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Duration 3 Days 18 CPD hours This course is intended for This course is for 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. This course teaches Network Engineers how to design, implement, and maintain Azure networking solutions. 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. Prerequisites Prerequisite courses (or equivalent knowledge and hands-on experience): AZ-104T00 - Microsoft Azure Administrator 1 - Introduction to Azure Virtual Networks Explore Azure Virtual Networks Configure public IP services Design name resolution for your virtual network Enable cross-virtual network connectivity with peering Implement virtual network traffic routing Configure internet access with Azure Virtual NAT 2 - Design and implement hybrid networking Design and implement Azure VPN Gateway Connect networks with Site-to-site VPN connections Connect devices to networks with Point-to-site VPN connections Connect remote resources by using Azure Virtual WANs Create a network virtual appliance (NVA) in a virtual hub 3 - Design and implement Azure ExpressRoute Explore Azure ExpressRoute Design an ExpressRoute deployment Configure peering for an ExpressRoute deployment Connect an ExpressRoute circuit to a virtual network Connect geographically dispersed networks with ExpressRoute global reach Improve data path performance between networks with ExpressRoute FastPath Troubleshoot ExpressRoute connection issues 4 - Load balance non-HTTP(S) traffic in Azure Explore load balancing Design and implement Azure load balancer using the Azure portal Explore Azure Traffic Manager 5 - Load balance HTTP(S) traffic in Azure Design Azure Application Gateway Configure Azure Application Gateway Design and configure Azure Front Door 6 - Design and implement network security Get network security recommendations with Microsoft Defender for Cloud Deploy Azure DDoS Protection by using the Azure portal Deploy Network Security Groups by using the Azure portal Design and implement Azure Firewall Secure your networks with Azure Firewall Manager Implement a Web Application Firewall on Azure Front Door 7 - Design and implement private access to Azure Services Explain virtual network service endpoints Define Private Link Service and private endpoint Integrate private endpoint with DNS Integrate your App Service with Azure virtual networks 8 - Design and implement network monitoring Monitor your networks using Azure monitor Monitor your networks using Azure network watcher

Duration 4 Days 24 CPD hours This course is intended for 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. Overview Learn how to operate machine learning solutions at cloud scale using Azure Machine Learning. 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. Learn how to operate machine learning solutions at cloud scale using Azure Machine Learning. 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 with Azure Machine Learning and MLflow. Prerequisites Creating cloud resources in Microsoft Azure. Using Python to explore and visualize data. Training and validating machine learning models using common frameworks like Scikit-Learn, PyTorch, and TensorFlow. Working with containers AI-900T00: Microsoft Azure AI Fundamentals is recommended, or the equivalent experience. 1 - Design a data ingestion strategy for machine learning projects Identify your data source and format Choose how to serve data to machine learning workflows Design a data ingestion solution 2 - Design a machine learning model training solution Identify machine learning tasks Choose a service to train a machine learning model Decide between compute options 3 - Design a model deployment solution Understand how model will be consumed Decide on real-time or batch deployment 4 - Design a machine learning operations solution Explore an MLOps architecture Design for monitoring Design for retraining 5 - Explore Azure Machine Learning workspace resources and assets Create an Azure Machine Learning workspace Identify Azure Machine Learning resources Identify Azure Machine Learning assets Train models in the workspace 6 - Explore developer tools for workspace interaction Explore the studio Explore the Python SDK Explore the CLI 7 - Make data available in Azure Machine Learning Understand URIs Create a datastore Create a data asset 8 - Work with compute targets in Azure Machine Learning Choose the appropriate compute target Create and use a compute instance Create and use a compute cluster 9 - Work with environments in Azure Machine Learning Understand environments Explore and use curated environments Create and use custom environments 10 - Find the best classification model with Automated Machine Learning Preprocess data and configure featurization Run an Automated Machine Learning experiment Evaluate and compare models 11 - Track model training in Jupyter notebooks with MLflow Configure MLflow for model tracking in notebooks Train and track models in notebooks 12 - Run a training script as a command job in Azure Machine Learning Convert a notebook to a script Run a script as a command job Use parameters in a command job 13 - Track model training with MLflow in jobs Track metrics with MLflow View metrics and evaluate