Coverage
<< Full coverage of Microsoft’s AI900 and AI102 with Labs and Projects >>
— Describe, plan & manage AI workloads
— Describe principles of machine learning on Azure
— Describe features of computer vision, natural language processing & Generative AI workloads on Azure
— Implement Generative AI Solutions
— Implement an agentic solution
— Implement computer vision solutions
— Implement natural language processing solutions
— Implement knowledge mining, information extraction solutions and document intelligence.
AI Project Scenarios
— AI Model for Dance Classification using Microsoft Azure.
— Automated Sentiment Analysis System for Amazon HD Camera Reviews
— Detect and analyze faces
— Extracts text from images or scanned documents
— Converts printed or handwritten text into machine-readable format.
— Identify a person wearing a pink jacket and track movements.
— Automating data entry from scanned documents.
— Enhancing customer onboarding and compliance checks.
— Processing forms for financial, legal or healthcare applications.
— Customer feedback insights for e-Commerce platforms.
— Recognizing medical terms, diseases and drug names from unstructured clinical narratives.
— AI-powered employee helpdesk assistance for HR portals.
— Customer support chatbot for Banking FAQs.
— Academic knowledge base for university students.
— News headline sentiment monitor.
Labs & Exercises
— Explore Azure AI services, Explore Automated Machine Learning in Azure Machine Learning Studio
— Analyze images, Detect faces, Read text in Vision Studio
— Use Question Answering model with Language Studio
— Use Conversational Language Understanding with Language Studio
— Explore Speech Studio, Explore Text Translation, Analyze text with Language Studio
— Extract form data in Document Intelligence Studio
— Explore an Azure AI Search index (UI)
— Explore generative AI with Microsoft Copilot
— Explore Azure AI Foundry, Explore Azure AI Content Safety
— Use prebuilt Document Intelligence models, Extract Data from Forms
— Analyze Images with Azure AI Vision, Read Text in Images, Detect and Analyze Faces, Classify Images with Azure AI Vision custom model, Analyze Video, Analyze and Translate Text.
— Create a Question Answering solution
— Create a conversational language understanding app
— Recognize and Synthesize Speech, Custom text classification, Translate speech
— Integrate Azure OpenAI into your app, Utilize prompt engineering in your app, Implement Retrieval Augmented Generation (RAG) with Azure OpenAI Service, Generate code, Generate images
— Create an Azure Cognitive Search Solution, Create a Custom Skill for Azure AI Search
— Create a Knowledge Store with Azure AI Search
— Use prebuilt Document Intelligence models, Extract Data from Forms
Data Engineering in Microsoft Fabric & Azure enables users to design, build, and maintain infrastructures and systems that enable organizations to collect, store, process, and analyze large volumes of data.
Data analytics can provide valuable insights into customer behavior and preferences. By analyzing data from customer interactions, such as website visits, social media engagement, and purchase history, businesses can gain a better understanding of their target audience. The researchers and scientists can use Microsoft technologies to collect, store, process, analyze and visualize research data for various research projects. Technologies like Microsoft Fabric and Power BI helps to fulfill the needs of researchers without coding or programming knowledge.
Coverage
<< Full coverage of Microsoft’s DP900, DP600 and PL300 with Labs and Projects >>
— Describe core data concepts
— Identify considerations for relational and non relational data on Azure
— Describe an analytics workload on Azure
— Maintain a data analytics solution using Microsoft Fabric.
— Prepare and enrich data for analysis.
— Secure and maintain analytics assets
— Implement and manage semantic models
— Prepare, model, visualize and analyze the data using Power BI.
— Manage and secure Power BI.
Data Project Scenarios
— Hospital Patient Readmission Analytics – a comprehensive end-to-end healthcare data engineering project.
— HR Analytics Dashboard for Workforce Optimization
— Cloud-Based Retail Data Warehouse
— Airline Dataset Analysis using Microsoft Fabric and PowerBI.
— Customer Reviews Analysis using Microsoft Fabric and PowerBI.
— End to End Data Engineering using Fabric: Create a Lakehouse, ingest data using various methods, perform data transformation and modeling, and generate a final report.
— Use Fabric to detect and prevent fraud: Banks can use Fabric to detect suspicious spending patterns and protect their customers.
— Use fabric and forecast demand, understand customer buying patterns, optimize inventory management, and conduct targeted marketing strategies to enhance sales and customer experience.
— Utilize Power BI in Fabric to create comprehensive reports and dashboards.
— Use Azure Synapse Analytics in Fabric to forecast future trends and behaviors. For example, healthcare organizations can use Text Analytics from Azure Cognitive Services to extract insights from large case histories
— Monitoring streaming data: Leverage Fabric’s real-time analytics capabilities to monitor streaming data in industries like finance and retail. This enables quick responses to changing conditions and enhances operational efficiency
— Real-Time Intelligence: Use the streaming and query capabilities of Real-Time Intelligence to analyze data like London bike share data. Learn to stream and transform the data, run KQL queries, build a Real-Time Dashboard, and create a Power BI report
— Use Power BI and automate financial reporting by consolidating data from multiple systems into a single dashboard, covering income statements, balance sheets, and cash flows.
— Power BI Dashboard for Fleet Management: Integrate telematics data, fuel usage, and diagnostic information into dashboards for live fleet tracking and operational expense reduction.
