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Certified Business Analysis Professional™ (CBAP®) Boot Camp: Virtual In-House Training The course provides targeted exam preparation support for IIBA® Level 3 - CBAP® exam candidates, including both a BABOK® Guide Version 3.0 content review and exam preparation tutorial. The class is interactive, combining discussion, application of concepts, study tips, and a practice exam. Knowledge Check quizzes and self-assessments allow candidates to identify areas of weakness and create a custom study plan tailored to their individual needs as well as study aids to support their exam preparation after the course. The course materials include a copy of A Guide to the Business Analysis Body of Knowledge® (BABOK® Guide) Version 3.0. What you will Learn Upon completion, participants will be able to: Demonstrate familiarity with the structure and content of the IIBA® BABOK® Guide Improve their probability of passing the Level 3 - CBAP® Exam Identify their knowledge gaps through the use of module Knowledge Check quizzes Gauge their readiness for taking the exam by IIBA® BABOK® Guide Knowledge Are Foundation Concepts for IIBA® CBAP® Prep IIBA® - the Organization Business Analysis - the Profession Knowledge Check Terminology and Key Concepts IIBA®'s BABOK® Guide - the Standard Underlying Competencies BA Techniques Business Analysis Planning and Monitoring Knowledge Check Overview BAP&M Tasks BAP&M Techniques Elicitation and Collaboration Knowledge Check Overview E&C Tasks E&C Techniques Requirements Life Cycle Management Knowledge Check Overview RLCM Tasks RLCM Techniques Strategy Analysis Knowledge Check Overview SA Tasks SA Techniques Requirements Analysis and Design Definition Knowledge Check Overview RA&DD Tasks RA&DD Techniques Solution Evaluation Knowledge Check Overview SE Tasks SE Techniques Exam Preparation Practice Exam and debrief Exam Preparation Study Tips Manage Study Plan Exam Process Exam day

PMI's Authorized PMP Prep Course: Virtual In-House Training If you are taking this course, you probably have some professional exposure to the duties of a project manager, or you may be considering embarking on a career in professional project management. Your ability as a project manager to demonstrate best practices in project management-both on the job and through professional certification-is becoming the standard to compete in today's fast-paced and highly technical workplace. In this course, you will apply the generally recognized practices of project management acknowledged by the Project Management Institute (PMI)® to successfully manage projects. Project managers who have proven skills and experience can find exciting, high-visibility opportunities in a wide range of fields. This course is specifically designed to provide you with the proven, practical body of project management knowledge and skills that you need to demonstrate project management mastery on the job. Additionally, this course can be a significant part of your preparation for the Project Management Professional (PMP)® Certification Exam. The skills and knowledge you gain in this course will help you avoid making costly mistakes and increase your competitive edge in the project management profession.