models 14 - Perform hyperparameter tuning with Azure Machine Learning Define a search space Configure a sampling method Configure early termination Use a sweep job for hyperparameter tuning 15 - Run pipelines in Azure Machine Learning Create components Create a pipeline Run a pipeline job 16 - Register an MLflow model in Azure Machine Learning Log models with MLflow Understand the MLflow model format Register an MLflow model 17 - Create and explore the Responsible AI dashboard for a model in Azure Machine Learning Understand Responsible AI Create the Responsible AI dashboard Evaluate the Responsible AI dashboard 18 - Deploy a model to a managed online endpoint Explore managed online endpoints Deploy your MLflow model to a managed online endpoint Deploy a model to a managed online endpoint Test managed online endpoints 19 - Deploy a model to a batch endpoint Understand and create batch endpoints Deploy your MLflow model to a batch endpoint Deploy a custom model to a batch endpoint Invoke and troubleshoot batch endpoints

Duration 4 Days 24 CPD hours This course is intended for Software engineers concerned with building, managing and deploying AI solutions that leverage Azure AI Services, Azure AI Search, and Azure OpenAI. They are familiar with C# or Python and have knowledge on using REST-based APIs to build computer vision, language analysis, knowledge mining, intelligent search, and generative AI solutions on Azure. AI-102 Designing and Implementing an Azure AI Solution is intended for software developers wanting to build AI infused applications that leverage?Azure AI Services,?Azure AI Search, and?Azure OpenAI. The course will use C# or Python as the programming language. Prerequisites Before attending this course, students must have: Knowledge of Microsoft Azure and ability to navigate the Azure portal Knowledge of either C# or Python Familiarity with JSON and REST programming semantics Recommended course prerequisites AI-900T00: Microsoft Azure AI Fundamentals course 1 - Prepare to develop AI solutions on Azure Define artificial intelligence Understand AI-related terms Understand considerations for AI Engineers Understand considerations for responsible AI Understand capabilities of Azure Machine Learning Understand capabilities of Azure AI Services Understand capabilities of the Azure Bot Service Understand capabilities of Azure Cognitive Search 2 - Create and consume Azure AI services Provision an Azure AI services resource Identify endpoints and keys Use a REST API Use an SDK 3 - Secure Azure AI services Consider authentication Implement network security 4 - Monitor Azure AI services Monitor cost Create alerts View metrics Manage diagnostic logging 5 - Deploy Azure AI services in containers Understand containers Use Azure AI services containers 6 - Analyze images Provision an Azure AI Vision resource Analyze an image Generate a smart-cropped thumbnail 7 - Classify images Provision Azure resources for Azure AI Custom Vision Understand image classification Train an image classifier 8 - Detect, analyze, and recognize faces Identify options for face detection analysis and identification Understand considerations for face analysis Detect faces with the Azure AI Vision service Understand capabilities of the face service Compare and match detected faces Implement facial recognition 9 - Read Text in images and documents with the Azure AI Vision Service Explore Azure AI Vision options for reading text Use the Read API 10 - Analyze video Understand Azure Video Indexer capabilities Extract custom insights Use Video Analyzer widgets and APIs 11 - Analyze text with Azure AI Language Provision an Azure AI Language resource Detect language Extract key phrases Analyze sentiment Extract entities Extract linked entities 12 - Build a question answering solution Understand question answering Compare question answering to Azure AI Language understanding Create a knowledge base Implement multi-turn conversation Test and publish a knowledge base Use a knowledge base Improve question answering performance 13 - Build a conversational language understanding model Understand prebuilt capabilities of the Azure AI Language service Understand resources for building a conversational language understanding model Define intents, utterances, and entities Use patterns to differentiate similar utterances Use pre-built entity components Train, test, publish, and review a conversational language understanding model 14 - Create a custom text classification solution Understand types of classification projects Understand how to build text classification projects 15 - Create a custom named entity extraction solution Understand custom named entity recognition Label your data Train and evaluate your model 16 - Translate text with Azure AI Translator service Provision an Azure AI Translator resource Specify translation options Define custom translations 17 - Create speech-enabled apps with Azure AI services Provision an Azure resource for speech Use the Azure AI Speech to Text API Use the text to speech API Configure audio format and voices Use Speech Synthesis Markup Language 18 - Translate speech with the Azure AI Speech service Provision an Azure resource for speech translation Translate speech