Oversikt over relevante kurs innen IT-kurs i Gjøvik

Understand the purpose and key concepts of the Service Desk practice, including how it serves as the central point of contact between the service provider and the users, facilitating effective communication. This eLearning is: Interactive Self-paced Device-friendly 2-3 hours content mobil-optimised practical exercises     Exam: 20 questions Multiple Choice 30 minutes Closed book Minimum required score to pass: (65%)   [-]

Understand the key concepts of Relationship Management, Supplier Management, Service Level Management, Continual Improvement, and Information Security Management, elucidating their significance in fostering collaboration, ensuring service quality, driving continual improvement, and maintaining information security. This eLearning is: Interactive Self-paced   Device-friendly   12 hours content   Mobile-optimised   Practical exercises   Exam: 60 questions Multiple choise 90 minutes Closed book Minimum required score to pass: 65% [-]

Understand the purpose and key concepts of the Monitor, Support, and Fulfil practices, elucidating their importance in maintaining, supporting, and delivering IT services effectively.InteractiveOur eLearning:Self-pacedDevice-friendly12 hour contentMobile-optimised Exam:60 questionsMultiple Choice90 minutesClosed bookMinimum required score to pass: 65%  [-]

Understand the purpose and key concepts of the Plan, Implement, and Control practices, highlighting their importance in establishing, executing, and governing IT service strategies effectively. This eLearning is: Interactive Self-paced Device-friendly 2-3 hours content Mobile-optimised Practical exercises   Exam: 60 questions Multiple Choice 90 Minutes Closed book Pass Mark: 65% [-]

Understand the purpose and key concepts of Release Management, elucidating its significance in planning, scheduling, and controlling the build, test, and deployment of releases to ensure they deliver the expected outcomes. The eLearning course: Interactive Self-paced Device-friendly 2-3 hour content Mobile-optimised Practical exercises   Exam:   20 questions Multiple choise Closed book 30 minutes Minimum required score to pass: 65% [-]

Understand the purpose and key concepts of the Supplier Management Practice, elucidating its importance in managing supplier relationships and ensuring value delivery from third-party services. This eLearning is: Interactive Self-paced   Device-friendly   2-3 hours content   Mobile-optimised   Practical exercises   Exam: 20 questions Multiple choise 30 minutes Closed book Minimum required score to pass: 65% [-]

Understand the purpose and key concepts of Problem Management, including its role in identifying and managing the root causes of incidents to prevent recurrence.   This eLearning is: Interactive   Self-paced   Device-friendly   2-3 hours content   Mobile-optimised   Practical exercises   Exam: 20 questions Multiple choise 30 minutes Closed book Minimum required score to pass: 65% [-]

Kurset fokuserer på konvertering av etterspørsel til verdi via IT-relaterte tjenester. Modulen dekker sentrale emner som SLA-design, styring av flere leverandører, kommunikasjon, relasjonsstyring, CX- og UX-design, kartlegging av kunder og mer. E-læringskurset inneholder 18 timer med undervisning, og er delt inn i 8 moduler. Les mer om ITIL® 4 på  AXELOS sine websider. Du vil motta en e-post med tilgang til e-læringen, sertifiseringsvoucher og digital bok fra Peoplecert. Du avtaler tid for sertifiseringen som beskrevet i e-posten fra Peoplecert. [-]

MS-101: Microsoft 365 Mobility and Security [-]

OneNote er et program fra Microsoft som gir deg mulighet til å digitalisere dine notater. Programmet egner seg særlig for deg som har behov for å skrive møtenotater, foredragsnotater og arbeidsnotater. OneNote vil synkronisere dine notater på tvers av dine enheter, og kan benyttes på din PC, din smarttelefon eller nettbrett. Du kan bygge inn tekst og filer fra Outlook, Word, Excel og PowerPoint, samt film- og lydfiler. Har du oversikten over notater etter at møter er over? Føler du at de papirbaserte notatene over tid blir uoversiktlige og lite tilgjengelige. Du kan jobbe raskere, smartere og bedre ved å ta i bruk OneNote. I dine digitale notater i OneNote kan du inkludere tekst, bilder, lenker til filer og websider, lyd og film. Du kan ta notater fra din smarttelefon, ditt nettbrett eller din PC; alt etter hva du har tilgjengelig. Systemet vil synkronisere notatene på tvers av dine enheter. OneNote er en del av Microsoft Office og er tilgjengelig gratis for alle.  Kurset kan spesialtilpasses og holdes bedriftsinternt i deres eller våre lokaler. Deltakere må ha med egen datamaskin med relevant programvare. 6 gode grunner til å delta Du vil se hvor enkelt det er å ta raske notater Lær hvordan du finner igjen notater raskt og effektivt Du vil kunne koble notater til oppgaver i Outlook Lær å holde møtenotater koblet til avtaler og møter i Outlook Få en innføring i hvordan flere kan jobbe samtidig med notater Lær hvordan OneNote kobler lyd/videoopptak med notater Synkroniser dine notater mellom dine enheter (PC, mobil, nettbrett) Forkunnskap: Erfaring i bruk av Microsoft Office. Varighet:3 timer Pris:3200 kroner Ansatte ved UiS har egne betalingsbetingelser. [-]

