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Deze training is specifiek ontworpen om medewerkers de nodige kennis en vaardigheden bij te brengen om veilig en effectief om te gaan met de opslag van gevaarlijke stoffen volgens de richtlijnen van P PGS 15 is een richtlijn in Nederland die regels en eisen stelt aan de opslag van gevaarlijke stoffen. Het doel van deze richtlijn is om de veiligheid te waarborgen en risico's voor mens en milieu te minimaliseren. Wanneer je meer dan 2.500 kg aan gevaarlijke stoffen opslaat, is het volgens PGS 15 verplicht om minimaal één deskundige in dienst te hebben die kennis heeft van de juiste omgang met en opslag van deze stoffen. Dit helpt ervoor te zorgen dat alle veiligheidsprocedures correct worden gevolgd en dat eventuele incidenten snel en effectief kunnen worden aangepakt. Het doel van de training PGS 15 is om deelnemers inzicht te geven in essentiële veiligheidsaspecten bij de opslag van gevaarlijke stoffen. De training richt zich op: Omgevingsveiligheid: Minimaliseren van risico's voor milieu en omgeving. Arbeidsveiligheid: Waarborgen van veiligheid en gezondheid van werknemers. Brand- en rampenbestrijding: Voorbereiden op effectieve reacties bij noodsituaties. Deze aspecten helpen medewerkers om te voldoen aan PGS 15 en een veilige opslagomgeving te creëren.

OVERVIEW This course is an immersive, hands-on training course designed to equip IT professionals, solution architects, and business technologists with the skills to design, build, and manage intelligent AI agents using Microsoft Copilot Studio. Participants will gain practical experience creating both conversational and autonomous agents that integrate seamlessly with Microsoft 365, Power Platform, and enterprise data sources. Through guided labs, real-world examples, and advanced customization exercises, learners will develop the confidence to deploy secure, scalable Copilot agents that enhance productivity, automate workflows, and transform organizational processes. This course blends conceptual understanding with hands-on application, progressing from foundational Copilot principles to advanced orchestration, governance, and autonomous agent design.   Updated July 2026 OBJECTIVES After this course participants should be able to: Create and configure copilots using Microsoft Copilot Studio. Design conversational experiences using topics, entities, and generative AI capabilities. Integrate copilots with Microsoft 365, Power Platform, and external data sources. Implement actions and workflows to automate business processes. Configure authentication, security, and user access settings for copilots. Test, publish, and manage copilots for organizational use. Monitor copilot performance and optimize responses to improve user experience. AUDIENCE This course is designed for professionals who want to build or manage intelligent agents using Microsoft Copilot Studio, including: - Power Platform Makers and Developers who wish to extend their automation capabilities with AI-driven agents. - IT Administrators and Solution Architects responsible for deploying and governing AI solutions within Microsoft 365 environments. - Business Analysts and Citizen Developers looking to create task-specific copilots without extensive coding. - AI and Automation Specialists who want to design secure, autonomous, and scalable Copilot agents for enterprise use cases. CERTIFICATION None NEXT STEP None CONTENT Module 1 – Copilot Chat and Agents Introduce participants to Microsoft Copilot Chat and the concept of agents within the Microsoft 365 ecosystem. Learners will explore how Copilot serves as the AI interface for users and how agents extend its capabilities to perform tasks, retrieve data, and automate workflows. Understanding Microsoft Copilot Overview of Copilot in Microsoft 365 (Teams, Word, Outlook, and more) How Copilot Chat enhances productivity through conversational AI “Copilot lite” vs “Full Copilot” capabilities How Copilot integrates with organizational data and permissions Introducing Agents Definition and purpose of Copilot Agents Types of agents: Simple, Task-based, and Autonomous Example scenarios: IT Helpdesk Bot, HR Policy Assistant, Project Tracker How agents interact with Copilot Chat Deployment and Security Where agents run: Teams, web, and Power Platform environments Data governance, privacy, and compliance considerations Licensing, cost control, and telemetry tracking Lab 1: Build agents in Copilot Studio Lite Experience Module 2 – Building Agents with Copilot Studio This module provides a deep dive into the architecture, core capabilities, and authoring experience of Microsoft Copilot Studio. Participants will learn how Copilot Studio operates within the Microsoft ecosystem, understand its performance and governance considerations, and develop hands-on skills in building conversational agents using the authoring canvas and its core components followed by an introduction to its advanced tools, orchestration features, and generative AI integration. Through guided instruction and a practical lab, learners will design, author, and deploy a fully functional conversational agent using variables, entities, and structured conversation flows. Copilot Architecture and Core Capabilities Overview of Copilot Architecture and its role in the Microsoft 365 and Power Platform ecosystem Copilot Studio Core Capabilities – building, deploying, and managing conversational and autonomous agents Copilots and Conversational AI – the evolution from chat-based interaction to orchestrated reasoning Understanding conversation volumes, quotas, and limits for performance management Performance optimization and telemetry insights for efficient operations Planning and Lifecycle Management Planning your agent – defining purpose, scope, target users, and measurable outcomes The Typical Copilot Studio Lifecycle: Initiate → Prepare → Design → Build → Deploy → Operate → Optimize Selecting the right tools for your governance requirements Citizen, Partnered, and Pro Developer governance zones Role-based permissions and security boundaries Leveraging the Power Platform Center of Excellence (CoE) for governance, auditing, and analytics Copilot Studio Authoring Canvas Navigating the authoring canvas and understanding topic-driven design Using the Message Node to deliver static, dynamic, or formatted responses Using the Question Node to capture user input and enable conditional branching Creating rich text responses (hyperlinks, adaptive cards, lists, media) Using variables to navigate customers to tailored content Defining entities and slot-filling for structured data collection Topic management – organizing and linking topics for efficient conversation flow Using enhanced speech authoring capabilities for voice interactions Productivity and pro-code options – integrating Power Fx, expressions, and custom logic Lab 2: Build a conversational agent in Copilot Studio Agent Tools, Knowledge, and Orchestration Overview of Agent Tools within Copilot Studio Connecting Knowledge Sources for grounding answers and context Using Tools to extend agent capabilities: Copilot Connectors – integrate with Microsoft 365, Dynamics 365, and external APIs Computer Use Tool – automate interactions with desktop or web apps Code Interpreter – execute Python code directly within Copilot Studio Advanced Approvals – automate multi-level approval processes Leveraging the