to text Synthesize translations 19 - Create an Azure AI Search solution Manage capacity Understand search components Understand the indexing process Search an index Apply filtering and sorting Enhance the index 20 - Create a custom skill for Azure AI Search Create a custom skill Add a custom skill to a skillset 21 - Create a knowledge store with Azure AI Search Define projections Define a knowledge store 22 - Plan an Azure AI Document Intelligence solution Understand AI Document Intelligence Plan Azure AI Document Intelligence resources Choose a model type 23 - Use prebuilt Azure AI Document Intelligence models Understand prebuilt models Use the General Document, Read, and Layout models Use financial, ID, and tax models 24 - Extract data from forms with Azure Document Intelligence What is Azure Document Intelligence? Get started with Azure Document Intelligence Train custom models Use Azure Document Intelligence models Use the Azure Document Intelligence Studio 25 - Get started with Azure OpenAI Service Access Azure OpenAI Service Use Azure OpenAI Studio Explore types of generative AI models Deploy generative AI models Use prompts to get completions from models Test models in Azure OpenAI Studio's playgrounds 26 - Build natural language solutions with Azure OpenAI Service Integrate Azure OpenAI into your app Use Azure OpenAI REST API Use Azure OpenAI SDK 27 - Apply prompt engineering with Azure OpenAI Service Understand prompt engineering Write more effective prompts Provide context to improve accuracy 28 - Generate code with Azure OpenAI Service Construct code from natural language Complete code and assist the development process Fix bugs and improve your code 29 - Generate images with Azure OpenAI Service What is DALL-E? Explore DALL-E in Azure OpenAI Studio Use the Azure OpenAI REST API to consume DALL-E models 30 - Use your own data with Azure OpenAI Service Understand how to use your own data Add your own data source Chat with your model using your own data 31 - Fundamentals of Responsible Generative AI Plan a responsible generative AI solution Identify potential harms Measure potential harms Mitigate potential harms Operate a responsible generative AI solution

Why Learn 3ds Max Basic to Advanced Training Course? Learn 3ds max design and animation with our comprehensive 3ds Max Basic to Advanced Training Course. Whether you're a beginner or an experienced user, this course is thoughtfully designed to elevate your skills. Course Link Duration: 40 hrs Method: 1-on-1, Personalized attention & Tailored content. Schedule: 1-on-1 training with flexible scheduling. Book a convenient hour, Mon to Sat, from 9 am to 7 pm. Course Details: Course Duration: 40 hours Course Syllabus: Part 1: Introduction to 3ds Max and Vray Familiarization with the 3ds Max Interface Configuring an efficient workspace in 3ds Max Seamless navigation within 3ds Max and Vray Understanding the power of Vray and its interface Part 2: 3D Modeling Precision crafting of fundamental shapes Mastering modifiers for advanced modeling Creating intricate 3D models using diverse techniques Learning the art of polygonal modeling Shaping both organic and inorganic models flawlessly Part 3: Texturing Applying and refining textures with expertise Grasping the essentials of UVW mapping Harnessing material editor to perfection Customizing materials with Vray materials Part 4: Lighting Discovering the magic of illumination Differentiating various light types Comprehending the subtleties of light properties Setting up captivating scenes with Vray lights Part 5: Animation Embarking on an animation adventure Expertly setting up keyframes and animation curves Infusing life into objects in a mesmerizing 3D space Elevating animations with Vray's prowess Part 6: Advanced Rendering with Vray Entering the realm of rendering excellence Mastering Vray Global Illumination for realism Optimizing visuals with Vray Physical Cameras Illuminating scenes with Vray HDRI Lighting Employing Vray Proxies and Render Elements for finesse Streamlining rendering workflow with Vray's techniques Part 7: Projects Showcasing skills through hands-on projects Crafting a captivating yet simple interior scene Designing an alluring exterior scene Taking on the challenge of a complex scene with multiple elements Course Requirements: A computer with 3ds Max and Vray installed (trial versions are available on Autodesk and Chaos Group websites) Basic knowledge of computer operations Enthusiasm for 3D modeling and animation Course Objectives: Upon completing the course, you will have mastered 3ds Max and Vray, enabling you to create photorealistic 3D scenes. The skills acquired will open doors to opportunities in the 3D modeling and animation industry, while also serving as a foundation for further honing your expertise. Course Overview: Photoshop Post-Production Delve into the world of Photoshop post-production through our comprehensive course. Learn essential photo editing techniques, including color correction, retouching, and compositing. Unleash your artistic potential by creating mesmerizing visual effects and stylizing your photos. Course Syllabus: Part 1: Introduction to Photoshop Post-Production Navigating the Photoshop Interface with