security Considerations
• Azure VM Authentication and Access Control Considerations  
• Azure VM Licensing, Pricing, and Support Considerations

Module 6: Planning for Migrating SAP Workloads to Azure

• Strategies for Migrating SAP Systems to Microsoft Azure
• SAP a Workload Planning and Deployment Checklist

Module 7: Implementing Azure VM-Based SAP Solutions

• Azure VM Deployment Methodologies
• Single-Instance Implementations (2-tier and 3-tier)
• Implementing HA SAP NetWeaver with AnyDB on Azure VMs
• Implementing HA SAP HANA on Azure VMs
• Configuring Azure Enhanced Monitoring Extension for SAP
• Implementing AD and Azure AD-Based Authentication

Lab: Online Lab: Implementing SAP Architecture on Azure VMs Running Windows

Lab: Online Lab: Implementing SAP Architecture on Azure VMs Running Linux

Module 8: SAP HANA on Azure (Large Instances) (HLI)

• Foundations of SAP HANA on Azure (Large Instances) (HLI)
• HLI Certified Offerings and Sample Architecture
• HLI Planning Considerations
• HLI High Availability and Disaster Recovery Planning
• HLI Backup and Security Considerations
• HLI Licensing and Support
• Implementing, Deploying, and Managing HLI
• HLI Monitoring and Troubleshooting

Module 9: Migrating SAP Workloads to Azure

• Migration Options
• DMO Methodology
• Cloud Migration Options
• Very Large Database Migration to Azure

Module 10: Maintaining Azure for SAP Workloads

• Remote Management
• Performing Backups and Restores
• Networking Changes
• OS and Workload Updates
• Vertical and Horizontal Scaling
• Disaster Recovery

Module 11: Monitoring and Troubleshooting Azure for SAP Workloads

• Monitoring and Troubleshooting Azure VMs
• Raising Support Requests

Developing Solutions for Microsoft Azure AZ204 Course Outline

Within this training course you will learn the following modules: 

Module 1: Create Azure App Service web apps

Learn how Azure App Service functions and how to create and update an app. Explore App Service authentication and authorization, configuring app settings, scale apps, and how to use deployment slots.

Lessons

• Explore Azure App Service
• Configure web app settings
• Scale apps in Azure App Service
• Explore Azure App Service deployment slots

Module 2: Implement Azure functions

Learn how to create and deploy Azure Functions. Explore hosting options, bindings, triggers, and how to use Durable Functions to define stateful workflows.

Lessons

• Explore Azure Functions
• Develop Azure Functions
• Implement Durable Functions

Module 3: Develop solutions that use Blob storage

Learn how to create Azure Blob storage resources, manage data through the blob storage lifecycle, and work with containers and items by using the Azure Blob storage client library V12 for .NET.

Lessons

• Explore Azure Blob storage
• Manage the Azure Blob storage lifecycle
• Work with Azure Blob storage

Module 4: Develop solutions that use Azure Cosmos DB

Learn how to create Azure Cosmos DB resources with the appropriate consistency levels, choose and create a partition key, and perform data operations by using the .NET SDK V3 for Azure Cosmos DB.

Lessons

• Explore Azure Cosmos DB
• Implement partitioning in Azure Cosmos DB
• Work with Azure Cosmos DB

Module 5: Implement infrastructure as a service solution

Learn how to create and deploy virtual machine, deploy resources using Azure Resource Manager templates, and manage and deploy containers.

Lessons

• Provision virtual machines in Azure
• Create and deploy Azure Resource Manager templates
• Manage container images in Azure Container Registry
• Run container images in Azure Container Instances

Module 6: Implement user authentication and authorization

Learn how to implement authentication and authorization to resources by using the Microsoft identity platform, Microsoft Authentication Library, shared access signatures, and use Microsoft Graph.

Lessons

• Explore the Microsoft identity platform
• Implement authentication by using the Microsoft Authentication Library
• Implement shared access signatures
• Explore Microsoft Graph

Module 7: Implement secure cloud solutions

Learn how to deploy apps more securely in Azure by using Azure Key Vault, managed identities, and Azure App Configuration.

Lessons

• Implement Azure Key Vault
• Implement managed identities
• Implement Azure App Configuration

Module 8: Implement API Management

Learn how the API Management service functions, how to transform and secure APIs, and how to create a backend API.

Lessons

• Explore API Management

Module 9: Develop event-based solutions

Learn how to build applications with event-based architectures by integrating Azure Event Grid and Azure Event Hubs into your solution.

Lessons

• Explore Azure Event Grid
• Explore Azure Event Hubs

Module 10: Develop message-based solutions

Learn how to build applications with message-based architectures by integrating Azure Service Bus and Azure Queue Storage into your solution.

Lessons

• Discover Azure message queues

Module 11: Instrument solutions to support monitoring and logging

Learn how to instrument apps to enable Application Insights to monitor performance and help troubleshoot issues.

Lessons

• Monitor app performance

Module 12: Integrate caching and content delivery within solutions

Learn how to improve the performance and scalability of your applications by integrating Azure Cache for Redis and Azure Content Delivery Network into your solution.

Lessons

• Develop for Azure Cache for Redis
• Develop for storage on CDNs

Migrate NoSQL Workloads to Azure Cosmos DB (MDP060)​ Course Outline

Module 1: Building Globally Distributed Applications with Cosmos DB
Lessons

• Cosmos DB Overview
• Cosmos DB APIs
• Provisioning Throughput
• Partitioning/Sharding Best Practices

Lab: Practice Labs

• Create Cosmos DB Account
• Configure RUs

Module 2: Migrate MongoDB Workloads to Cosmos DB
Lessons

• Understand Migration Benefits
• Migration Planning
• Data Migration
• Application Migration
• Post-Migration considerations

Lab: Practice Labs

• Create a Migration Project

• Define Source and Target
• Perform Migration
• Verify Migration

Module 3: Migrate Cassandra DB Workloads to Cosmos DB

Lessons

• Understand Migration Benefits
• Migration Planning
• Data Migration
• Application Migration
• Post-Migration Considerations

Lab: Practice Labs

• Export the Schema
• Move Data Using CQLSH COPY
• Move Data Using Spark
• Verify Migration