ITIL® 4 Leader: Digital and IT Strategy: Virtual In-House Training The ITIL® 4 Leader: Digital and IT Strategy (DITS) is one of the two modules in the ITIL® 4 Strategic Leader (SL) certification scheme. The other module in the SL designation is the ITIL® 4 Strategist: Direct, Plan & Improve. Accredited training for the ITIL® 4 Strategic Leader modules is mandatory to enable full understanding of the core material. The ITIL ® 4 Digital and IT Strategy certification focuses on enabling business success through the creation of digital and IT strategies. The IT and Digital Strategy certification adds a new perspective to the ITIL suite and elevates the discussion around ITIL concepts to a strategic level among business leaders and aspiring leaders. The ITIL® 4 Digital and IT Strategy course is based on the ITIL® 4 Digital and IT Strategy exam specification from AXELOS. With the help of ITIL® 4 concepts and terminology, exercises, and examples included in the course, you will acquire relevant knowledge to pass the certification exam. The core learning material in the course is supported by interactive case study, discussions and activities. What You Will Learn The ITIL ® 4 Digital and IT Strategy course covers the content in relation to the following learning outcomes of the exam specification for ITIL ® 4 Digital and IT Strategy: Demonstrate the use of the ITIL guiding principles in Digital and IT Strategy decisions and activities Understand how to leverage digital strategy to react to digital disruption Understand the relationship between the concepts of Digital and IT Strategy, the service value system and the service value chain, and explain how to utilize them to create value Understand how an organization uses Digital and IT Strategy to remain viable in environments disrupted by digital technology Understand strategic approaches made possible by digital and information technology to achieve customer/market relevance and operational excellence Understand the risks and opportunities of Digital and IT Strategy Understand the steps and techniques involved in defining and advocating for a Digital and IT Strategy Understand how to implement a Digital and IT Strategy Key Concepts of Digital and IT Strategy Digital, Information, and Communication Technology Digital Transformation Services, Products, and Competitive Advantage Tiers of Strategy Business Models Operating Models Strategy and the Service Value System Opportunity and Demand Value Governance ITIL® Guiding Principles Continual Improvement ITIL® Practices What is Vision? Disruptions Vision Digital Disruptions Balanced Strategic Focus Positioning Tools for Digital Organizations Assignment 1: Digital Disruption and Digital Positioning Where Are We Now? Environmental Analysis Opportunity Analysis Digital Readiness Assessment How Do We Get There (Strategic Planning) Strategy Planning Financial Aspects of Digital and IT Strategy Business Models for Strategy Planning Portfolio Optimization How Do We Get There (Strategic Approaches) Strategic Approaches for Digital Organizations Strategic Approaches for Operational Excellence Strategic Approaches to Evolution Strategic Approaches to Social Responsibility and Sustainability Assignment 2: Strategic Approaches for Digital Organizations Take Action (Managing Strategic Initiatives) How Strategies are Implemented Coordinating Strategy and Strategic Initiatives Leading Digital Transformation Digital Leadership Assignment 3: Strategy Planning and Communication Did We Get There? (Measuring Strategy) Key Facts About Measurement Measuring a Strategy Instrumenting Strategy How Do We Keep the Momentum Going Long-Term Momentum: Ensuring Organizational Viability Short-Term Momentum: Parallel Operation Assignment: Digital Strategy in VUCA Environment Managing Innovation and Emerging Technologies Managing Innovation Formal Approach to Innovation Management Culture that Supports Innovation Approaches to Innovation Evaluating and Adopting Emerging Technology Managing Strategic Risk Risk Management Risk Identification Risk Posture Risk Treatment

SAFe® Agile Software Engineering: Virtual In-House Training The introduction of Lean-Agile and DevOps principles and practices into software engineering has sparked new skills and approaches that help organizations deliver higher-quality, software-centric solutions faster and more predictably. This workshop-oriented course explores foundational principles and practices and how continuous flow of value delivery and built-in quality are enabled by XP technical practices, Behavioral-Driven Development (BDD), and Test-Driven Development (TDD). Attendees will learn proven practices to detail, model, design, implement, verify, and validate stories in the SAFe® Continuous Delivery Pipeline, as well as the practices that build quality into code and designs. Attendees will also explore how software engineering fits into the larger solution context and understand their role in collaborating on intentional architecture and DevOps. What you will Learn To perform the role of a SAFe® Agile Software Engineer, you should be able to: Define Agile Software Engineering and the underlying values, principles, and practices Apply the Test-First principle to create alignment between tests and requirements Create shared understanding with Behavior-Driven Development (BDD) Communicate with Agile modeling Design from context for testability Build applications with code and design quality Utilize the test infrastructure for automated testing Collaborate on intentional architecture and emergent design Apply Lean-Agile principles to optimize the flow of value Create an Agile Software Engineering plan Introduction to Agile Software Engineering Connecting Principles and Practices to Built-in Quality Accelerating Flow Applying Intentional Architecture Thinking Test-First Discovering Story Details Creating a Shared Understanding with Behavior-Driven Development (BDD) Communicating with Models Building Systems with Code Quality Building Systems with Design Quality Implementing with Quality