Orchestrator to coordinate multi-topic, multi-tool execution Designing Prompts effectively for contextual, human-like responses Generative AI and Optimization Generative AI in Copilot Studio – enabling AI reasoning and dynamic responses Generative building – AI-assisted topic creation and optimization Generative answers – grounded, data-informed AI responses How Retrieval-Augmented Generation (RAG) supports accuracy and reliability Using analytics to easily optimize with data-driven insights Tracking agent performance Measuring engagement and response accuracy Iterating designs based on telemetry Integration, Agent Flows, and Multi-Agent Orchestration Designing Agent Flows for end-to-end automation Multi-agent orchestration – coordinating several specialized agents to handle complex workflows Planning integrations with Power Automate, Azure services, and external APIs Deploying and using agents in any system – Teams, web, Dynamics 365, or embedded applications Lab 3: Use tools in Copilot Studio Module 3 – Building Autonomous Agents This module introduces the concept of autonomous agents within Microsoft Copilot Studio and explores how they extend beyond conversational interactions to perform independent, goal-oriented actions. Learners will understand the difference between conversational and autonomous agents, explore the core components that enable autonomy, and learn how to craft precise, ethical, and effective agent instructions. Through guided demonstrations and practical exercises, participants will design the foundational structure of an autonomous agent, integrating triggers, tools, and logic to automate business processes intelligently and responsibly. Understanding Autonomous Agents What Are Autonomous Agents? Definition and evolution of autonomous agents within Microsoft Copilot Studio How autonomous behavior enhances business automation and decision-making Examples of autonomous agents in real-world enterprise scenarios (approvals, notifications, data insights) Conversational vs Autonomous Agents Key differences in purpose, behavior, and user interaction Conversational agents: reactive and guided by prompts Autonomous agents: proactive, event-driven, and task-focused Choosing the right agent type for your business process Reinventing Business Processes with Agents How autonomous agents can reimagine workflows across departments Mapping manual processes to autonomous agent capabilities Best practices for balancing automation with control and accountability Crafting Autonomous Agent Instructions The Art of Instruction Design Writing clear, structured agent instructions Defining role, tone, scope, and context for autonomous behaviour Handling ambiguity, uncertainty, and ethical constraints Building Blocks of Writing Agent Instructions Purpose statement Behavioural rules and escalation paths Contextual memory and grounding Error handling and safety checks Examples of well-structured vs poorly written instructions Building Blocks of an Autonomous Agent Core Components of Autonomy Triggers: initiating events that activate agents (manual, scheduled, event-driven) Tools: actions and connectors that allow agents to perform work Knowledge: content sources and grounding data Instructions: defining how agents reason and respond Designing multi-step workflows with Copilot Studio tools and triggers Understanding dependencies, data flow, and governance boundaries Lab 4: Make your agent autonomous in Copilot Studio Module 4 – Language & Orchestration This module focuses on how Microsoft Copilot Studio interprets, processes, and orchestrates natural language to deliver accurate, contextual, and human-like responses. Learners will explore language understanding models, orchestration types, and best practices for building intelligent agents that can manage ambiguity, route topics effectively, and deliver consistent communication across languages and scenarios. Natural Language Understanding (NLU) in Copilot Studio Overview of Natural Language Understanding and its role in conversational AI How Copilot interprets intent, entities, and context from user input Comparison of language processing models: Classic Orchestration (topic-based routing) Generative Orchestration (AI-driven reasoning and chaining) The evolution from rule-based intent matching to generative orchestration Orchestration Models and Logic Classic Orchestration How Copilot matches user intent to predefined topics Topic routing, priority, and fallback mechanisms Using disambiguation prompts when multiple intents are detected Disambiguation and Orchestration Strategies for managing ambiguous queries Designing topic structures for clear and efficient disambiguation Example: differentiating between “request access” vs. “reset password” topics Generative Orchestration How Copilot uses generative AI to route across tools, topics, and data The orchestration engine: connecting tools, knowledge sources, and actions Combining structured (classic) and generative (adaptive) orchestration for hybrid agents How orchestration improves responsiveness, reduces errors, and automates complex reasoning How Tools and Topics Are Orchestrated Role of the orchestrator in managing flow between topics and tools Understanding priority, context retention, and handoffs Integrating multiple data sources or services within a single agent session Designing for Clarity and Effectiveness Best Practices for Agent Instructions Writing clear, concise, and contextual instructions for accurate orchestration Controlling tone, scope, and fallback responses Using grounding and constraints to ensure reliable generative outputs Best Practices for Topic Inputs & Outputs Defining input and output expectations for each topic Using metadata and variable binding for consistent data transfer Creating well-structured topic hierarchies to reduce confusion and error loops Language Control and Localization What Controls the Agent Language Role of AI models, user preferences, and system settings in language handling Managing multilingual environments with consistent tone and structure Auto-Detect Spoken Language How Copilot Studio detects and adapts to user language automatically Best practices for supporting multilingual users (voice and text) Configuring fallback or default language options for enterprise scenarios Module 5 – AI capabilities This module introduces learners to the power of Retrieval-Augmented Generation (RAG) and how it enhances Copilot Studio agents with accurate, grounded, and secure generative responses. Participants will explore how RAG architecture operates within Copilot Studio, how to connect and manage knowledge sources effectively, and how to safely infuse generative AI capabilities into topic designs while maintaining compliance and reliability. How RAG Enhances AI Responses Understanding Retrieval-Augmented Generation (RAG) and why it’s essential for enterprise-grade AI The limitations of pure generative AI (hallucination, context drift, factual inaccuracy) How RAG combines retrieved data from trusted knowledge sources with generative reasoning Benefits of RAG in Copilot Studio: Grounded responses based on organizational data Context retention and relevance in extended conversations Reduced risk of misinformation or unsupported answers Real-world examples: HR policy retrieval, compliance support, and project insights RAG Architecture in Copilot Studio Overview of RAG architecture