ease Mastering the art of Layers and Layer Masks Embracing the power of Selections Understanding the basics of Color Correction Part 2: Retouching Techniques Unraveling common Retouching Tools and Techniques Removing blemishes, wrinkles, and unwanted elements Achieving remarkable results with Dodging and Burning Part 3: Compositing Embracing the art of Compositing Working seamlessly with Multiple Images Blending images flawlessly using Layer Masks Part 4: Visual Effects and Stylizing Techniques Crafting Special Effects with Layer Styles and Blending Modes Enhancing images with Filters Achieving mesmerizing effects with Color Grading and Adjustment Layers Part 5: Projects Applying your knowledge to hands-on projects Perfectly Retouching a portrait Creating an alluring composite image Fashioning a stylized masterpiece Course Requirements: A computer with Photoshop installed (trial version available on Adobe website) Basic knowledge of computer operations Passion for photo editing and manipulation

Duration 3 Days 18 CPD hours This course is intended for This course is aimed at people in senior roles including CIOs, chief digital officers and other aspiring CxOs, as well as consultants and others involved in digital transformations, service delivery and strategic delivery. Overview By the end of this course, you will understand: The internal and external factors to consider while crafting digital strategy How IT strategy differs from digital strategy and how they can be integrated Creating a digital strategy that achieves the most value from digital Implementing and sustaining digital strategy Developing and nurturing digital capabilities for continual business innovation and value co-creation This course takes you on a digital strategy journey. Its iterative, eight-step model moves from ?vision? through to ?actions? and is about creating sustainable, digital momentum. You experience the four key capabilities to develop a holistic, digital capability framework: digital leadership, managing innovation and emerging technologies, risk management and structuring a digital enterprise. This class includes an exam voucher. Prerequisites Delegates attending this course must have successfully achieved the ITIL 4 Foundation Qualification; your certificate must be presented as documentary evidence to gain admission to this course. Although there is no mandatory requirement, ideally candidates should have at least two years professional experience working in IT Service Management. 1 - ITIL GUIDING PRINCIPLES TO ALL ASPECTS OF DIGITAL AND IT STRATEGY Focus on Value Start Where You Are Progress Iteratively with Feedback Collaborate and Promote Visibility Think and Work Holistically Keep It Simple and Practical Optimise and Automate 2 - LEVERAGE DIGITAL STRATEGY TO REACT TO DIGITAL DISRUPTION Digital Technology Digital Business Digital Organisation Digitisation Digital Transformation Business Strategy and Business Models Digital and IT Strategy Products Services Relationship Between Digital, IT Strategy and Components of ITIL SVS. 3 - RELATIONSHIP BETWEEN CONCEPTS OF DIGITAL AND IT STRATEGY, SERVICE VALUE SYSTEM AND SERVICE VALUE CHAIN Environmental Analysis External Analysis: PESTLE Internal Analysis: Four Dimensions of Service Management 4 - HOW AN ORGANISATION USES DIGITAL AND IT STRATEGY TO REMAIN VIABLE IN ENVIRONMENTS How an Organisation?s Viability is Related to Agile, Resilient, Lean, Continuous and Co-Creational it is How to Analyse the VUCA Factors and Address them in a Digital and IT Strategy Organisation?s Position in a Particular Market or Industry Digital Positioning Tool to Determine Appropriate Position for a Digital Organization 5 - EXPLAIN AND COMPARE THREE LEVELS OF DIGITAL DISRUPTION Ecosystem Industry/Market Organisational Influenced factors Achieving Customer/Market Relevance Achieving Operational Excellence Internal and External Focus Balanced Approach 6 - STRATEGIC APPROACHES BY DIGITAL AND IT TO ACHIEVE CUSTOMER/MARKET RELEVANCE AND OPERATIONAL EXCELLENCE How to Apply Approaches to Achieve Customer/Market Relevance Customer Journeys Omnichannel Delivery and Support Context-Sensitive Delivery and Support Customer Analytics Customer Feedback and 360ø Approaches How to Achieve Operational Excellence in the Four Dimensions of Service Management Understand the Financial Aspects of Digital and IT Strategy in Terms of the Following Financial Policies Portfolio Optimization Funding Projects, Products and Services Balancing Cost of Innovation and Operation Charging Models Assess Strategic Approaches for Digital Organizations 7 - RISKS AND OPPORTUNITIES OF DIGITAL AND IT STRATEGY Concept of Risk Management in the Context of a Digital Organisation Context of Digital and IT Strategy Identify Risk Assess Risk Concept of Risk Posture and Show How to Determine an Acceptable Balance Between Opportunity and Risk Explain the Concept of Innovation, Including its Key Elements and Techniques Apply Techniques to Develop and Maintain a Culture of Innovation 8 - STEPS AND TECHNIQUES INVOLVED IN DEFINING AND ADVOCATING FOR DIGITAL AND IT STRATEGY How to Use Digital Readiness Assessment to Perform Gap Analysis Between an Organisation?s Current and Desired Positions Approaches for Scraping Data from Dynamic Websites How to Define and Communicate a Vision and a Strategy How to Use Business Cases to Advocate for a Digital and IT Strategy 9 - IMPLEMENTATION OF A DIGITAL AND IT STRATEGY How to Define Operating Models for Digital Organisations Major Skills Required of Leaders in Digital Organisation Apply Approaches to Strategy Coordination and Implementation: Large-Scale Transformation Incremental Transformation Mergers and Acquisitions Individual Changes Approaches to POMs (Parallel Operating Models) How to Assess Success of a Digital and IT Strategy Typical Activities of a Digital Transformation Programme