COURSE OVERVIEW   This Python Programming 1 course is designed to give delegates the knowledge to develop and maintain Python scripts using the current version (V3) of Python. There are many similarities between Python V2 and Python V3. The skills gained on this course will allow the delegate to develop their own skills further using Python V2 or V3 to support the development and maintenance of scripts. The Python Programming 1 course comprises sessions dealing with syntax,variables and data types,operators and expressions,conditions and loops,functions,objects,collections,modules and packages,strings,pattern matching,exception handling,binary and text files,and databases. Exercises and examples are used throughout the course to give practical hands-on experience with the techniques covered. TARGET AUDIENCE The Python Programming 1 course course is aimed at those who want to improve their Python programming skills,and for developers/engineers who want to migrate to Python from another language,particularly those with little or no object-oriented knowledge. For those wishing to learn Python and have no previous knowledge of programming,they should look to attend our foundation course Introduction to Programming - Python. COURSE OBJECTIVES This course aims to provide the delegate with the knowledge to be able to produce Python scripts and applications that exploit all core elements of the language including variables,expressions,selection and iteration,functions,objects,collections,strings,modules,pattern matching,exception handling,I/O,and classes. COURSE CONTENT DAY 1 COURSE INTRODUCTION Administration and Course Materials Course Structure and Agenda Delegate and Trainer Introductions SESSION 1: GETTING STARTED About Python Python versions Python documentation Python runtimes Installing Python The REPL shell Python editors SESSION 2: PYTHON SCRIPTS & SYNTAX Script naming Comments Docstring Statements The backslash Code blocks Whitespace Console IO (to enable the writing of simple programs) A first Python program Script execution SESSION 3: VARIABLES & DATA TYPES Literals Identifiers Assignment Numbers (bool,int,float,complex) Binary,octal,and hexadecimal numbers Floating point accuracy Collections (str,list,tuple,set,dict) None Implicit and explicit type conversion (casting) The type function SESSION 4: OPERATORS & EXPRESSIONS Arithmetic Operators Assignment Operators Comparison Operators Logical Operators Membership Operators Bitwise Operators Identity Operators SESSION 5: CONDITIONS & LOOPS Conditional statements (if,elif,else) Nested conditional statements Short hand if/if else Python's alternative to the ternary operator Iterative statements (while,for,else) The range function Iterating over a list Break Continue Nested conditional/iterative statements COURSE CONTENTS - DAY 2 SESSION 6: FUNCTIONS Declaration Invocation Default values for parameters Named arguments args and kwargs Returning multiple values None returned Variable scope Masking and shadowing The pass keyword Recursive functions SESSION 7: OBJECTS AND CLASSES About objects Attributes and the dot notation The dir function Dunder attributes Mutability The id function Pass by reference Introduction to Classes Class Declaration and Instantiation Data attributes Methods Composition SESSION 8: LISTS About lists List syntax including slicing Getting and setting list elements Iterating over a list Checking for the presence of a value The len function List methods incl. append,insert,remove,pop,clear,copy,sort,reverse etc. The del keyword Appending to and combining lists List comprehension SESSION 9: TUPLES About tuples Tuple syntax Getting tuple elements including unpacking Iterating over a tuple Checking for the presence of a value The len function Appending to and combining tuples SESSION 10: SETS About Sets Dictionary syntax Creating,adding and removing set elements Iterating over a set Membership Testing Sorting Copying Set methods incl. union,intersection,difference,symmetric_difference etc. COURSE CONTENTS - DAY 3 SESSION 11: DICTIONARIES About dictionaries Dictionary syntax Getting and setting dictionary elements Iterating over a dictionary (keys,values,and items) Checking for the presence of a key The len function Dictionary methods incl. keys,values,items,get,pop,popitem,clear etc. The del keyword Dictionary comprehension SESSION 12: STRINGS About strings String syntax including slicing Escape characters Triple-quoted strings Concatenation Placeholders The format method Other methods e.g. endswith,find,join,lower,replace,split,startswith,strip,upper etc. A string as a list of bytes SESSION 13: MODULES & PACKAGES About modules Inbuilt modules math,random and platform the dir() and help() functions Creating and using modules the __pycache__ and the .pyc files The module search path Importing modules Namespaces Importing module objects The import wildcard Aliases Importing within a function Executable modules Reloading a module About packages Importing packaged modules Importing packaged module objects Package initialisation Subpackages Referencing objects in sibling packages The Standard Library Installing modules and packages using pip SESSION 14: PATTERN MATCHING About regular expressions Regular expression special characters Raw strings About the re module re module functions incl. match,search,findall,full match,split,sub   COURSE CONTENTS - DAY 4 SESSION 15: EXCEPTION HANDLING About exceptions and exception handling Handling exceptions (try,except,else,finally) Exception types The exception object Raising exceptions Custom exception types Built-in exceptions hierarchy SESSION 16: FILES & THE FILESYSTEM The open function Methods for seeking (seekable,seek) Methods for reading from a file (readable,read,readline,readlines) Iterating over a file Methods for writing to a file (writable,write,writelines) Introduction to context managers Text encoding schemes,codepoints,codespace ASCII and UNICODE (UTF schemes) UTF-8,binary and hexadecimal representations The ord() and chr() functions Binary files,bytes and bytearray I/O layered abstraction. About the os module os module functions incl. getcwd,listdir,mkdir,chdir,remove,rmdir etc. OSError numbers and the errno module SESSION 17: DATABASES The DB-API DP-API implementations Establishing a connection Creating a cursor Executing a query Fetching results Transactions Inserting,updating,and deleting records FOLLOW ON COURSES Python Programming 2  Data Analysis Python  Apache Web Server PHP Programming  PHP & MySQL for Web Development  PHP & MariaDB for Web Development  Perl Programming  Ruby Programming  Introduction to MySQL  Introduction to MariaDB [-]