within the Microsoft Copilot ecosystem The retrieval layer: indexing, search, and grounding data pipelines The generation layer: contextual synthesis using large language models (LLMs) How Copilot Studio manages context windows, relevance scoring, and ranking Integrating with Microsoft Graph, SharePoint, Dataverse, and external data repositories Understanding caching, latency, and query optimization in RAG-based designs Knowledge Sources and Generative AI Defining knowledge sources in Copilot Studio SharePoint document libraries Dataverse tables External data via connectors or APIs How Copilot Studio uses knowledge sources to ground generative responses Techniques for data curation and preparation for RAG indexing Integrating structured and unstructured data into your Copilot agent How Generative AI interacts with these sources to produce reliable, factual answers Generative AI Security and Compliance Considerations Data protection in generative AI workflows How Copilot Studio handles sensitive or restricted content Managing compliance requirements (GDPR, data residency, retention policies) Understanding Responsible AI principles within Microsoft’s Copilot framework Limiting generative scope through instructions, topic constraints, and data access rules Setting boundaries for autonomous or open-ended responses Infusing Generative AI into Topics Methods for embedding generative capabilities directly within topics Enabling Generative Building and Generative Answers in Copilot Studio Designing hybrid topics that use both structured and generative logic Writing grounded prompts that combine user input, retrieved knowledge, and AI synthesis Testing and refining generative topics to ensure consistency, tone, and accuracy Module 6 – Integrations This module explores how to integrate Copilot Studio agents with enterprise systems and external data sources using connectors, flows, APIs, and automation tools. Learners will understand key integration patterns, performance constraints, and quotas, as well as how to extend Copilot capabilities through custom connectors, HTTP requests, and the Model Context Protocol (MCP). Integration Patterns and Considerations Overview of integration architecture within Copilot Studio and the Power Platform Integration patterns: Direct integration (connectors and HTTP actions) Event-driven orchestration (Power Automate flows) Data-driven integration (Dataverse, APIs, Azure Logic Apps) Choosing the right integration model based on scalability, latency, and control Security considerations for data flow and authentication (OAuth, managed identity, service principal) Quotas, Limits, and Performance Understanding Copilot Studio integration quotas and limits (calls per minute, session size, data throughput) Performance tuning strategies for efficient agent workflows Managing API throttling, retries, and error handling in long-running tasks Using telemetry and analytics to monitor connector and flow performance How to design integrations that minimize cost and resource consumption Agent Flows, HTTP Actions, and Connectors Introduction to Agent Flows in Copilot Studio for orchestrating integrations Using HTTP actions for RESTful API calls and external service integration Working with standard connectors (SharePoint, Outlook, Teams, Azure, Dynamics 365, Dataverse, etc.) Designing connector-based actions to extend agent functionality Handling timeouts and long-running processes in cloud flows Best practices for async processing and status callbacks Managing cloud flow timeouts gracefully with user feedback loops Custom Connectors and Advanced Integration Overview of Custom Connectors in Copilot Studio and Power Platform How to build and register a custom connector for internal or external APIs Using Swagger/OpenAPI definitions to define connector actions and responses Testing and validating custom connectors in sandbox environments Governance considerations for connector publishing and sharing Integration with Azure Functions and Logic Apps for extensibility Model Context Protocol (MCP) Introduction to the Model Context Protocol (MCP) and its role in AI-driven integrations How MCP enables Copilot agents to securely access external data models Using MCP to extend Copilot with contextual understanding from multiple systems Best practices for maintaining data integrity and compliance in MCP-connected agents Computer-Using Agents (CUA) and RPA Understanding Computer-Using Agents (CUA) – what they are and how they work Enabling agents to interact with desktop applications and web browsers Comparing CUA and Robotic Process Automation (RPA): RPA for structured, rule-based workflows CUA for intelligent, adaptive automation through AI orchestration Building integrated processes that combine cloud flows, connectors, and CUAs Example use cases: Reading data from legacy apps Filling forms or automating reports Coordinating actions between on-premises and cloud systems Module 7 – Security, monitoring & governance This module equips learners with the knowledge and best practices needed to secure, monitor, and govern Microsoft Copilot Studio environments at scale. Participants will learn how to balance innovation and control, implement zoned governance models, enforce data loss prevention (DLP) and compliance policies, and manage the security of both agents and users. Balancing Innovation and Governance The importance of governance in AI and Copilot deployment Balancing citizen development and enterprise oversight How governance enables safe innovation without stifling productivity Building governance frameworks aligned with corporate IT and security policies Common governance challenges in scaling Copilot adoption The Agent Controls Model Understanding the Agent Controls Model: policies, permissions, and oversight Key governance layers: user, environment, tenant, and data How agent-level controls support compliance and operational transparency Tracking agent performance and telemetry for security auditing Zoned Security, Governance, and Operations Overview of Governance Zones (1–3): Zone 1: Citizen Development (low-risk, innovation sandbox) Zone 2: Partnered Development (moderate risk, departmental use) Zone 3: Pro Development (high governance, enterprise-critical) Mapping organizational maturity to governance zones Getting Started with Zones – setting up secure, scalable environments How to manage transitions between zones while maintaining compliance Security and Administration Controls Overview of security architecture in Copilot Studio and Power Platform Security, agent, and user management strategies: Assigning roles and permissions Enabling secure authentication (Azure AD, Entra ID) Monitoring user actions and agent activity logs Designing an effective environment strategy for production, test, and development Understanding Copilot Studio security roles and least-privilege access design Data Loss Prevention (DLP) and Policy Management Overview of DLP Policies in Power Platform and Copilot Studio The role of DLP connectors and data classification in controlling data flow How to manage connectors across risk profiles (Business vs. Non-Business) DLP policies and rules per environment — and when they can be safely relaxed Designing DLP frameworks that balance flexibility and compliance Practical examples: blocking external connectors, auditing data access Securing Copilot Studio Usage at Scale Strategies for secure