Oversikt over relevante kurs innen Microsoft Azure i Jämtlands län

AZ-900: Microsoft Azure Fundamentals [-]

AZ-104: Microsoft Azure Administrator [-]

COURSE OVERVIEW Students will begin by understanding the core compute and storage technologies that are used to build an analytical solution. They will then explore how to design an analytical serving layers and focus on data engineering considerations for working with source files. The students will learn how to interactively explore data stored in files in a data lake. They will learn the various ingestion techniques that can be used to load data using the Apache Spark capability found in Azure Synapse Analytics or Azure Databricks, or how to ingest using Azure Data Factory or Azure Synapse pipelines. The students will also learn the various ways they can transform the data using the same technologies that is used to ingest data. The student will spend time on the course learning how to monitor and analyze the performance of analytical system so that they can optimize the performance of data loads, or queries that are issued against the systems. They will understand the importance of implementing security to ensure that the data is protected at rest or in transit. The student will then show how the data in an analytical system can be used to create dashboards, or build predictive models in Azure Synapse Analytics. TARGET AUDIENCE The primary audience for this course is data professionals, data architects, and business intelligence professionals who want to learn about data engineering and building analytical solutions using data platform technologies that exist on Microsoft Azure. The secondary audience for this course data analysts and data scientists who work with analytical solutions built on Microsoft Azure. COURSE OBJECTIVES   Explore compute and storage options for data engineering workloads in Azure Design and Implement the serving layer Understand data engineering considerations Run interactive queries using serverless SQL pools Explore, transform, and load data into the Data Warehouse using Apache Spark Perform data Exploration and Transformation in Azure Databricks Ingest and load Data into the Data Warehouse Transform Data with Azure Data Factory or Azure Synapse Pipelines Integrate Data from Notebooks with Azure Data Factory or Azure Synapse Pipelines Optimize Query Performance with Dedicated SQL Pools in Azure Synapse Analyze and Optimize Data Warehouse Storage Support Hybrid Transactional Analytical Processing (HTAP) with Azure Synapse Link Perform end-to-end security with Azure Synapse Analytics Perform real-time Stream Processing with Stream Analytics Create a Stream Processing Solution with Event Hubs and Azure Databricks Build reports using Power BI integration with Azure Synpase Analytics Perform Integrated Machine Learning Processes in Azure Synapse Analytics COURSE CONTENT Module 1: Explore compute and storage options for data engineering workloads This module provides an overview of the Azure compute and storage technology options that are available to data engineers building analytical workloads. This module teaches ways to structure the data lake, and to optimize the files for exploration, streaming, and batch workloads. The student will learn how to organize the data lake into levels of data refinement as they transform files through batch and stream processing. Then they will learn how to create indexes on their datasets, such as CSV, JSON, and Parquet files, and use them for potential query and workload acceleration. Introduction to Azure Synapse Analytics Describe Azure Databricks Introduction to Azure Data Lake storage Describe Delta Lake architecture Work with data streams by using Azure Stream Analytics Lab 1: Explore compute and storage options for data engineering workloads Combine streaming and batch processing with a single pipeline Organize the data lake into levels of file transformation Index data lake storage for query and workload acceleration After completing module 1, students will be able to: Describe Azure Synapse Analytics Describe Azure Databricks Describe Azure Data Lake storage Describe Delta Lake architecture Describe Azure Stream Analytics Module 2: Design and implement the serving layer This module teaches how to design and implement data stores in a modern data warehouse to optimize analytical workloads. The student will learn how to design a multidimensional schema to store fact and dimension data. Then the student will learn how to populate slowly changing dimensions through incremental data loading from Azure Data Factory. Design a multidimensional schema to optimize analytical workloads Code-free transformation at scale with Azure Data Factory Populate slowly changing dimensions in Azure Synapse Analytics pipelines Lab 2: Designing and Implementing the Serving Layer Design a star schema for analytical workloads Populate slowly changing dimensions with Azure Data Factory and mapping data flows After completing module 2, students will be able to: Design a star schema for analytical workloads Populate a slowly changing dimensions with Azure Data Factory and mapping data flows Module 3: Data engineering considerations for source files This module explores data engineering considerations that are common when loading data into a modern data warehouse analytical from files stored in an Azure Data Lake, and understanding the security consideration associated with storing files stored in the data lake. Design a Modern Data Warehouse using Azure Synapse Analytics Secure a data warehouse in Azure Synapse Analytics Lab 3: Data engineering considerations Managing files in an Azure data lake Securing files stored in an Azure data lake After completing module 3, students will be able to: Design a Modern Data Warehouse using Azure Synapse Analytics Secure a data warehouse in Azure Synapse Analytics Module 4: Run interactive queries using Azure Synapse Analytics serverless SQL pools In this module, students will learn how to work with files stored in the data lake and external file sources, through T-SQL statements executed by a serverless SQL pool in Azure Synapse Analytics. Students will query Parquet files stored in a data lake, as well as CSV files stored in an external data store. Next, they will create Azure Active Directory security groups and enforce access to files in the data lake through Role-Based Access Control (RBAC) and Access Control Lists (ACLs). Explore Azure Synapse serverless SQL pools capabilities Query data in the lake using Azure Synapse serverless SQL pools Create metadata objects in Azure Synapse serverless SQL pools Secure data and manage users in Azure Synapse serverless SQL pools Lab 4: Run interactive queries using serverless SQL pools Query Parquet data with serverless SQL pools Create external tables for Parquet and CSV files Create views with serverless SQL pools Secure access to data in a data lake when using serverless SQL pools Configure data