Dykke ned i verden av råformat-bildebehandling med "Camera Raw: Komplett", ledet av Espen Faugstad hos Utdannet.no. Adobe Camera Raw, lansert i 2003, er et kraftig verktøy for import og forbedring av råformatbilder og er integrert i flere Adobe-programmer som Bridge, Photoshop, og Lightroom. Dette kurset er designet for å gi deg en grundig forståelse av Camera Raw, uavhengig av tidligere erfaring. Du vil lære hvordan du effektivt beskjærer bilder, justerer hvitbalanse, eksponering og fargemetning. Kurset dekker også hvordan du korrigerer bildeperspektiv og objektivfeil, og hvordan du anvender lokaljusteringer for å forbedre dine bilder. Espen vil guide deg gjennom de forskjellige aspektene av Camera Raw, fra grunnleggende til mer avanserte teknikker. Med praktiske prosjekter og eksempler vil du få praktisk erfaring som vil tillate deg å anvende din nye kunnskap til dine egne fotoprosjekter. Ved kursets slutt vil du ha en dyptgående forståelse av Camera Raw og være i stand til å bruke verktøyet for å forbedre dine bilder på en profesjonell måte.   Innhold: Kapittel 1: Introduksjon Kapittel 2: Beskjære Kapittel 3: Basic Kapittel 4: Detail Kapittel 5: Lens corrections Kapittel 6: Resterende Kapittel 7: Spot removal Kapittel 8: Adjustment brush Kapittel 9: Filters Kapittel 10: Prosjekter Kapittel 11: Eksportere Kapittel 12: Avslutning   Varighet: 2 timer og 50 minutter   Om Utdannet.no: Utdannet.no tilbyr noen av landets beste digitale nettkurs. Tjenesten fungerer på samme måte som strømmetjenester for musikk eller TV-serier. Våre kunder betaler en fast månedspris og får tilgang til alle kursene som er produsert så langt. Plattformen har hatt en god vekst de siste årene og kan skilte med 30.000 registrerte brukere og 1,5 millioner videoavspillinger. Vårt mål er å gjøre kompetanseutvikling moro, spennende og tilgjengelig for alle – og med oss har vi Innovasjon Norge og Forskningsrådet. [-]