scaling across large organizations Controlling adoption through environment boundaries and sharing rules Using analytics and reports to monitor agent performance, cost, and usage Automating governance checks and policy enforcement through CoE Starter Kit Prompt Injection Mitigations – preventing manipulation of agent behavior through malicious inputs DDoS Protection for Anonymous Chatbots – safeguarding public-facing Copilots against overload attacks Data Residency and Compliance Management Understanding data residency and storage in Microsoft Copilot Studio Managing data movement restrictions across geographies and tenants Compliance with GDPR, SOX, HIPAA, and other global standards Designing for multi-region governance and local data processing Tools and best practices for monitoring data movement and access patterns Module 8 – Application Lifecycle Management This module focuses on implementing Application Lifecycle Management (ALM) practices for Copilot Studio. Learners will explore how ALM ensures structured development, testing, and deployment of Copilot agents across environments while maintaining governance, consistency, and control. Participants will gain practical insights into using Power Platform ALM, Azure DevOps, GitHub Actions, and Power Platform Pipelines to manage agent updates and continuous delivery in enterprise environments. Understanding ALM Strategy Defining an ALM strategy for Copilot agents within Microsoft 365 and Power Platform Why ALM is essential for enterprise-scale development and governance The benefits of structured lifecycle management: Version control Testing and quality assurance Controlled deployment across environments Reduced risk and rework Aligning ALM practices with organizational change management and security policies What Is ALM and Why It’s Important Overview of Application Lifecycle Management (ALM) concepts Development → Testing → Staging → Production cycles Managing agent versions and configurations How ALM supports collaboration between makers, developers, and administrators Common pitfalls in unmanaged agent updates or direct publishing Real-world ALM examples in Copilot Studio agent deployment What “Publish” Really Does in Copilot Studio Understanding the Publish process in Copilot Studio What happens behind the scenes when publishing an agent Version creation, environment packaging, and synchronization How publishing differs from exporting/importing solutions in Power Platform Best practices for publishing safely without disrupting production agents Integrating publishing into your broader ALM workflow Power Platform ALM for Copilot Studio Overview of Power Platform ALM capabilities relevant to Copilot Studio How solutions encapsulate Copilot agents, connections, and data configurations Managing Copilot components as part of broader Power Platform solutions Understanding environments and their role in ALM: Development, Test, UAT, and Production Environment permissions, data boundaries, and DLP alignment Tracking agent versions, dependencies, and solution history ALM with Azure DevOps Integrating Azure DevOps with Power Platform and Copilot Studio Managing Copilot solution source control and version tracking Automating deployment pipelines through Azure DevOps YAML templates Example workflows: Export → Validate → Deploy Trigger-based deployments for agents or connectors Using Azure DevOps Boards for ALM governance and change tracking GitHub Actions for Microsoft Power Platform Introduction to GitHub Actions for CI/CD with Power Platform Setting up a GitHub repository to manage Copilot Studio solutions Example automation: Exporting a solution from Dev → Importing to Test or Prod Running validation checks before deployment Comparing Azure DevOps Pipelines vs. GitHub Actions for ALM workflows Security and permission considerations for GitHub integrations Power Platform Pipelines for Copilot Studio Introduction to Power Platform Pipelines — no-code ALM for citizen and pro developers How Pipelines simplify solution promotion across environments Configuring automated pipelines for Copilot Studio agents Using deployment profiles to control environment variables and data connections Monitoring deployment success, rollback procedures, and version tracking Combining Pipelines, GitHub, and DevOps for hybrid ALM strategies Module 9 – Analytics & KPIs This module teaches learners how to measure, analyze, and optimize the performance of Copilot Studio agents using data-driven insights. Participants will explore conversation analytics, engagement metrics, and key performance indicators (KPIs) that reflect business impact and user satisfaction. By implementing a structured analytics and optimization strategy, learners will be able to continuously improve their agents’ effectiveness, refine conversation design, and demonstrate ROI to stakeholders. Conversation Design and Outcome Tracking Understanding conversation analytics in Copilot Studio Measuring conversation flow effectiveness: intent recognition, success paths, and drop-off points Designing conversations with measurable outcomes (e.g., task completion, satisfaction, resolution rates) Tracking end-user interactions and intent success using telemetry and built-in analytics Mapping conversational outcomes to business objectives and performance goals Using conversation data to refine prompts, topics, and agent logic Engagement and Outcomes Defining and measuring engagement metrics: Total users, active sessions, conversation depth, and dwell time Repeat interactions and user satisfaction trends Identifying key engagement drivers — tone, context, personalization, and response time Using data to segment audiences and identify high-value user scenarios Correlating agent engagement with organizational productivity and ROI Example KPIs: Resolution rate per topic Time-to-response improvement Reduction in support tickets through automation Business cost savings from AI adoption Analytics Strategy Building a comprehensive analytics strategy for Copilot agents Aligning analytics goals with business priorities and governance policies Defining measurable KPIs for agent performance and value realization Using Microsoft analytics tools: Copilot Studio Analytics Dashboard Power BI integration for advanced reporting Dataverse telemetry for raw data analysis How to combine Copilot analytics with Power Platform CoE dashboards Tracking metrics across environments: Dev, Test, and Production Data governance considerations in analytics — ensuring accuracy and privacy Optimization Strategy Developing an optimization cycle for continuous improvement: Monitor → 2. Analyze → 3. Adjust → 4. Deploy → 5. Measure again Leveraging A/B testing for topic or prompt improvements Applying analytics insights to refine: Agent tone and conversational flow Topic hierarchy and orchestration logic Knowledge source selection and generative AI prompts Using performance data to identify underperforming agents or topics Creating a feedback loop with stakeholders and users for ongoing tuning Setting thresholds and alerts for critical KPIs (e.g., low satisfaction, high failure rate Module 10 – Licensing and capacity This module provides learners with a deep understanding of how Microsoft Copilot Studio licensing and capacity consumption work within the Power Platform ecosystem. Participants will learn how to plan, monitor, and manage Copilot Studio resource usage across