lake security using Role-Based Access Control (RBAC) and Access Control List After completing module 4, students will be able to: Understand Azure Synapse serverless SQL pools capabilities Query data in the lake using Azure Synapse serverless SQL pools Create metadata objects in Azure Synapse serverless SQL pools Secure data and manage users in Azure Synapse serverless SQL pools Module 5: Explore, transform, and load data into the Data Warehouse using Apache Spark This module teaches how to explore data stored in a data lake, transform the data, and load data into a relational data store. The student will explore Parquet and JSON files and use techniques to query and transform JSON files with hierarchical structures. Then the student will use Apache Spark to load data into the data warehouse and join Parquet data in the data lake with data in the dedicated SQL pool. Understand big data engineering with Apache Spark in Azure Synapse Analytics Ingest data with Apache Spark notebooks in Azure Synapse Analytics Transform data with DataFrames in Apache Spark Pools in Azure Synapse Analytics Integrate SQL and Apache Spark pools in Azure Synapse Analytics Lab 5: Explore, transform, and load data into the Data Warehouse using Apache Spark Perform Data Exploration in Synapse Studio Ingest data with Spark notebooks in Azure Synapse Analytics Transform data with DataFrames in Spark pools in Azure Synapse Analytics Integrate SQL and Spark pools in Azure Synapse Analytics After completing module 5, students will be able to: Describe big data engineering with Apache Spark in Azure Synapse Analytics Ingest data with Apache Spark notebooks in Azure Synapse Analytics Transform data with DataFrames in Apache Spark Pools in Azure Synapse Analytics Integrate SQL and Apache Spark pools in Azure Synapse Analytics Module 6: Data exploration and transformation in Azure Databricks This module teaches how to use various Apache Spark DataFrame methods to explore and transform data in Azure Databricks. The student will learn how to perform standard DataFrame methods to explore and transform data. They will also learn how to perform more advanced tasks, such as removing duplicate data, manipulate date/time values, rename columns, and aggregate data. Describe Azure Databricks Read and write data in Azure Databricks Work with DataFrames in Azure Databricks Work with DataFrames advanced methods in Azure Databricks Lab 6: Data Exploration and Transformation in Azure Databricks Use DataFrames in Azure Databricks to explore and filter data Cache a DataFrame for faster subsequent queries Remove duplicate data Manipulate date/time values Remove and rename DataFrame columns Aggregate data stored in a DataFrame After completing module 6, students will be able to: Describe Azure Databricks Read and write data in Azure Databricks Work with DataFrames in Azure Databricks Work with DataFrames advanced methods in Azure Databricks Module 7: Ingest and load data into the data warehouse This module teaches students how to ingest data into the data warehouse through T-SQL scripts and Synapse Analytics integration pipelines. The student will learn how to load data into Synapse dedicated SQL pools with PolyBase and COPY using T-SQL. The student will also learn how to use workload management along with a Copy activity in a Azure Synapse pipeline for petabyte-scale data ingestion. Use data loading best practices in Azure Synapse Analytics Petabyte-scale ingestion with Azure Data Factory Lab 7: Ingest and load Data into the Data Warehouse Perform petabyte-scale ingestion with Azure Synapse Pipelines Import data with PolyBase and COPY using T-SQL Use data loading best practices in Azure Synapse Analytics After completing module 7, students will be able to: Use data loading best practices in Azure Synapse Analytics Petabyte-scale ingestion with Azure Data Factory Module 8: Transform data with Azure Data Factory or Azure Synapse Pipelines This module teaches students how to build data integration pipelines to ingest from multiple data sources, transform data using mapping data flowss, and perform data movement into one or more data sinks. Data integration with Azure Data Factory or Azure Synapse Pipelines Code-free transformation at scale with Azure Data Factory or Azure Synapse Pipelines Lab 8: Transform Data with Azure Data Factory or Azure Synapse Pipelines Execute code-free transformations at scale with Azure Synapse Pipelines Create data pipeline to import poorly formatted CSV files Create Mapping Data Flows After completing module 8, students will be able to: Perform data integration with Azure Data Factory Perform code-free transformation at scale with Azure Data Factory Module 9: Orchestrate data movement and transformation in Azure Synapse Pipelines In this module, you will learn how to create linked services, and orchestrate data movement and transformation using notebooks in Azure Synapse Pipelines. Orchestrate data movement and transformation in Azure Data Factory Lab 9: Orchestrate data movement and transformation in Azure Synapse Pipelines Integrate Data from Notebooks with Azure Data Factory or Azure Synapse Pipelines After completing module 9, students will be able to: Orchestrate data movement and transformation in Azure Synapse Pipelines Module 10: Optimize query performance with dedicated SQL pools in Azure Synapse In this module, students will learn strategies to optimize data storage and processing when using dedicated SQL pools in Azure Synapse Analytics. The student will know how to use developer features, such as windowing and HyperLogLog functions, use data loading best practices, and optimize and improve query performance. Optimize data warehouse query performance in Azure Synapse Analytics Understand data warehouse developer features of Azure Synapse Analytics Lab 10: Optimize Query Performance with Dedicated SQL Pools in Azure Synapse Understand developer features of Azure Synapse Analytics Optimize data warehouse query performance in Azure Synapse Analytics Improve query performance After completing module 10, students will be able to: Optimize data warehouse query performance in Azure Synapse Analytics Understand data warehouse developer features of Azure Synapse Analytics Module 11: Analyze and Optimize Data Warehouse Storage In this module, students will learn how to analyze then optimize the data storage of the Azure Synapse dedicated SQL pools. The student will know techniques to understand table space usage and column store storage details. Next the student will know how to compare storage requirements between identical tables that use different data types. Finally, the student will observe the impact materialized views have when executed in place of complex queries and learn how to avoid extensive logging by optimizing delete operations. Analyze and optimize data warehouse storage in Azure Synapse Analytics Lab 11: Analyze and Optimize Data Warehouse Storage Check for skewed data and space