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

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 3 Days 18 CPD hours This course is intended for The primary audience for this course is people who are moving into a database role, or whose role has expanded to include database technologies. Developers that deliver content from SQL Server databases will also benefit from this material. Overview After completing this course, you will be able to: Describe key database concepts in the context of SQL Server Describe database languages used in SQL Server Describe data modelling techniques Describe normalization and denormalization techniques Describe relationship types and effects in database design Describe the effects of database design on performance Describe commonly used database objects This course is provided as an introductory class for anyone getting started with databases. It will be useful to programmers and other IT professionals whose job roles are expanding into database management. Students will learn fundamental database concepts through demonstrations and hands-on labs on a SQL Server instance. This material updates and replaces course Microsoft course 10985 which was previously published under the same title. Module 1: Introduction to databases Introduction to Relational Databases Other Databases and Storage Data Analysis SQL Server Database Languages Module 2: Data Modeling Data Modelling Designing a Database Relationship Modeling Module 3: Normalization Fundamentals of Normalization Normal Form Denormalization Module 4: Relationships Introduction to Relationships Planning Referential Integrity Module 5: Performance Indexing Query Performance Concurrency Module 6: Database Objects Tables Views Stored Procedures, Triggers and Functions

Duration 3 Days 18 CPD hours This course is intended for This course is intended for information workers and data science professionals who seek to use database reporting and analysis tools such as Microsoft SQL Server Reporting Services, Excel, Power BI, R, SAS and other business intelligence tools, and wish to use TSQL queries to efficiently retrieve data sets from Microsoft SQL Server relational databases for use with these tools. Overview After completing this course, students will be able to: - Identify independent and dependent variables and measurement levels in their own analytical work scenarios. - Identify variables of interest in relational database tables. - Choose a data aggregation level and data set design appropriate for the intended analysis and tool. - Use TSQL SELECT queries to produce ready-to-use data sets for analysis in tools such as PowerBI, SQL Server Reporting Services, Excel, R, SAS, SPSS, and others. - Create stored procedures, views, and functions to modularize data retrieval code. This course is about writing TSQL queries for the purpose of database reporting, analysis, and business intelligence. 1 - INTRODUCTION TO TSQL FOR BUSINESS INTELLIGENCE Two Approaches to SQL Programming TSQL Data Retrieval in an Analytics / Business Intelligence Environment The Database Engine SQL Server Management Studio and the CarDeal Sample Database Identifying Variables in Tables SQL is a Declarative Language Introduction to the SELECT Query Lab 1: Introduction to TSQL for Business Intelligence 2 - TURNING TABLE COLUMNS INTO VARIABLES FOR ANALYSIS: SELECT LIST EXPRESSIONS, WHERE, AND ORDER BY Turning Columns into Variables for Analysis Column Expressions, Data Types, and Built-in Functions Column aliases Data type conversions Built-in Scalar Functions Table Aliases The WHERE clause ORDER BY Lab 1: Write queries 3 - COMBINING COLUMNS FROM MULTIPLE TABLES INTO A SINGLE DATASET: THE JOIN OPERATORS Primary Keys, Foreign Keys, and Joins Understanding Joins, Part 1: CROSS JOIN and the Full Cartesian Product Understanding Joins, Part 2: The INNER JOIN Understanding Joins, Part 3: The OUTER JOINS Understanding Joins, Part 4: Joining more than two tables Understanding Joins, Part 5: Combining INNER and OUTER JOINs Combining JOIN Operations with WHERE and ORDER BY Lab 1: Write SELECT queries 4 - CREATING AN APPROPRIATE AGGREGATION LEVEL USING GROUP BY Identifying required aggregation level and granularity Aggregate Functions GROUP BY HAVING Order of operations in SELECT queries Lab 1: Write queries 5 - SUBQUERIES, DERIVED TABLES AND COMMON TABLE EXPRESSIONS Non-correlated and correlated subqueries Derived tables Common table expressions Lab 1: Write queries 6 - ENCAPSULATING DATA RETRIEVAL LOGIC Views Table-valued functions Stored procedures Creating objects for read-access users Creating database accounts for analytical client tools Lab 1: Encapsulating Data Retrieval Logic 7 - GETTING YOUR DATASET TO THE CLIENT Connecting to SQL Server and Submitting Queries from Client Tools Connecting and running SELECT queries from: Excel PowerBI RStudio Exporting datasets to files using Results pane from SSMS The bcp utility The Import/Export Wizard Lab 1: Getting Your Dataset to the Client Additional course details: Nexus Humans 55232 Writing Analytical Queries for Business Intelligence 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 55232 Writing Analytical Queries for Business Intelligence 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 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