environments while maintaining cost efficiency and operational scalability. Licensing and Capacity Overview Overview of Copilot Studio licensing models Licensing through Microsoft 365, Power Platform, and standalone Copilot subscriptions Key licensing dependencies: Power Virtual Agents, Power Automate, Dataverse Capacity components and what they represent: Dataverse storage (database, file, log) Power Platform request limits AI Builder and Copilot Studio usage entitlements Aligning licensing strategy with your organization’s scale, user base, and governance zones How to assign and manage licenses across tenants and environments Basic Credit Consumption Scenarios Understanding Copilot capacity credits and how they are consumed Common usage patterns that drive credit consumption: Agent interactions and conversation sessions Generative AI responses and knowledge retrieval Tool usage (code interpreter, connectors, RAG queries) Mapping agent types to credit requirements (simple, task-based, autonomous) Real-world credit usage examples for typical Copilot deployments How conversation complexity, orchestration, and integration affect credit burn Agent Activity and Billing Rates Understanding Agent Activity metrics and their impact on billing How billing rates differ based on: Generative AI vs. retrieval-based responses Tool and connector usage Frequency of orchestration or autonomous agent actions Reading and interpreting usage reports in the Power Platform admin center Using telemetry data to connect activity metrics to cost drivers Best practices for minimizing unnecessary agent calls and redundant executions Understanding Credit Burn Rate Definition of credit burn rate in Copilot Studio How to monitor and project credit consumption across environments Factors influencing burn rate: Agent concurrency and scaling Number of active users or sessions Size and complexity of AI prompts and retrieval operations Strategies for managing and reducing burn rate: Optimizing conversation length and efficiency Reusing knowledge sources and cached responses Scheduling non-critical agents during off-peak hours Setting up alerts or dashboards to track consumption trends Copilot Studio Estimator Introduction to the Copilot Studio Estimator Tool How to use the estimator to forecast usage, licensing, and costs Simulating scenarios based on: Number of agents Daily conversation volume Generative AI usage patterns Interpreting estimator outputs to guide budget and capacity planning Integrating estimator results into business case and ROI modeling Capacity Management Building a capacity management strategy for Copilot Studio Monitoring capacity in the Power Platform Admin Center Allocating capacity per environment and adjusting for usage growth Using governance zones and environment strategy to balance capacity Managing cross-tenant capacity sharing and reporting Planning for scale: forecasting enterprise-wide usage Coordinating with IT operations and finance teams for ongoing monitoring Module 11 – Testing agents This module teaches learners how to effectively test, validate, and ensure quality for Copilot Studio agents before deployment. Participants will explore Copilot Studio Kit testing capabilities, learn how to test agents at scale, and understand the types of tests supported within the Copilot Studio ecosystem. By applying structured testing practices, learners will develop the skills to identify defects, improve performance, and deliver reliable, production-ready Copilot agents that align with enterprise standards. Introduction to Testing in Copilot Studio The importance of testing and validation in the Copilot agent lifecycle How testing fits into the Application Lifecycle Management (ALM) and deployment process Typical challenges in testing AI-driven and conversational systems Core goals of agent testing: Ensuring accuracy, reliability, and usability Verifying data access and security Confirming proper orchestration and workflow logic Overview of the Copilot Studio Kit Introduction to the Copilot Studio Kit and its testing capabilities Components of the Kit: Test automation framework Reporting and analytics tools Configuration and environment setup utilities How the Kit integrates with Power Platform, Azure DevOps, or GitHub Actions pipelines Preparing the testing environment and data sets Using the Kit for continuous testing in multi-environment deployments Testing Agents at Scale Strategies for scaling agent testing across multiple environments and use cases Simulating large-scale user interactions and load testing scenarios Managing and tracking test execution across Dev, UAT, and Production environments Automating tests as part of CI/CD pipelines (DevOps or GitHub Actions) Monitoring performance and response times under real-world usage conditions Identifying and resolving issues related to: Latency and orchestration delays Data retrieval and grounding (RAG) errors Tool integration failures or misconfigurations Ensuring governance compliance during automated test runs Supported Test Types in the Copilot Studio Kit Overview of test types supported in Copilot Studio Kit: Unit Tests: Validate individual topics, nodes, and responses Integration Tests: Verify that connectors, triggers, and tools function correctly together Regression Tests: Ensure existing functionality remains stable after changes or updates Performance Tests: Evaluate speed, concurrency, and response times Security Tests: Validate DLP adherence, authentication, and permissions Conversational Flow Tests: Assess natural language understanding, disambiguation, and orchestration accuracy Best practices for selecting appropriate test types for each phase of development How to interpret test results and generate actionable reports Additional Labs: The following labs have been provided in order to gain additional hands on learning experience with Copilot Studio. They augment the labs aligned to each module and should be carried out once all the previous labs have been completed if the course is run other 3 days: Advanced Lab 1: Create a Monthly Business Review (MBR) Agent Advanced Lab 2: Build an Autonomous Account News Assistant Agent Advanced Lab 3: Track conversation outcomes and user feedback on AI responses Advanced Lab 4: Autonomous Portfolio Lookup Agent with Computer-Using Agents (CUA) Advanced Lab 5: Deliver high-quality, scalable agents with Copilot Studio Kit Advanced Lab 6: Model Context Protocol (MCP) & Copilot Studio