usage Understand column store storage details Study the impact of materialized views Explore rules for minimally logged operations After completing module 11, students will be able to: Analyze and optimize data warehouse storage in Azure Synapse Analytics Module 12: Support Hybrid Transactional Analytical Processing (HTAP) with Azure Synapse Link In this module, students will learn how Azure Synapse Link enables seamless connectivity of an Azure Cosmos DB account to a Synapse workspace. The student will understand how to enable and configure Synapse link, then how to query the Azure Cosmos DB analytical store using Apache Spark and SQL serverless. Design hybrid transactional and analytical processing using Azure Synapse Analytics Configure Azure Synapse Link with Azure Cosmos DB Query Azure Cosmos DB with Apache Spark pools Query Azure Cosmos DB with serverless SQL pools Lab 12: Support Hybrid Transactional Analytical Processing (HTAP) with Azure Synapse Link Configure Azure Synapse Link with Azure Cosmos DB Query Azure Cosmos DB with Apache Spark for Synapse Analytics Query Azure Cosmos DB with serverless SQL pool for Azure Synapse Analytics After completing module 12, students will be able to: Design hybrid transactional and analytical processing using Azure Synapse Analytics Configure Azure Synapse Link with Azure Cosmos DB Query Azure Cosmos DB with Apache Spark for Azure Synapse Analytics Query Azure Cosmos DB with SQL serverless for Azure Synapse Analytics Module 13: End-to-end security with Azure Synapse Analytics In this module, students will learn how to secure a Synapse Analytics workspace and its supporting infrastructure. The student will observe the SQL Active Directory Admin, manage IP firewall rules, manage secrets with Azure Key Vault and access those secrets through a Key Vault linked service and pipeline activities. The student will understand how to implement column-level security, row-level security, and dynamic data masking when using dedicated SQL pools. Secure a data warehouse in Azure Synapse Analytics Configure and manage secrets in Azure Key Vault Implement compliance controls for sensitive data Lab 13: End-to-end security with Azure Synapse Analytics Secure Azure Synapse Analytics supporting infrastructure Secure the Azure Synapse Analytics workspace and managed services Secure Azure Synapse Analytics workspace data After completing module 13, students will be able to: Secure a data warehouse in Azure Synapse Analytics Configure and manage secrets in Azure Key Vault Implement compliance controls for sensitive data Module 14: Real-time Stream Processing with Stream Analytics In this module, students will learn how to process streaming data with Azure Stream Analytics. The student will ingest vehicle telemetry data into Event Hubs, then process that data in real time, using various windowing functions in Azure Stream Analytics. They will output the data to Azure Synapse Analytics. Finally, the student will learn how to scale the Stream Analytics job to increase throughput. Enable reliable messaging for Big Data applications using Azure Event Hubs Work with data streams by using Azure Stream Analytics Ingest data streams with Azure Stream Analytics Lab 14: Real-time Stream Processing with Stream Analytics Use Stream Analytics to process real-time data from Event Hubs Use Stream Analytics windowing functions to build aggregates and output to Synapse Analytics Scale the Azure Stream Analytics job to increase throughput through partitioning Repartition the stream input to optimize parallelization After completing module 14, students will be able to: Enable reliable messaging for Big Data applications using Azure Event Hubs Work with data streams by using Azure Stream Analytics Ingest data streams with Azure Stream Analytics Module 15: Create a Stream Processing Solution with Event Hubs and Azure Databricks In this module, students will learn how to ingest and process streaming data at scale with Event Hubs and Spark Structured Streaming in Azure Databricks. The student will learn the key features and uses of Structured Streaming. The student will implement sliding windows to aggregate over chunks of data and apply watermarking to remove stale data. Finally, the student will connect to Event Hubs to read and write streams. Process streaming data with Azure Databricks structured streaming Lab 15: Create a Stream Processing Solution with Event Hubs and Azure Databricks Explore key features and uses of Structured Streaming Stream data from a file and write it out to a distributed file system Use sliding windows to aggregate over chunks of data rather than all data Apply watermarking to remove stale data Connect to Event Hubs read and write streams After completing module 15, students will be able to: Process streaming data with Azure Databricks structured streaming Module 16: Build reports using Power BI integration with Azure Synpase Analytics In this module, the student will learn how to integrate Power BI with their Synapse workspace to build reports in Power BI. The student will create a new data source and Power BI report in Synapse Studio. Then the student will learn how to improve query performance with materialized views and result-set caching. Finally, the student will explore the data lake with serverless SQL pools and create visualizations against that data in Power BI. Create reports with Power BI using its integration with Azure Synapse Analytics Lab 16: Build reports using Power BI integration with Azure Synpase Analytics Integrate an Azure Synapse workspace and Power BI Optimize integration with Power BI Improve query performance with materialized views and result-set caching Visualize data with SQL serverless and create a Power BI report After completing module 16, students will be able to: Create reports with Power BI using its integration with Azure Synapse Analytics Module 17: Perform Integrated Machine Learning Processes in Azure Synapse Analytics This module explores the integrated, end-to-end Azure Machine Learning and Azure Cognitive Services experience in Azure Synapse Analytics. You will learn how to connect an Azure Synapse Analytics workspace to an Azure Machine Learning workspace using a Linked Service and then trigger an Automated ML experiment that uses data from a Spark table. You will also learn how to use trained models from Azure Machine Learning or Azure Cognitive Services to enrich data in a SQL pool table and then serve prediction results using Power BI. Use the integrated machine learning process in Azure Synapse Analytics Lab 17: Perform Integrated Machine Learning Processes in Azure Synapse Analytics Create an Azure Machine Learning linked service Trigger an Auto ML experiment using data from a Spark table Enrich data using trained models Serve prediction results using Power BI After completing module 17, students will be able to: Use the integrated machine learning process in Azure Synapse Analytics     [-]