AZ-700: Designing and Implementing Microsoft Azure Networking Solutions [-]

 This course teaches you to leverage your existing knowledge of Python and machine learning to manage data ingestion and preparation, model training and deployment, and machine learning solution monitoring in Microsoft Azure. TARGET AUDIENCE This course is designed for data scientists with existing knowledge of Python and machine learning frameworks like Scikit-Learn, PyTorch, and Tensorflow, who want to build and operate machine learning solutions in the cloud. COURSE CONTENT Module 1: Introduction to 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. Getting Started with Azure Machine Learning Azure Machine Learning Tools Lab : Creating an Azure Machine Learning WorkspaceLab : Working with Azure Machine Learning Tools 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: No-Code Machine Learning with Designer This module introduces the Designer tool, a drag and drop interface for creating machine learning models without writing any code. You will learn how to create a training pipeline that encapsulates data preparation and model training, and then convert that training pipeline to an inference pipeline that can be used to predict values from new data, before finally deploying the inference pipeline as a service for client applications to consume. Training Models with Designer Publishing Models with Designer Lab : Creating a Training Pipeline with the Azure ML DesignerLab : Deploying a Service with the Azure ML Designer After completing this module, you will be able to Use designer to train a machine learning model Deploy a Designer pipeline as a service Module 3: Running Experiments and Training Models In this module, you will get started with experiments that encapsulate data processing and model training code, and use them to train machine learning models. Introduction to Experiments Training and Registering Models Lab : Running ExperimentsLab : Training and Registering Models 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. Working with Datastores Working with Datasets Lab : Working with DatastoresLab : Working with Datasets After completing this module, you will be able to Create and consume datastores Create and consume datasets Module 5: Compute Contexts 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. Working with Environments Working with Compute Targets Lab : Working with EnvironmentsLab : Working with Compute Targets 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. Introduction to Pipelines Publishing and Running Pipelines Lab : Creating a PipelineLab : Publishing 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. Real-time Inferencing Batch Inferencing Lab : Creating a Real-time Inferencing ServiceLab : Creating 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 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. Hyperparameter Tuning Automated Machine Learning Lab : Tuning HyperparametersLab : Using Automated Machine Learning 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: Interpreting Models Many of the decisions made by organizations and automated systems today are based on predictions made by machine learning models. It's increasingly important to be able to understand the factors that influence the predictions made by a model, and to be able to determine any unintended biases in the model's behavior. This module describes how you can interpret models to explain how feature importance determines their predictions. Introduction to Model Interpretation using Model Explainers Lab : Reviewing Automated Machine Learning ExplanationsLab : Interpreting Models After completing this module, you will be able to Generate model explanations with automated machine learning Use explainers to interpret machine learning models 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. Monitoring Models with Application Insights Monitoring Data Drift Lab : Monitoring a Model with Application InsightsLab : Monitoring Data Drift After completing this module, you will be able to Use Application Insights to monitor a published model Monitor data drift   [-]