OVERVIEW Get the knowledge and skills to build, manage, and secure a VMware Cloud Foundation (VCF) Private Cloud version 9.1.   This five-day course provides knowledge and skills on VCF architecture, licensing models, and Private Cloud components at both the fleet and instance levels. The course covers end-to-end fleet deployment using the VCF Installer, post-deployment tasks including VCF Operations component setup, and fleet management through Single Sign-On configuration, VCF Identity Broker, role-based access control, and credential management. Participants, through hands-on labs,  will also deploy and manage workload domains, configure VMware NSX networking with Virtual Private Cloud constructs and Transit Gateway configurations, and manage vSAN storage using policies, fault domains, and VCF Operations monitoring tools. The course also covers VCF certificate management using SDDC Manager with Microsoft CA, OpenSSL CA, and third-party certificate authorities. Participants will perform lifecycle management operations including patching and upgrading VCF management and fleet components, and executing backup and restore procedures across the VCF fleet. In the VCF Security section, participants will define security, compliance, and resilience in VCF, assess compliance posture using Security Posture Management, monitor security health through the Security Operations Dashboard, and review audit trail events. The course concludes with VCF upgrade paths to version 9.1, covering supported upgrade scenarios from vSphere 8.0 and VCF 5.2 environments. Updated July 2026 OBJECTIVES By the end of the course, participants should be able to: Describe VMware Cloud Foundation architecture and Private Cloud components at the fleet and instance levels Deploy and configure a VCF Private Cloud using the VCF Installer, including networking, storage, and post-deployment tasks Manage VCF Fleet operations including SSO, RBAC, identity federation, workload domains, NSX networking, and vSAN storage Manage VCF lifecycle using the software depot, patch and upgrade VCF components, and implement backup and restore procedures Apply VCF security using Security Posture Management, the Security Operations Dashboard, certificate management, and supported upgrade paths to VCF 9.1 AUDIENCE System Administrators, Solution Engineers, Consultants, Architects, and Support Personnel CERTIFICATION Specific certification for VCF 9.1 is not yet available.  NEXT STEP None CONTENT Module 1: Course Introduction Describe VMware Cloud Foundation components, architecture, and supported private cloud use cases Explain the VMware Cloud Foundation licensing model and available license types Outline the course curriculum covering deployment, fleet management, networking, storage, lifecycle, and security Module 2: VCF Private Cloud: Overview and Architecture Define VMware Cloud Foundation key features, use cases, and integrated security capabilities across all layers Explain the advanced services and operational capabilities of VMware Cloud Foundation Describe the VCF Private Cloud architecture including fleet-level and instance-level components Identify the various roles and capabilities within a VCF Private Cloud environment Module 3: VCF Private Cloud Deployment Identify VCF fleet deployment considerations and planning requirements using the Planning and Preparation Workbook Outline the sequence for deploying VCF Private Cloud including core component configuration Describe the deployment configuration of VCF instance and fleet management components Deploy a new VCF fleet using the VCF Installer wizard or a deployment specification JSON file Module 4: VCF Post-Deployment Tasks Explain VCF Operations for Networks, VCF Operations for Logs, and VCF Identity Broker deployment models Describe deployment considerations for VCF Operations components including Real-Time Metrics Deploy and navigate VCF Operations components and perform post-deployment management tasks Module 5: VCF Fleet Management Describe the VCF licensing model including capacity pooling, primary and add-on licenses, and 9.1 licensing procedures Configure VCF Single Sign-On and VCF Identity Broker deployment models for all VCF instance components Identify supported directories and identity providers and configure SSO enablement across VCF components Manage role-based access control, users, user groups, and credentials in VMware Cloud Foundation Outline password management workflows and available API options for automating credential management tasks Module 6: VCF Workload Domain Explain VMware Cloud Foundation workload domains and apply vSphere, network, and storage design considerations Describe design prerequisites and create workload domains using VCF Operations Configure vCenter linked groups and manage host and cluster additions within workload domains Import an existing vCenter as a workload domain using VCF Operations including applicable import limitations Module 7: VCF Networking Identify the core components of the NSX architecture including management, control, and data planes Describe NSX overlay networking concepts including Geneve encapsulation, TEP configuration, and Traceflow Explain Virtual Private Cloud constructs and configure VPC and VPC subnets in VMware Cloud Foundation Configure network connectivity during VCF Fleet deployment including Distributed and Centralized Transit Gateways Module 8: VCF Storage Management Describe VMware Cloud Foundation storage models including principal and supplemental storage options Explain vSAN networking and hardware requirements and configure vSAN clusters with appropriate storage policies Configure vSAN fault domains and manage vSAN cluster operations in VMware Cloud Foundation Analyze vSAN performance and generate storage reports using VCF Operations monitoring tools Module 9: VCF Certificate Management Describe public key infrastructure concepts including certificate signing requests and CA options in SDDC Manager Manage certificates in VMware Cloud Foundation using the SDDC Manager certificate management workflow Integrate VCF Operations with Microsoft CA and OpenSSL CA for certificate issuance and installation Install and manage certificates issued by Microsoft CA, OpenSSL CA, or third-party CAs in VCF Operations Module 10: VCF Lifecycle Management Identify the role of the software depot and configure it in VCF Operations to manage VCF fleet lifecycle Manage software depot content, storage, and bundle assignments in VCF Operations Upgrade and patch VCF management and fleet management components using lifecycle management workflows Configure backup and restore procedures for the VCF fleet using SFTP, SDDC Manager, and fleet management nodes Execute backup and restore operations for fleet-level and VCF management components Module 11: VCF Security Define security, compliance, and resilience principles and describe integrated security features across all VCF layers Assess compliance and manage security posture using Security Posture Management in VMware Cloud Foundation Monitor security health through the Security Operations Dashboard and analyze events using the Audit Trail Orchestrate unified Protection and Recovery workflows using VCF advanced networking and security capabilities Module 12: VCF Upgrade Paths Identify supported upgrade paths to VCF 9.1 including key considerations, prerequisites, and compatibility assessments Explain the upgrade process from vSphere 8.0 to VCF 9.1 using the existing vSphere deployment path Outline upgrade stages from vSphere 8.0 with multiple Aria components to VMware Cloud Foundation 9.1 Describe prerequisites and upgrade sequence from VCF 5.2 with Aria components to VMware Cloud Foundation 9.1 Differentiate between the existing vSphere upgrade path and the vCenter import process for transitioning to VCF 9.1