COURSE OVERVIEW . This training collection gives you the hands-on skills to deliver virtual desktops and applications through a single virtual desktop infrastructure platform. You will build on your skills in configuring and managing VMware Horizon® 8 through a combination of lecture and hands-on labs. You learn how to configure and deploy pools of virtual machines and how to provide a customized desktop environment to end-users. Additionally, you will learn how to install and configure a virtual desktop infrastructure platform. You learn how to install and configure VMware Horizon® Connection Server™, VMware Unified Access Gateway™, how to configure a load balancer for use with Horizon, and how to establish Cloud Pod Architecture.  Product Alignment: VMware Horizon 8 V2006 TARGET AUDIENCE Operators, administrators, and architects for VMware Horizon should enroll in this course. These individuals are responsible for the creation, maintenance, and or delivery of remote and virtual desktop services. Additional duties can include the implementation, support, and administration of an organization's end-user computing infrastructure. COURSE OBJECTIVES By the end of the course, you should be able to meet the following objectives: Recognize the features and benefits of Horizon Use VMware vSphere® to create VMs to be used as desktops for Horizon Create and optimize Windows VMs to create Horizon desktops Install and configure Horizon Agent on Horizon desktop Configure and manage the VMware Horizon® Client™ systems and connect the client to a VMware Horizon desktop Configure, manage, and entitle desktop pools of full VMs Configure, manage, and entitle pools of instant-clone desktops Create and use Remote Desktop Services (RDS) desktops and application pools Monitor the Horizon environment using Horizon Console Dashboard and Horizon Help Desk Tool Identify Horizon Connection Server installation, architecture, and requirements. Describe the authentication and certification options for a Horizon environment Recognize the integration process and benefits of VMware Workspace ONE® Access™ and Horizon 8 Discuss performance and scalability options available in Horizon 8 Describe different security options for the Horizon environment COURSE CONTENT 1  Course Introduction Introductions and course logistics Course objectives 2  Introduction to VMware Horizon Recognize the features and benefits of Horizon Describe the conceptual and logical architecture of Horizon 3  Introduction to Use Case Define a use case for your virtual desktop and application infrastructure Convert customer requirements to use-case attributes 4  vSphere for Horizon 8 Explain basic virtualization concepts Use VMware vSphere® Client™ to access your vCenter Server system and VMware ESXi™ hosts Create, provision, and remove a virtual machine 5  VMware Horizon Desktops Create a Windows and a Linux virtual machine using vSphere Optimize and prepare Windows and Linux virtual machines to set up Horizon desktop VMs 6  VMware Horizon Agents Outline the configuration choices when installing Horizon Agent on Windows and Linux virtual machines Create a gold master for Windows Horizon desktops 7  VMware Horizon Pools Identify the steps to set up a template for desktop pool deployment List the steps to add desktops to the VMware Horizon® Connection Server™ inventory Compare dedicated-assignment and floating-assignment pools Outline the steps to create an automated pool Define user entitlement Explain the hierarchy of global, pool-level, and user-level policies 8  VMware Horizon Client Options Describe the different clients and their benefits Access Horizon desktop using various Horizon clients and HTML Configure integrated printing, USB redirection, and the shared folders option Configure session collaboration and media optimization for Microsoft Teams 9  Creating and Managing Instant-Clone Desktop Pools List the advantages of instant clones Explain the provisioning technology used for instant clone desktop pools Set up an automated pool of instant clones Push updated images to instant clone desktop pools 10  Creating RDS Desktop and Application Pools Explain the difference between an RDS desktop pool and an automated pool Compare and contrast an RDS session host pool, a farm, and an application pool Create an RDS desktop pool and an application pool Access RDS desktops and application from Horizon Client Use the instant clone technology to automate the build-out of RDSH farms Configure load-balancing for RDSHs on a farm 11  Monitoring VMware Horizon Monitor the status of the Horizon components using the Horizon Administrator console dashboard Monitor desktop sessions using the HelpDesk tool 12  Course Introduction Introductions and course logistics Course objectives 13  Horizon Connection Server Recognize VMware Horizon reference architecture Identify the Horizon Connection Server supported features Identify the recommended system requirements for Horizon Connection Server Configure the Horizon event database Outline the steps for the initial configuration of Horizon Connection Server Discuss the ADAM database as a critical component of Horizon Connection Server installation 14  VMware Horizon Authentication and Certificates Compare the authentication options that Horizon Connection Server supports Describe the Smartcard authentication options that Horizon Connection Server supports Outline the steps to create a Horizon administrator and custom roles Describe the roles available in a Horizon environment Explain the role that certificates play for Horizon Connection Server Install and configure certificates for Horizon Connection Server Install and configure True SSO in a Horizon environment 15  Workspace ONE Access & Virtual Application Management Recognize the features and benefits of Workspace ONE Access Recognize the Workspace ONE Access console features Explain identity management in Workspace ONE Access Explain access management in Workspace ONE Access Describe the Workspace ONE Access directory integration Describe the Workspace ONE Access directory integration Deploy virtual applications with Workspace services 16  VMware Horizon Performance and Scalability Describe the purpose of a replica connection server Explain how multiple Horizon Connection Server instances in a pod maintain synchronization Describe the 3D rendering options available in Horizon 8 List the steps to configure graphics cards for use in a Horizon environment Configure a load balancer for use in a Horizon environment Explain Horizon Cloud Pod Architecture LDAP replication and VIPA Explain Horizon Cloud Pod Architecture scalability options 17  Managing VMware Horizon Security Explain concepts relevant to secure Horizon connections Describe how to restrict Horizon connections. Discuss the benefits of using Unified Access Gateway List the two-factor authentication options that are supported by Unified Access Gateway List Unified Access Gateway firewall rules Describe the situation in which you might deploy Unified Access Gateway instances with one, two, or three network interfaces TEST CERTIFICATION VMware Certified Professional – Desktop and Mobility 2020 (VCP-DTM 2020) [-]