COURSE OVERVIEW These professionals manage and support an infrastructure that includes on-premises and Azure IaaS-hosted Windows Server-based workloads. The course teaches IT professionals how to leverage the hybrid capabilities of Azure, how to migrate virtual and physical server workloads to Azure IaaS, and how to manage and secure Azure VMs running Windows Server. The course also covers how to perform tasks related to high availability, troubleshooting, and disaster recovery. The course highlights various administrative tools and technologies including Windows Admin Center, PowerShell, Azure Arc, Azure Automation Update Management, Microsoft Defender for Identity, Azure Security Center, Azure Migrate, and Azure Monitor. TARGET AUDIENCE This four-day course is intended for Windows Server Hybrid Administrators who have experience working with Windows Server and want to extend the capabilities of their on-premises environments by combining on-premises and hybrid technologies. Windows Server Hybrid Administrators who already implement and manage on-premises core technologies want to secure and protect their environments, migrate virtual and physical workloads to Azure Iaas, enable a highly available, fully redundant environment, and perform monitoring and troubleshooting. COURSE OBJECTIVES After you complete this course you will be able to: Harden the security configuration of the Windows Server operating system environment. Enhance hybrid security using Azure Security Center, Azure Sentinel, and Windows Update Management. Apply security features to protect critical resources. Implement high availability and disaster recovery solutions. Implement recovery services in hybrid scenarios. Plan and implement hybrid and cloud-only migration, backup, and recovery scenarios. Perform upgrades and migration related to AD DS, and storage. Manage and monitor hybrid scenarios using WAC, Azure Arc, Azure Automation and Azure Monitor. Implement service monitoring and performance monitoring, and apply troubleshooting. COURSE CONTENT Module 1: Windows Server security This module discusses how to protect an Active Directory environment by securing user accounts to least privilege and placing them in the Protected Users group. The module covers how to limit authentication scope and remediate potentially insecure accounts. The module also describes how to harden the security configuration of a Windows Server operating system environment. In addition, the module discusses the use of Windows Server Update Services to deploy operating system updates to computers on the network. Finally, the module covers how to secure Windows Server DNS to help protect the network name resolution infrastructure. Lessons for module 1 Secure Windows Sever user accounts Hardening Windows Server Windows Server Update Management Secure Windows Server DNS Lab : Configuring security in Windows Server Configuring Windows Defender Credential Guard Locating problematic accounts Implementing LAPS After completing module 1, students will be able to: Diagnose and remediate potential security vulnerabilities in Windows Server resources. Harden the security configuration of the Windows Server operating system environment. Deploy operating system updates to computers on a network by using Windows Server Update Services. Secure Windows Server DNS to help protect the network name resolution infrastructure. Implement DNS policies. Module 2: Implementing security solutions in hybrid scenarios This module describes how to secure on-premises Windows Server resources and Azure IaaS workloads. The module covers how to improve the network security for Windows Server infrastructure as a service (IaaS) virtual machines (VMs) and how to diagnose network security issues with those VMs. In addition, the module introduces Azure Security Center and explains how to onboard Windows Server computers to Security Center. The module also describes how to enable Azure Update Management, deploy updates, review an update assessment, and manage updates for Azure VMs. The modules explains how Adaptive application controls and BitLocker disk encryption are used to protect Windows Server IaaS VMs. Finally, the module explains how to monitor Windows Server Azure IaaS VMs for changes in files and the registry, as well as monitoring modifications made to application software. Lessons for module 2 Implement Windows Server IaaS VM network security. Audit the security of Windows Server IaaS Virtual Machines Manage Azure updates Create and implement application allowlists with adaptive application control Configure BitLocker disk encryption for Windows IaaS Virtual Machines Implement change tracking and file integrity monitoring for Windows Server IaaS VMs Lab : Using Azure Security Center in hybrid scenarios Provisioning Azure VMs running Windows Server Configuring Azure Security Center Onboarding on-premises Windows Server into Azure Security Center Verifying the hybrid capabilities of Azure Security Center Configuring Windows Server 2019 security in Azure VMs After completing module 2, students will be able to: Diagnose network security issues in Windows Server IaaS virtual machines. Onboard Windows Server computers to Azure Security Center. Deploy and manage updates for Azure VMs by enabling Azure Automation Update Management. Implement Adaptive application controls to protect Windows Server IaaS VMs. Configure Azure Disk Encryption for Windows IaaS virtual machines (VMs). Back up and recover encrypted data. Monitor Windows Server Azure IaaS VMs for changes in files and the registry. Module 3: Implementing high availability This module describes technologies and options to create a highly available Windows