About this Training Course Managed Pressure Drilling (MPD) offers opportunities to improve safety and efficiency in drilling, and it is increasingly recognised by operators and regulators. MPD aims to cost effectively resolve chronic drilling problems that contribute to non-productive time. This 3 full-day course provides participants with an understanding of the MPD techniques and equipment. The course starts with the design requirements for an MPD operation. It covers all MPD methods, including constant bottom hole pressure and mud cap drilling operations. It also includes all of the rig types - both onshore and offshore - and MPD. MPD equipment is presented in detail and the design aspects of the well are also examined. Training Objectives This course aims to provide participants with a good understanding of the various aspects of managed pressure drilling. It also enables participants to gain knowledge of planning MPD operations, type of equipment and the equipment requirements for both onshore and offshore and MPD operations. At the end of the course, participants will be able to: Understand the variations in MPD Design MPD operations for various techniques Understand the engineering requirements of MPD Define MPD equipment requirements Understand operational and well control aspects of MPD Target Audience This course is designed for engineers, managers and service providers who are about to use MPD techniques and are trying to better understand the associated complexities and challenges surrounding MPD. Trainer Your expert course leader was born and raised in Nijmegen, Netherlands. He holds an MSc in Drilling Engineering from The Robert Gordon University in Aberdeen and has over 39 years of oilfield experience. He started in the industry back in 1977 as a Mud logger before becoming a wellsite drilling engineer in 1980 working mainly in the Middle East and West Africa. In 1988, he joined Shell in in the Southern North Sea. In 1995, he joined Smedvig in Norway and worked on a number of deepwater wells as well as on underbalanced drilling operations. In 1999, he started Leading Edge Advantage in Aberdeen and worked in coiled tubing, underbalanced and managed pressure drilling. In 2004, he joined Weatherford as Regional Engineering Manager in Singapore and developed the first ultra deepwater MPD systems. In 2010, he joined SPT Group specializing in well control and hydraulics modelling. SPT Group was later acquired by Schlumberger in 2012. At Schlumberger, he was Drilling Adviser, with a focus on well control, relief well planning and MPD. During that time, he was also an instructor for NExT teaching numerous well engineering courses. In 2015, he joined Wild Well as Engineering Manager in Asia and in February 2017, he became an independent consultant concentrating on provision of drilling engineering training. He has written and presented numerous papers and authored several chapters in the SPE Advanced Technology books. He is an active committee member and speaker on numerous SPE Workshops around the world. POST TRAINING COACHING SUPPORT (OPTIONAL) To further optimise your learning experience from our courses, we also offer individualized 'One to One' coaching support for 2 hours post training. We can help improve your competence in your chosen area of interest, based on your learning needs and available hours. This is a great opportunity to improve your capability and confidence in a particular area of expertise. It will be delivered over a secure video conference call by one of our senior trainers. They will work with you to create a tailor-made coaching program that will help you achieve your goals faster. Request for further information post training support and fees applicable Accreditions And Affliations

Excel but different. Harness this most powerful tool but on a course tailored to your business and needs. Using case studies relevant to your business and often actual files your firm uses on a daily basis we embark on a voyage of discovery. This isn't a boring "watch what I do and copy" course. This is exciting, engaging, funny and enjoyable. Delegates will learn relevant skills but also, because of the delivery methods they will gain an increased enjoyment of using Excel and will retain and implement all of the material covered. They also have access to the trainer for an unlimited amount of time after the course to continue to grow their knowledge