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.   Agenda Module 1: Cloud Concepts -Learning Objectives-Why Cloud Services?-Infrastructure-as-a-Service (IaaS), Platform-as-a-Service (PaaS) and Software-as-a-Service (SaaS)-Public, Private, and Hybrid cloud models Module 2: Core Azure Services -Core Azure architectural components-Core Azure Services and Products-Azure Solutions-Azure management tools Module 3: Security, Privacy, Compliance and Trust -Securing network connectivity in Azure-Core Azure Identity services-Security tools and features-Azure governance methodologies-Monitoring and Reporting in Azure-Privacy, Compliance and Data Protection standards in Azure Module 4: Azure Pricing and Support -Azure subscriptions-Planning and managing costs-Support options available with Azure-Service lifecycle in Azure [-]

AZ-400: Designing and Implementing Microsoft DevOps solutions [-]

AI-900: Microsoft Azure AI Fundamentals [-]

DP-900: Microsoft Azure Data Fundamentals [-]

The course combines lecture with case studies to demonstrate basic architect design principles. Successful students have experience and knowledge in IT operations, including networking, virtualization, identity, security, business continuity, disaster recovery, data platforms, and governance. Students also have experience designing and architecting solutions. COURSE OBJECTIVES Skills gained Design a governance solution. Design a compute solution. Design an application architecture. COURSE CONTENT Module 1: Design compute and application solutions In this module you will learn about governance, compute, and application architectures. Lessons of Module 1 Design for governance Design for compute solutions Design for application architectures Lab : Case studies of Module 1 After completing this module, students will be able to: Design a governance solution. Design a compute solution. Design an application architecture. Module 2: Design storage solutions In this module, you will learn about non-relational storage, relational storage, and data integration solutions. Lessons of Module 2 Design a non-relational storage solution. Design a relational storage solution. Design a data integration solution. Lab : Case studies of Module 2 After completing this module, students will be able to: Design non-relational storage solutions. Design relational storage solutions. Design a data integration solution. Module 3: Design networking and access solutions In this module you will learn about authentication and authorization, identity and access for applications, and networking solutions. Lessons of Module 3 Design authentication and authorization solutions Design networking solutions Lab : Case studies of Module 3 After completing this module, students will be able to: Design authentication and authorization solutions. Design network solutions. Module 4: Design business continuity solutions Lessons of Module 4 Design for backup and disaster recovery Design monitoring solutions Design for migrations Lab : Case studies of Module 4 After completing this module, students will be able to: Design backup and disaster recovery. Design monitoring solutions. Design for migrations. [-]