Labs & Exercises
— Provision Azure relational database Services
— Explore Azure Storage, Azure Cosmos DB
— Explore Data Analytics and Realtime Analytics in Microsoft Fabric
— Visualize data with Power BI
— Create and ingest data with a Microsoft Fabric lakehouse
— Analyze data with Apache Spark, Use delta tables in Apache Spark
— Create and use a Dataflow Gen2 in Microsoft Fabric
— Ingest data with a pipeline
— Organize your Fabric lakehouse using a medallion architecture
— Explore Real-Time Intelligence in Fabric, Ingest real-time data with Eventstreams in Microsoft Fabric
— Work with data in a Microsoft Fabric eventhouse
— Analyze data in a data warehouse, Load data into a data warehouse in Microsoft Fabric
— Monitor a data warehouse in Microsoft Fabric, Secure a warehouse in Microsoft Fabric
— Implement deployment pipelines in Microsoft Fabric, Monitor Fabric activity in the Monitor hub, Secure data access in Microsoft Fabric
— Get Data, Load Data in Power BI Desktop, Design a Data Model in Power BI, Create DAX calculations in Power BI Desktop
— Create visual calculations in Power BI Desktop, Design and enhance a report in Power BI Desktop
— Perform data analysis in Power BI, Create and manage workspaces in Power BI service, Create a Power BI dashboard, Enforce row-level security in Power BI
Coverage
<< Full coverage of Microsoft’s AZ900, AZ204, AZ104, SC900 and AZ500 with Labs and Projects >>
- #1: Cloud Developer
- — Develop azure compute solutions and storage.
- — Implement security, monitor solutions
- #2: Cloud Administrator
- — Deploy and manage azure computer resources
- — Implement vm, virtual networking and monitoring
- #3: Security Engineer
- — Secure identity, access, networks, compute, storage, databases and all cloud resources
Project Scenarios
- #1: Cloud Developer
- — Build a backend application and deploy to Azure and implement CI/CD Pipeline using Azure DevOps or GitHub Actions. Setup application monitoring and diagnostics.
- — Ensure security for the app and include monitoring
- — Optimize the app using CDN and Azure Cache for Redis.
- — Deploy the app to Azure Container
- — Build and deploy serverless web application.
- — Setup and manage a database instance using Azure database.
- — Implement a NoSQL database solution.
- — Develop and deploy an e-commerce application using various azure services and technologies.
- — Develop a secure and efficient system for managing patient data and medical records using cloud services.
- — Create and secure a Web API. Implement API versioning and documentation.
- #2: Cloud Administrator
- — Create and manage Virtual machines and Virtual networks.
- — Implement strategies to optimize cloud resource usage and minimize costs.
- — Design and implement a cloud infrastructure architecture for a specific application or business needs.
- — Deploy and manage azure kubernetes services.
- — Setup and manage azure load balancers and application gateways.
- — Implement azure policy compliance and governance.
- — Create and manage azure storage solutions.
- — Automate azure deployments with ARM templates.
- — Setup azure monitoring and alerts.
- Manage azure subscriptions and resource groups.
- #3: Security Engineer
- — Implement multi-factor authentication to secure a product or service.
- — Secure a virtual machine, virtual network or application.
- — Enabling encryption for various cloud based services and applications.
- — Configuring logging and monitoring various services.
- — Implement identity and access management policies.
- –Implement disaster recovery and business continuity.
- — Implement application and infrastructure level security.
- — Implement security measures and compliance solutions for a cloud environment
- — Implement Azure Sentinel for Threat Detection and Response.
- — Encrypt data at rest and in transit, manage azure key vault.
Labs & Exercises
- #1: Cloud Developer
- — Build a web application on Azure platform as a service offerings
- — Create an Azure Function by using Visual Studio Code
- — Implement task processing logic by using Azure Functions
- — Create Blob storage resources by using the .NET client library
- — Retrieve Azure Storage resources and metadata by using the Azure Storage SDK for .NET
- — Construct a polyglot data solution
- — Build and run a container image by using Azure Container Registry Tasks
- — Deploy a container instance by using the Azure CLI, Deploy a container app
- — Deploy compute workloads by using images and containers
- — Implement interactive authentication by using MSAL.NET
- — Authenticate by using OpenID Connect, MSAL, and .NET SDKs
- — Access resource secrets more securely across services
- — Create a backend API
- — Create a multi-tier solution by using Azure services
- — Route custom events to web endpoint by using Azure CLI
- — Publish and subscribe to Event Grid events
- — Send and receive message from a Service Bus queue by using .NET.
- — Monitor services that are deployed to Azure
- — Connect an app to Azure Cache for Redis by using .NET Core.
- — Enhance a web application by using the Azure Content Delivery Network
- #2: Cloud Administrator
- — Manage Microsoft Entra ID Identities
- — Manage Subscriptions and RBAC
- — Manage Governance via Azure Policy
- — Manage Azure resources by Using ARM Templates
- — Implement Virtual Networks
- — Implement Intersite Connectivity
- — Implement Traffic Management
- — Manage Azure Storage
- — Manage Virtual Machines
- — Implement Web Apps
- — Implement Data Protection
- — Implement Monitoring
- #3: Security Engineer
- — Role Based Access Control
- — MFA – Conditional access – Identity protection
- — Microsoft Entra privileged identity management
- — Network and application security groups
- — Azure Firewall
- — Configuring and securing ACR and AKS
- — Securing Azure SQL Database
- — Service Endpoints and Securing Storage
- — Key Vault
- — Azure Monitor
- — Microsoft Defender for Cloud
- — Microsoft Sentinel