Wil je als leidinggevende bewuster schakelen tussen mensen, resultaten en organisatiebelangen? In deze driedaagse training versterk je jouw leiderschap met inzichten uit de organisatiepsychologie. Je ontdekt hoe jouw gedrag doorwerkt in samenwerking, motivatie en verandering. Met praktische handvatten en begeleiding van trainers uit de praktijk maak je bewustere keuzes en geef je doelgerichter leiding in uiteenlopende werksituaties. Deze training wordt binnen Eduvision verzorgd door Skills Alchemist, ons label voor trainingen op het gebied van human skills en organisatieontwikkeling, met de dienstverlening die je van Eduvision gewend bent. Algemene omschrijving Leidinggeven vraagt om meer dan alleen het aansturen van een team. Je schakelt voortdurend tussen mensen, resultaten en organisatiebelangen, terwijl je keuzes maakt in situaties die vaak meerdere belangen raken. In deze driedaagse training ontwikkel je jouw leiderschap vanuit inzichten uit de organisatiepsychologie. Je leert hoe jouw gedrag invloed heeft op samenwerking, motivatie en verandering en hoe je jouw leiderschapsstijl bewust afstemt op wat een situatie vraagt. De training begint met het versterken van het inzicht in jezelf als leider en de invloed van jouw gedrag op anderen. Vervolgens ontdek je hoe je mensen verbindt, motiveert en richting geeft in uiteenlopende situaties. Vanuit die basis werk je aan het begeleiden van verandering en het versterken van samenwerking binnen teams en organisaties. Tijdens de training werk je met herkenbare voorbeelden en praktijkgerichte oefeningen die direct aansluiten op situaties waarmee leidinggevenden dagelijks te maken hebben. De trainer brengt ruime ervaring mee vanuit verschillende organisaties en vertaalt inzichten steeds naar concrete toepassingen. Deze training bieden we ook als bedrijfstraining voor jou en je team De inhoud stemmen we dan af op jullie werksituatie, concrete vraagstukken, context en leiderschapsstijl  zodat de training direct aansluit op wat er binnen jouw organisatie speelt. Zo ontstaat een gerichte en praktische training waar je de volgende dag direct mee aan de slag kunt. Programma Tijdens de training Leiderschap voor Teammanagement komen in basis onderstaande onderwerpen aan bod. Afhankelijk van ontwikkelingen op het vakgebied, kan de feitelijke trainingsinhoud hier echter van afwijken. Bel ons gerust voor meer informatie over de actuele inhoud. Jouw leiderschap als vertrekpunt Inzicht in jouw leiderschapsstijl en de invloed daarvan op anderen Herkennen van overtuigingen, drijfveren en gedragspatronen Ontwikkelen van een leiderschapsstijl die past bij jouw rol en ambities Schakelen tussen mensen, resultaten en organisatiebelangen Bewust afwegen van verschillende belangen en verantwoordelijkheden Leidinggeven vanuit intrinsieke motivatie en eigenaarschap Omgaan met weerstand en uiteenlopende perspectieven Mensen in beweging brengen Voeren van gesprekken die verbinden, richting geven en uitdagen Herkennen van groepsdynamiek en gedrag binnen teams Creëren van vertrouwen, betrokkenheid en gezamenlijke verantwoordelijkheid Leidinggeven aan verandering Begrijpen hoe mensen reageren op verandering en onzekerheid Situationeel leidinggeven en bewust schakelen in leiderschapsstijl Bouwen aan weerbaarheid, ontwikkeling en duurzame samenwerking De training is interactief en praktijkgericht opgezet. Je werkt actief aan herkenbare situaties, met veel ruimte voor vragen en eigen voorbeelden. Door te oefenen en bespreken ervaar je hoe je jouw leiderschap bewust afstemt op verschillende situaties en met meer vertrouwen schakelt tussen mensen, resultaten en organisatiebelangen. De training wordt verzorgd door trainers uit de praktijk, die ervaring hebben in verschillende organisatiecontexten. We houden altijd rekening met jouw verwachtingen, zodat de training aansluit bij wat voor jou relevant is. Doelen Na deze training heb je handvatten om: Te schakelen tussen mensen, resultaten en organisatiebelangen Te herkennen hoe jouw gedrag invloed heeft op samenwerking, motivatie en prestaties Jouw leiderschapsstijl af te stemmen op verschillende situaties en behoeften Richting te geven aan verandering en omgaan met weerstand binnen teams Eigenaarschap en samenwerking te versterken binnen jouw team

Leer de fundamenten van de Go-taal, datastructuren, interfaces, generics en concurrency. In deze cursus bouw je een sterke basis in Go door te leren hoe Go-programma's zijn opgebouwd met packages en modules, hoe functies en methoden worden ontworpen en hoe interfaces polymorfisme mogelijk maken. Je oefent met onderdelen van de taal, zoals statements, strings en runes, slices, maps, error- en panic-afhandeling, en generics met type constraints. De cursus introduceert ook goroutines, channels en select voor concurrent programmeren, en laat het praktische gebruik zien van standaardbibliotheekpackages zoals bufio, cmp, crypto, sql, iter, log, math, net en os. - Explain Go program structure using packages, modules, imports, and initialization order [Understand] - Apply Go statements to write clear and correct control flow [Apply] - Apply Go core data types to represent and transform values safely [Apply] - Implement functions and methods using parameters, return values, receivers, and defer for predictable behavior [Apply] - Analyze interface-based designs, method sets, and type assertions for safe polymorphism [Analyze] - Implement generic workflows using type parameters and constraints [Apply] - Implement concurrent workflows using goroutines, channe…

Operationalize machine learning and generative AI solutions | AI-300 | AI300 This course prepares learners to design, implement, and operate Machine Learning Operations (MLOps) and Generative AI Operations (GenAIOps) solutions on Azure. It covers building secure and scalable AI infrastructure, managing the full lifecycle of traditional machine learning models with Azure Machine Learning, and deploying, evaluating, monitoring, and optimizing generative AI applications and agents using Microsoft Foundry. Learners will gain hands-on knowledge of automation, continuous integration and delivery, infrastructure as code, and observability by using tools such as GitHub Actions, Azure CLI, and Bicep. The course emphasizes collaboration with data science and DevOps teams to deliver reliable, production-ready AI systems aligned with modern MLOps and GenAIOps best practices. Voorkennis Basic understanding of machine learning concepts, Python programming, and familiarity with Azure Machine Learning and DevOps fundamentals is recommended. Voor wie This course is intended for data scientists, machine learning engineers, and DevOps professionals who want to design and operate production-grade AI solutions on Azure. It is suited for learners with experience in Python, a foundational understanding of machine learning concepts, and basic familiarity with DevOps practices such as source control, CI/CD, and command-line tools, who are preparing to implement MLOps and GenAIOps workflows using Azure-native services. Doelstellingen By the end of this course, learners will be able to design, deploy, automate, monitor, and optimize machine learning and generative AI solutions on Azure using MLOps and GenAIOps practices to deliver secure, scalable, and production‑ready AI systems. Inhoud – Operationalize machine learning models (MLOps) – Operationalize generative AI applications (GenAIOps) Exclusief examen AI-300 | Operationalizing Machine Learning and Generative AI solutions DutchTrain is een officieel geaccrediteerd Test Center voor Pearson Vue Test, Prometric, Kryterion, Castle Worldwide, Certiport & PSI. U bent bij ons van harte welkom voor examens welke via deze Test Centers beschikbaar zijn. Examens kunnen elke dag, binnen kantooruren, worden afgenomen. Duur 4 dagen Deze training is ook beschikbaar als – Education On Demand (e-learning) – Maatwerktraining, neem hiervoor contact op met een van onze opleidingsadviseurs.