COURSE OVERVIEW  This course covers the process of designing, implementing, and managing core Azure networking infrastructure, Hybrid Networking connections, load balancing traffic, network routing, private access to Azure services, network security and monitoring. Learn how to design and implement a secure, reliable, network infrastructure in Azure and how to establish hybrid connectivity, routing, private access to Azure services, and monitoring in Azure. TARGET AUDIENCE This course is aimed at Network Engineers looking to specialize in Azure networking solutions. An Azure Network engineer designs and implements core Azure networking infrastructure, hybrid networking connections, load balance traffic, network routing, private access to Azure services, network security and monitoring. The azure network engineer will manage networking solutions for optimal performance, resiliency, scale, and security. COURSE CONTENT Module 1: Azure Virtual Networks In this module you will learn how to design and implement fundamental Azure Networking resources such as virtual networks, public and private IPs, DNS, virtual network peering, routing, and Azure Virtual NAT. Azure Virtual Networks Public IP Services Public and Private DNS Cross-VNet connectivity Virtual Network Routing Azure virtual Network NAT Lab 1: Design and implement a Virtual Network in Azure Lab 2: Configure DNS settings in Azure Lab 3: Connect Virtual Networks with Peering After completing module 1, students will be able to: Implement virtual networks Configure public IP services Configure private and public DNS zones Design and implement cross-VNET connectivity Implement virtual network routing Design and implement an Azure Virtual Network NAT   Module 2: Design and Implement Hybrid Networking In this module you will learn how to design and implement hybrid networking solutions such as Site-to-Site VPN connections, Point-to-Site VPN connections, Azure Virtual WAN and Virtual WAN hubs. Site-to-site VPN connection Point-to-Site VP connections Azure Virtual WAN Lab 4: Create and configure a local gateway Create and configure a virtual network gateway Create a Virtual WAN by using Azure Portal Design and implement a site-to-site VPN connection Design and implement a point-to-site VPN connection Design and implement authentication Design and implement Azure Virtual WAN Resources   Module 3: Design and implement Azure ExpressRoute In this module you will learn how to design and implement Azure ExpressRoute, ExpressRoute Global Reach, ExpressRoute FastPath and ExpressRoute Peering options. ExpressRoute ExpressRoute Direct ExpressRoute FastPath ExpressRoute Peering Lab 5: Create and configure ExpressRoute Design and implement Expressroute Design and implement Expressroute Direct Design and implement Expressroute FastPath   Module 4: load balancing non-HTTP(S) traffic in Azure In this module you will learn how to design and implement load balancing solutions for non-HTTP(S) traffic in Azure with Azure Load balancer and Traffic Manager. Content Delivery and Load Blancing Azure Load balancer Azure Traffic Manager Azure Monitor Network Watcher Lab 6: Create and configure a public load balancer to load balance VMs using the Azure portal Lab:7 Create a Traffic Manager Profile using the Azure portal Lab 8: Create, view, and manage metric alerts in Azure Monitor Design and implement Azure Laod Balancers Design and implement Azure Traffic Manager Monitor Networks with Azure Monitor Use Network Watcher   Module 5: Load balancing HTTP(S) traffic in Azure In this module you will learn how to design and implement load balancing solutions for HTTP(S) traffic in Azure with Azure Application gateway and Azure Front Door. Azure Application Gateway Azure Front Door Lab 9: Create a Front Door for a highly available web application using the Azure portal Lab 10: Create and Configure an Application Gateway Design and implement Azure Application Gateway Implement Azure Front Door   Module 6: Design and implement network security In this module you will learn to design and imponent network security solutions such as Azure DDoS, Azure Firewalls, Network Security Groups, and Web Application Firewall. Azure DDoS Protection Azure Firewall Network Security Groups Web Application Firewall on Azure Front Door Lab 11: Create a Virtual Network with DDoS protection plan Lab 12: Deploy and Configure Azure Firewall Lab 13: Create a Web Application Firewall policy on Azure Front Door Configure and monitor an Azure DDoS protection plan implement and manage Azure Firewall Implement network security groups Implement a web application firewall (WAF) on Azure Front Door   Module 7: Design and implement private access to Azure Services In this module you will learn to design and implement private access to Azure Services with Azure Private Link, and virtual network service endpoints. Define Azure Private Link and private endpoints Design and Configure Private Endpoints Integrate a Private Link with DNS and on-premises clients Create, configure, and provide access to Service Endpoints Configure VNET integration for App Service Lab 14: restrict network access to PaaS resources with virtual network service endpoints Lab 15: create an Azure private endpoint Define the difference between Private Link Service and private endpoints Design and configure private endpoints Explain virtual network service endpoints Design and configure access to service endpoints Integrate Private Link with DNS Integrate your App Service with Azure virtual networks   TEST CERTIFICATION This course helps to prepare for exam AZ-700 [-]

AZ-305: Microsoft Azure Architect Design [-]