Duration 3 Days 18 CPD hours This three-day instructor-led course is aimed at modern device management professionals looking to manage their enterprise devices using Microsoft Intune. This course will cover Enrolment, Application Management, Endpoint Security and Windows Autopilot as well as Azure Active Directory Conditional Access and Identity Protection. The delegates will learn how to enroll devices, deploy applications and manage them to maximize user productivity and device security. 1: Introduction to Microsoft Intune Mobile Device Management Microsoft Intune Azure Active Directory AAD Identity Protection AAD Conditional Access 2: Microsoft Intune Device Management Enrolling Devices Device Compliance Device Profiles Device Updates 3: Microsoft Intune Application Management Application Management Deploying Applications Application Configuration Managing Applications Policy Sets and Guided Scenarios 4: Microsoft Intune Endpoint Security Security Baselines and tasks Antivirus Disk Encryption Firewall Atack Surface reduction Endpoint detection and response Account Protection 5: Deploying Windows with Windows Autopilot Windows Autopilot overview Preparing for Windows Autopilot deployment Deploying Windows 11 using Windows Autopilot 6: Microsoft Intune Additional and Premium Features Remote Help Tunnel for Mobile Application Management Endpoint Privilege Management Advanced Endpoint Analytics Additional course details: Nexus Humans 55399 Implementing and Managing Microsoft Intune training program is a workshop that presents an invigorating mix of sessions, lessons, and masterclasses meticulously crafted to propel your learning expedition forward. This immersive bootcamp-style experience boasts interactive lectures, hands-on labs, and collaborative hackathons, all strategically designed to fortify fundamental concepts. Guided by seasoned coaches, each session offers priceless insights and practical skills crucial for honing your expertise. Whether you're stepping into the realm of professional skills or a seasoned professional, this comprehensive course ensures you're equipped with the knowledge and prowess necessary for success. While we feel this is the best course for the 55399 Implementing and Managing Microsoft Intune course and one of our Top 10 we encourage you to read the course outline to make sure it is the right content for you. Additionally, private sessions, closed classes or dedicated events are available both live online and at our training centres in Dublin and London, as well as at your offices anywhere in the UK, Ireland or across EMEA.

Duration 3 Days 18 CPD hours This course is intended for This course is intended for IT professionals who are experienced in general Windows Server and Windows Client administration. Students should have a foundational knowledge of Windows PowerShell, which they can obtain by taking course 10961C: Automating Administration with Windows PowerShell. In addition, this course provides scripting guidance for Microsoft Azure administrators and developers who support development environments and deployment processes. Overview After completing this course, you will be able to: Create advanced functions. Use Microsoft .NET Framework and REST API in Windows PowerShell. Handle script errors. Use XML, JSON, and custom formatted data. Manage Microsoft Azure resources Analyze and debug scripts Understand Windows PowerShell workflow. This course teaches students how to automate administrative tasks using PowerShell. Students will learn crucial scripting skills such as creating advanced functions, writing controller scripts, and handling script errors. Candidates will learn how to use PowerShell when working with Microsoft Azure, SQL Server, Active Directory, IIS, Windows PowerShell Workflow, .NET resources, the REST API and XML, CSV & JSON formatted data files.This course replaces retired Microsoft course 10962. Module 1: Creating advanced functions Lesson 1: Converting a command into an advanced function Lesson 2: Creating a script module Lesson 3: Defining parameter attributes and input validation Lesson 4: Writing functions that accept pipeline input Lesson 5: Producing complex pipeline output Lesson 6: Using comment-based Help Lesson 7: Using Whatif and Confirm parameters Module 2: Using Microsoft .NET Framework and REST API in Windows PowerShell Lesson 1: Using .NET Framework in PowerShell Lesson 2: Using REST API in PowerShell Module 3: Writing controller scripts Lesson 1: Understanding controller scripts Lesson 2: Writing controller scripts with a user interface Lesson 3: Writing controller scripts that create reports Module 4: Handling script errors Lesson 1: Understanding error handling Lesson 2: Handling errors in a script Module 5: Using XML, JSON, and custom-formatted data Lesson 1: Working with XML formatted data Lesson 2: Working with JSON formatted data Lesson 3: Working with custom-formatted data Module 6: Enhancing server management with Desired State Configuration and Just Enough Administration Lesson 1: Implementing Desired State Configuration Lesson 2: Implementing Just Enough Administration Module 7: Analyzing and debugging scripts Lesson 1: Debugging in Windows PowerShell Lesson 2: Analyzing and debugging an existing script Module 8: Understanding Windows PowerShell Workflow Lesson 1: Understanding Windows PowerShell Workflows Lesson 2: Running Windows PowerShell Workflows