Server environment. The module introduces Clustered Shared Volumes for shared storage access across multiple cluster nodes. The module also highlights failover clustering, stretch clusters, and cluster sets for implementing high availability of Windows Server workloads. The module then discusses high availability provisions for Hyper-V and Windows Server VMs, such as network load balancing, live migration, and storage migration. The module also covers high availability options for shares hosted on Windows Server file servers. Finally, the module describes how to implement scaling for virtual machine scale sets and load-balanced VMs, and how to implement Azure Site Recovery. Lessons for module 3 Introduction to Cluster Shared Volumes. Implement Windows Server failover clustering. Implement high availability of Windows Server VMs. Implement Windows Server File Server high availability. Implement scale and high availability with Windows Server VMs. Lab : Implementing failover clustering Configuring iSCSI storage Configuring a failover cluster Deploying and configuring a highly available file server Validating the deployment of the highly available file server After completing module 3, students will be able to: Implement highly available storage volumes by using Clustered Share Volumes. Implement highly available Windows Server workloads using failover clustering. Describe Hyper-V VMs load balancing. Implement Hyper-V VMs live migration and Hyper-V VMs storage migration. Describe Windows Server File Server high availablity options. Implement scaling for virtual machine scale sets and load-balanced VMs. Implement Azure Site Recovery. Module 4: Disaster recovery in Windows Server This module introduces Hyper-V Replica as a business continuity and disaster recovery solution for a virtual environment. The module discusses Hyper-V Replica scenarios and use cases, and prerequisites to use it. The module also discusses how to implement Azure Site Recovery in on-premises scenarios to recover from disasters. Lessons for module 4 Implement Hyper-V Replica Protect your on-premises infrastructure from disasters with Azure Site Recovery Lab : Implementing Hyper-V Replica and Windows Server Backup Implementing Hyper-V Replica Implementing backup and restore with Windows Server Backup After completing module 4, students will be able to: Describe Hyper-V Replica, pre-requisites for its use, and its high-level architecture and components Describe Hyper-V Replica use cases and security considerations. Configure Hyper-V Replica settings, health monitoring, and failover options. Describe extended replication. Replicate, failover, and failback virtual machines and physical servers with Azure Site Recovery. Module 5: Implementing recovery services in hybrid scenarios This module covers tools and technologies for implementing disaster recovery in hybrid scenarios, whereas the previous module focus on BCDR solutions for on-premises scenarios. The module begins with Azure Backup as a service to protect files and folders before highlighting how to implementRecovery Vaults and Azure Backup Policies. The module describes how to recover Windows IaaS virtual machines, perform backup and restore of on-premises workloads, and manage Azure VM backups. The modules also covers how to provide disaster recovery for Azure infrastructure by managing and orchestrating replication, failover, and failback of Azure virtual machines with Azure Site Recovery. Lessons for module 5 Implement hybrid backup and recovery with Windows Server IaaS Protect your Azure infrastructure with Azure Site Recovery Protect your virtual machines by using Azure Backup Lab : Implementing Azure-based recovery services Implementing the lab environment Creating and configuring an Azure Site Recovery vault Implementing Hyper-V VM protection by using Azure Site Recovery vault Implementing Azure Backup After completing module 5, students will be able to: Recover Windows Server IaaS virtual machines by using Azure Backup. Use Azure Backup to help protect the data for on-premises servers and virtualized workloads. Implement Recovery Vaults and Azure Backup policies. Protect Azure VMs with Azure Site Recovery. Run a disaster recovery drill to validate protection. Failover and failback Azure virtual machines. Module 6: Upgrade and migrate in Windows Server This module discusses approaches to migrating Windows Server workloads running in earlier versions of Windows Server to more current versions. The module covers the necessary strategies needed to move domain controllers to Windows Server 2022 and describes how the Active Directory Migration Tool can consolidate domains within a forest or migrate domains to a new AD DS forest. The module also discusses the use of Storage Migration Service to migrate files and files shares from existing file servers to new servers running Windows Server 2022. Finally, the module covers how to install and use the Windows Server Migration Tools cmdlets to migrate commonly used server roles from earlier versions of Windows Server. Lessons for module 6 Active Directory Domain Services migration Migrate file server workloads using Storage Migration Service Migrate Windows Server roles Lab : Migrating Windows Server workloads to IaaS VMs Deploying AD DS domain controllers in Azure Migrating file server shares by using Storage Migration Service After completing module 6, students will be able to: Compare upgrading an AD DS forest and migrating to a new AD DS forest. Describe the Active Directory Migration Tool (ADMT). Identify the requirements and considerations for using Storage Migration Service. Describe how to migrate a server with storage migration. Use the Windows Server