Duration 3 Days 18 CPD hours This course is intended for This course is intended for IT professionals who need to manage the day-to-day environment of an SCCM 2012 SP1 or newer environment. Knowledge of the workings of a standalone primary SCCM site and basic PowerShell experience is recommended. Overview Create additional site system roles on existing or new servers. Modify high level site settings. Create and modify Discovery Methods, Boundaries and Boundary Groups. Create Collections using any of the membership rules available. Delegate authority through Roles and Scopes. Install the Configuration Manager client, modify client settings and restrict access to site systems. Deploy software to clients. Deploy software updates to clients. Configure compliance settings targeted against collections. Modify settings that govern monitoring functions such as Alerts and Status Settings. Work with Task Sequences for Operating System Deployment. Write basic PowerShell scripts using cmdlets learned and scripting constructs to accomplish configuration tasks. This course provides students with the knowledge and skills needed to use PowerShell for System Center Configuration Manager (SCCM) administration. In this course, students learn how to access the PowerShell cmdlets included with SCCM 2012 R2 and use them to perform configuration tasks for a primary site. Individual cmdlets will be used in working with objects such as Boundaries, Boundary Groups, Collections, Software Deployment, Patching, Compliance Settings, OSD Task Sequences, and many others. Basic scripting will also be covered so that students can learn how to put PowerShell to use when working with large sets of objects. Prerequisites Basic Windows and Active Directory knowledge. Conceptual knowledge of Configuration Manager objects and how they interact. Basic experience performing configuration tasks in ECM using the graphical console. Experience working from a command prompt. Basic knowledge of the fundamentals of Windows PowerShell. 1 - REVIEW OF SYSTEM CENTER CONFIGURATION MANAGER CONCEPTS Architecture of an System Center 2012 Configuration Manager Installation Managing Assets Content Delivery and Management Security, Monitoring, and Remote Management 2 - MANAGING RESOURCES Implementing Discovery Organizing Resources with Collections Working with Boundaries 3 - WORKING WITH CLIENTS Installing the Configuration Manager Client Managing Client Settings Managing Client Operations Monitoring Client Status 4 - DISTRIBUTING SOFTWARE Configure the Software Distribution Components Working with Distribution Points Creating Content for Distribution Deploying Software Applications 5 - UPDATING SYSTEMS WITH WSUS AND SCCM Integrating Configuration Manager and WSUS Managing Updates through Software Update Groups Creating and Deploying Update Packages Working with Automatic Deployment Rules 6 - HOW POWERSHELL CAN MANAGE COMPLIANCE SETTINGS Creating Compliance Settings Objects Deploying and Monitor the Baseline 7 - CONFIGURING OPERATING SYSTEM DEPLOYMENT OBJECTS Preparing the OSD Environment Working with Task Sequences 8 - WORKING AT THE SITE LEVEL Modify the Site Adding Site System Roles Adding a Secondary Site 9 - SECURITY AND MONITORING Configuring Role Based Administration Implementing Endpoint Protection Configuring Monitoring Options 10 - USING POWERSHELL SCRIPTING TO AUTOMATE SCCM TASKS Review of Scripting Constructs Introduction to the Configuration Manager WMI Classes