Implement data engineering solutions using Azure Databricks | DP-750 | DP750 Design and deliver scalable data pipelines using Azure Databricks and the lakehouse architecture. This course moves from foundational setup to production deployment, covering environment configuration and enterprise-grade governance. Learn to build robust ingestion pipelines, implement security with Unity Catalog, and deploy optimized workloads. By the end, you will have the practical skills to implement, secure, and maintain scalable lakehouse solutions that meet rigorous enterprise requirements. Voor wie The target audience is data engineers who have fundamental knowledge of data analytics concepts, a basic understanding of cloud storage, and familiarity with data organization principles. They should be comfortable working with SQL and have experience using Python, including notebooks, for data engineering tasks. Learners are expected to have a good understanding of Azure Databricks workspaces and Unity Catalog, along with familiarity with data access patterns and core data engineering and data warehouse concepts. In addition, they should have foundational knowledge of Azure security, including Microsoft Entra ID, and be familiar with Git version control fundamentals. Inhoud – Configure and manage Azure Databricks environments, including workspaces, clusters, and compute resources. – Ingest, transform, and model data using SQL and Python to support analytics and downstream workloads. – Build, deploy, and maintain scalable data pipelines using Azure Databricks and lakehouse patterns. – Apply data governance and security best practices using Unity Catalog for access control, governance, and data quality. – Integrate Azure Databricks with core Azure services, including Azure Storage, Azure Data Factory, Azure Monitor, Microsoft Entra ID, and Azure Key Vault. – Troubleshoot, monitor, and optimize Databricks workloads to ensure reliable, production‑ready data engineering solutions. Exclusief examen DP-750 | Implementing Data Engineering Solutions Using Azure Databricks DutchTrain is een officieel geaccrediteerd Test Center voor Pearson Vue Test, Prometric, Kryterion, Castle Worldwide, Certiport & PSI. U bent bij ons van harte welkom voor examens welke via deze Test Centers beschikbaar zijn. Examens kunnen elke dag, binnen kantooruren, worden afgenomen. Duur 4 dagen Deze training is ook beschikbaar als – Maatwerktraining, neem hiervoor contact op met een van onze opleidingsadviseurs.

Kubernetes has become the industry standard for operating container-based applications. Whether your organization seeks to deploy web applications for internal or external customers, operate a Spar... Day 1 - Introduction to and History of Kubernetes - 10,000 Feet View of Kubernetes - Crash Course: Docker Fundamentals - Lab 1: Deploying the First Container - Applications on Kubernetes (Pods, Services, Deployments) - Interfacing with Kubernetes with kubectl - Lab 2: Deploying a Real-World Application Day 2 - Guide to Kubernetes Cluster Infrastructure - Security with Secrets, RBAC and Namespaces - Networking Overview - Lab 3: Securing the Application - Understanding Ingress - Storage Options - Stateful Sets - Lab 4: Databases on Kubernetes Day 3 - High-Availability and Desaster Recovery - Elasticity and Scaling - Cloud Options - Lab 5: Spinning up a Cluster on Your Favorite Cloud Provider - Using Helm - Course Wrap-Up In this course, you will learn to: - Make architectural decisions based on Kubernetes architectural principles and paradigms - Leverage Kubernetes features to make your applications scalable, reliable, and highly available - Leverage the Kubernetes infrastructure to enable greater flexibility and resiliency of your overall solution - Adopt security best practices for their applications and clusters. - Deploy applications on clusters. This course is designed for: - Solutions Architects - Software Developers - Operations Engineers - DevOps Engineers - Technical Managers

This course covers the internals of Kubernetes and the impacts on application architecture. Ideally you have previously attended the course "Architecting on Kubernetes" or have comparable previous... Day 1 - Control Plane Infrastructure - Mastering Etcd - Networking Deep Dive - Lab 1: Troubleshooting Kubernetes - Microservices and Decoupling - Service Mesh - Mutal TLS - Lab 2: Deploying Istio Day 2 - Automate All the Things - Batch Jobs - DaemonSets - Lab 3: Automating the Cluster - Customizing Kubernetes In this course, you will learn to: - Interact with Kubernetes’s internal database etcd. - Learn how a service mesh will advance micro-services applications. - Learn to deploy a zero-trust network. - Adopt automation best practices for their applications and clusters. - Understand the customization options. This course is intended for: - Solutions architects - Software developers - System Administrators

Intervisie is een krachtig middel om met en van elkaars ervaringen en inzichten te leren. Maar dat kan alleen als dit professioneel wordt begeleid. Zie jij jezelf als (toekomstig) begeleider van intervisiebijeenkomsten in welke professionele bezetting dan ook? Dan helpt deze training je om met gebruik van dialoogtechnieken en inzichten uit de socratische gespreksfilosofie een professionele intervisiebegeleider te worden. Je beschikt al over de basiskennis en -vaardigheden van de training ‘Begeleiden van dialogen 1 – basis‘. Deze passen we toe in een professionele setting van collega’s die op basis van casuïstiek een begeleide dialoog hebben, waaruit zij lering kunnen trekken. Inhoud Je krijgt de gelegenheid te oefenen met collegiale consultatie als intervisievorm in deze rol en doet tevens kennis op van meerdere praktische benaderingen voor intervisie. Leerdoelen verdieping dialoogvaardigheden en -inzichten. gesprekstechnieken. collegiale consultatie als intervisievorm. krachtenveldanalyse. socratische gespreksvoering. verschillende werkvormen om intervisie meer diepgang te geven. Resultaat Na deze eendaagse training ben je in staat om als intervisiebegeleider professionele intervisie te verzorgen voor teams van professionals en/of samengestelde intervisiegroepen vanuit meerdere organisaties.