Recommend solutions to minimize costs Recommend a solution for Conditional Access, including multi-factor authentication Recommend a solution for a hybrid identity including Azure AD Connect and Azure AD Connect Recommend a solution for using Azure Policy Recommend a solution that includes KeyVault Recommend a solution that includes Azure AD Managed Identities Recommend a storage access solution Design and Azure Site Recovery solution Recommend a solution for autoscaling Recommend a solution for containers Recommend a solution for network security Recommend a solution for migrating applications and VMs Recommend a solution for migration of databases  Agenda Module 1: Design for Cost Optimization -Recommend Solutions for Cost Management-Recommended Viewpoints for Minimizing Costs Module 2: Design a Solution for Logging and Monitoring -Azure Monitoring Services-Azure Monitor Module 3: Design Authentication -Recommend a Solution for Multi-Factor Authentication-Recommend a Solution for Single-Sign On (SSO)-Five Steps for Securing Identity Infrastructure-Recommend a Solution for a Hybrid Identity-Recommend a Solution for B2B Integration Module 4: Design Authorization -Infrastructure Protection-Recommend a Hierarchical Structure for Management Groups, Subscriptions and Resource Groups Module 5: Design Governance -Recommend a Solution for using Azure Policy-Recommend a Solution for using Azure Blueprint Module 6: Design Security for Applications -Recommend a Solution using KeyVault-Recommend a Solution using Azure AD Managed Identities Module 7: Design a Solution for Databases Select an Appropriate Data Platform Based on RequirementsOverview of Azure Data StorageRecommend Database Service Tier SizingDynamically Scale Azure SQL Database and Azure SQL Managed InstancesRecommend a Solution for Encrypting Data at Rest, Transmission, and In Use Module 8: Design Data Integration -Recommend a Data Flow-Recommend a Solution for Data Integration Module 9: Select an Appropriate Storage Account -Understanding Storage Tiers-Recommend a Storage Access Solution-Recommend Storage Management Tools Module 10: Design a Solution for Backup and Recovery -Recommend a Recovery Solution for Hybrid and On-Premises Workloads-Design and Azure Site Recovery Solution-Recommend a Solution for Recovery in Different Regions-Recommend a Solution for Azure Backup Management-Design a Solution for Data Archiving and Retention Module 11: Design for High Availability -Recommend a Solution for Application and Workload Redundancy-Recommend a Solution for Autoscaling-Identify Resources that Require High Availability-Identify Storage Tpes for High Availability-Recommend a Solution for Geo-Redundancy of Workloads Module 12: Design a Compute Solution -Recommend a Solution for Compute Provisioning-Determine Appropriate Compute Technologies-Recommend a Solution for Containers-Recommend a Solution for Automating Compute Management Module 13: Design a Network Solution -Recommend a Solution for Network Addressing and Name Resolution-Recommend a Solution for Network Provisioning-Recommend a Solution for Network Security-Recommend a Solution for iInternete Connectivity and On-Premises Networks,-Recommend a Solution for Automating Network Management-Recommend a Solution for Load Balancing and Rraffic Routing Module 14: Design an Application Architecture -Recommend a Microservices Architecture-Recommend an Orchestration Solution for Deployment of Applications-Recommend a Solution for API Integration Module 15: Design Migrations -Assess and On-Premises Servers and Applications for Migration-Recommend a Solution for Migrating Applications and VMs-Recommend a Solution for Migration of Databases [-]

Objectives 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 Agenda Module 1: Identity and Access -Configure Azure Active Directory for Azure workloads and subscriptions-Configure Azure AD Privileged Identity Management-Configure security for an Azure subscription Module 2: Platform Protection -Understand cloud security-Build a network-Secure network-Implement host security-Implement platform security-Implement subscription security Module 3: Security Operations -Configure security services-Configure security policies by using Azure Security Center-Manage security alerts-Respond to and remediate security issues-Create security baselines Module 4: Data and applications -Configure security policies to manage data-Configure security for data infrastructure-Configure encryption for data at rest-Understand application security-Implement security for application lifecycle-Secure applications-Configure and manage Azure Key Vault       [-]