Migration Tools to migrate specific Windows Server roles. Module 7: Implementing migration in hybrid scenarios This module discusses approaches to migrating workloads running in Windows Server to an infrastructure as a service (IaaS) virtual machine. The module introduces using Azure Migrate to assess and migrate on-premises Windows Server instances to Microsoft Azure. The module also covers how migrate a workload running in Windows Server to an infrastructure as a service (IaaS) virtual machine (VM) and to Windows Server 2022 by using Windows Server migration tools or the Storage Migration Service. Finally, this module describes how to use the Azure Migrate App Containerization tool to containerize and migrate ASP.NET applications to Azure App Service. Lessons for module 7 Migrate on-premises Windows Server instances to Azure IaaS virtual machines Upgrade and migrate Windows Server IaaS virtual machines Containerize and migrate ASP.NET applications to Azure App Service Lab : Migrating on-premises VMs servers to IaaS VMs Implementing assessment and discovery of Hyper-V VMs using Azure Migrate Implementing migration of Hyper-V workloads using Azure Migrate After completing module 7, students will be able to: Plan a migration strategy and choose the appropriate migration tools. Perform server assessment and discovery using Azure Migrate. Migrate Windows Server workloads to Azure VM workloads using Azure Migrate. Explain how to migrate workloads using Windows Server Migration tools. Migrate file servers by using the Storage Migration Service. Discover and containerize ASP.NET applcations running on Windows. Migrate a containerized application to Azure App Service. Module 8: Server and performance monitoring in Windows Server This module introduces a range of tools to monitor the operating system and applications on a Windows Server computer as well as describing how to configure a system to optimize efficiency and to troublshoot problems. The module covers how Event Viewer provides a convenient and accessible location for observing events that occur, and how to interpret the data in the event log. The module also covers how to audit and diagnose a Windows Server environment for regulatory compliance, user activity, and troubleshooting. Finally, the module explains how to troubleshoot AD DS service failures or degraded performance, including recovery of deleted objects and the AD DS database, and how to troubleshoot hybrid authentication issues. Lessons for module 8 Monitor Windows Server performance Manage and monitor Windows Server event logs Implement Windows Server auditing and diagnostics Troubleshoot Active Directory Lab : Monitoring and troubleshooting Windows Server Establishing a performance baseline Identifying the source of a performance problem Viewing and configuring centralized event logs After completing module 8, students will be able to: Explain the fundamentals of server performance tuning. Use built-in tools in Windows Server to monitor server performance. Use Server Manager and Windows Admin Center to review event logs. Implement custom views. Configure an event subscription. Audit Windows Server events. Configure Windows Server to record diagnostic information. Recover the AD DS database and objects in AD DS. Troubleshoot AD DS replication. Troubleshoot hybrid authentication issues. Module 9: Implementing operational monitoring in hybrid scenarios This module covers using monitoring and troubleshooing tools, processes, and best practices to streamline app performance and availabilty of Windows Server IaaS VMs and hybrid instances. The module describes how to implement Azure Monitor for IaaS VMs in Azure, implement Azure Monitor in on-premises environments, and use dependency maps. The module then explains how to enable diagnostics to get data about a VM, and how to view VM metrics in Azure Metrics Explorer, and how to create a metric alert to monitor VM performance. The module then covers how to monitor VM performance by using Azure Monitor VM Insights. The module then describes various aspects of troubleshooting on premises and hybrid network connectivity, including how to diagnose common issues with DHCP, name resolution, IP configuration, and routing. Finally, the module examines how to troubleshoot configuration issues that impact connectivity to Azure-hosted Windows Server virtual machines (VMs), as well as approaches to resolve issues with VM startup, extensions, performance, storage, and encryption. Lessons for module 9 Monitor Windows Server IaaS Virtual Machines and hybrid instances Monitor the health of your Azure virtual machines by using Azure Metrics Explorer and metric alerts Monitor performance of virtual machines by using Azure Monitor VM Insights Troubleshoot on-premises and hybrid networking Troubleshoot Windows Server Virtual Machines in Azure Lab : Monitoring and troubleshooting of IaaS VMs running Windows Server Enabling Azure Monitor for virtual machines Setting up a VM with boot diagnostics Setting up a Log Analytics workspace and Azure Monitor VM Insights After completing module 9, students will be able to: Implement Azure Monitor for IaaS VMs in Azure and in on-premises environments. Implement Azure Monitor for IaaS VMs in Azure and in on-premises environments. View VM metrics in Azure Metrics Explorer. Use monitoring data to diagnose problems. Evaluate Azure Monitor Logs and configure Azure Monitor VM Insights. Configure a Log Analytics workspace. Troubleshoot on-premises connectivity and hybrid network connectivity. Troubleshoot AD DS service failures or degraded performance. Recover deleted security objects and the AD DS database. Troubleshoot hybrid authentication issues. [-]