Agenda Module 1: Implement VMs for Windows and Linux -Select Virtual Machine Size-Configure High Availability-Implement Azure Dedicated Hosts-Deploy and Configure Scale Sets-Configure Azure Disk Encryption Module 2: Automate Deployment and Configuration of Resources -Azure Resource Manager Templates-Save a Template for a VM-Evaluate Location of New Resources-Configure a Virtual Hard Disk Template-Deploy from a Template-Create and Execute an Automation Runbook Module 3: Implement Virtual Networking -Virtual Network Peering-Implement VNet Peering Module 4: Implement Load Balancing and Network Security -Implement Azure Load Balancer-Implement an Application Gateway-Understand Web Application Firewall-Implement Azure Firewall-Implement Azure Front Door-Implementing Azure Traffice Manager-Implement Network Security Groups and Application Security Grou-Implement Azure Bastion Module 5: Implement Storage Accounts -Storage Accounts-Blob Storage-Storage Security-Managing Storage-Accessing Blobs and Queues using AAD-Configure Azure Storage Firewalls and Virtual Networks Module 6: Implement Azure Active Directory -Overview of Azure Active Directory-Users and Groups-Domains and Custom Domains-Azure AD Identity Protection-Implement Conditional Access-Configure Fraud Alerts for MFA-Implement Bypass Options-Configure Trusted IPs-Configure Guest Users in Azure AD-Manage Multiple Directori Module 7: Implement and Manage Azure Governance -Create Management Groups, Subscriptions, and Resource Groups-Overview of Role-Based Access Control (RBAC)-Role-Based Access Control (RBAC) Roles-Azure AD Access Reviews-Implement and Configure an Azure Policy-Azure Blueprints Module 8: Implement and Manage Hybrid Identities -Install and Configure Azure AD Connect-Configure Password Sync and Password Writeback-Configure Azure AD Connect Health Module 9: Manage Workloads in Azure -Migrate Workloads using Azure Migrate-VMware - Agentless Migration-VMware - Agent-Based Migration-Implement Azure Backup-Azure to Azure Site Recovery-Implement Azure Update Management Module 10: Implement Cloud Infrastructure Monitoring -Azure Infrastructure Security Monitoring-Azure Monitor-Azure Workbooks-Azure Alerts-Log Analytics-Network Watcher-Azure Service Health-Monitor Azure Costs-Azure Application Insights-Unified Monitoring in Azure Module 11: Manage Security for Applications -Azure Key Vault-Azure Managed Identity Module 12: Implement an Application Infrastructure -Create and Configure Azure App Service-Create an App Service Web App for Containers-Create and Configure an App Service Plan-Configure Networking for an App Service-Create and Manage Deployment Slots-Implement Logic Apps-Implement Azure Functions Module 13: Implement Container-Based Applications -Azure Container Instances-Configure Azure Kubernetes Service Module 14: Implement NoSQL Databases -Configure Storage Account Tables-Select Appropriate CosmosDB APIs Module 15: Implement Azure SQL Databases -Configure Azure SQL Database Settings-Implement Azure SQL Database Managed Instances-High-Availability and Azure SQL Database [-]

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#, Python, or JavaScript and have knowledge on using REST-based APIs to build computer vision, language analysis, knowledge mining, intelligent search, and conversational AI solutions on Azure. COURSE OBJECTIVES After completing this course you should be able to: Describe considerations for creating AI-enabled applications Identify Azure services for AI application development Provision and consume cognitive services in Azure Manage cognitive services security Monitor cognitive services Use a cognitive services container Use the Text Analytics cognitive service to analyze text Use the Translator cognitive service to translate text Use the Speech cognitive service to recognize and synthesize speech Use the Speech cognitive service to translate speech Create a Language Understanding app Create a client application for Language Understanding Integrate Language Understanding and Speech Use QnA Maker to create a knowledge base Use a QnA knowledge base in an app or bot Use the Bot Framework SDK to create a bot Use the Bot Framework Composer to create a bot Use the Computer Vision service to analyze images Use Video Indexer to analyze videos Use the Custom Vision service to implement image classification Use the Custom Vision service to implement object detection Detect faces with the Computer Vision service Detect, analyze, and recognize faces with the Face service Use the Computer Vision service to read text in images and documents Use the Form Recognizer service to extract data from digital forms 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   COURSE CONTENT 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. Introduction to Artificial Intelligence Artificial Intelligence in Azure 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. Getting Started with Cognitive Services Using Cognitive Services for Enterprise Applications Lab: Get Started with Cognitive Services Lab: Get Started with Cognitive Services Lab: Monitor Cognitive Services Lab: 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. Analyzing Text Translating Text Lab: Analyze Text Lab: 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. Speech Recognition and Synthesis Speech Translation Lab: Recognize and Synthesize Speech Lab: 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. Creating a Language Understanding App Publishing and Using a Language Understanding App Using Language Understanding with Speech Lab: Create a Language Understanding App Lab: Create a Language Understanding Client Application Use the Speech and Language Understanding Services 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. Creating a QnA Knowledge Base Publishing and Using a QnA Knowledge Base Lab: Create a QnA Solution 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. Bot Basics Implementing a Conversational Bot Lab: Create a Bot with the Bot Framework SDK Lab: Create a Bot with a Bot Freamwork Composer 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. Analyzing Images Analyzing Videos Lab: Analyse Images with Computer Vision Lab: Analyze Images with Video Indexer 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. Image Classification Object Detection Lab: Classify Images with Custom Vision Lab: Detect Objects in Images with Custom Vision 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. Detecting Faces with the Computer Vision Service Using the Face Service Lab:Destect, Analyze and Recognize Faces 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. 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 Module 12: Creating a Knowledge Mining Solution Ultimately, 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. Implementing an Intelligent Search Solution Developing Custom Skills for an Enrichment Pipeline Creating a Knowledge Store Lab: Create and Azure Cognitive Search Solution Create a Custom Skill for Azure Cognitive Search Create a Knowledge Store with Azure Cognitive Search   TEST CERTIFICATION Recommended as preparation for the following exams: AI-102 - Designing and Implementing a Microsoft Azure AI Solution - Part of the requirements for the Microsoft Certified Azure AI Engineer Associate Certification.   HVORFOR VELGE SG PARTNER AS:  Flest kurs med Startgaranti Rimeligste kurs Beste service og personlig oppfølgning Tilgang til opptak etter endt kurs Partner med flere av verdens beste kursleverandører [-]