DP-100 certificate
Hey community! I'm currently preparing the DP-100 certification with a couple of coworkers, two of them already tried a first time and couldn't approve it although they completed the learning path and consistently scored 90+ on the test exam. What they have told me is that the real exam has questions that are a lot harder, and especially, questions that are not really answerable with the materials from the learning path, they say that they saw many deep questions about DevOps and other questions of resources of Azure that are not really a part of Azure Machine Learning or Foundry. I wanted to ask if anyone had a similar experience with this, I thought the exam was centered on the use of azure machine learning (and AI foundry). Can the exam contain questions that are not related with azure machine learning or foundry? Would really appreciate help here! Thanks everyone!Understanding the Fundamentals of AI Concepts for Nonprofits
Artificial Intelligence (AI) has become a cornerstone of modern technology, driving innovation across various sectors. Nonprofits, too, can harness the power of AI to enhance their operations and amplify their impact. In this blog, we'll explore fundamental AI concepts, common AI workloads, Microsoft's Responsible AI policies, and the tools and services available through Azure AI, all tailored for the nonprofit sector. Understanding AI Workloads AI workloads refer to the different types of tasks that AI systems can perform. Here are some common AI workloads relevant to nonprofits: Machine Learning: This involves training a computer model to make predictions and draw conclusions from data. Nonprofits can use machine learning to predict donor behavior, optimize fundraising strategies, and analyze program outcomes. Computer Vision: This capability allows software to interpret the world visually through cameras, video, and images. Applications include identifying and tracking wildlife for conservation efforts or analyzing images to assess disaster damage. Natural Language Processing (NLP): NLP enables computers to understand and respond to human language. Nonprofits can use NLP for sentiment analysis of social media posts, language translation for multilingual communities, and developing conversational AI like chatbots for donor engagement. Anomaly Detection: This involves automatically detecting errors or unusual activity. It is useful for fraud detection in financial transactions, monitoring network security, and ensuring data integrity. Conversational AI: This refers to the capability of a software agent to engage in conversations with humans. Examples include chatbots and virtual assistants that can answer questions, provide recommendations, and perform tasks, enhancing donor and beneficiary interactions. Responsible AI Practices As AI technology continues to evolve, it is crucial to ensure it is developed and used responsibly. Microsoft's Responsible AI policies emphasize the importance of fairness, reliability, safety, privacy, security, inclusiveness, transparency, and accountability in AI systems. These principles guide the development and deployment of AI solutions to ensure they benefit everyone and do not cause harm. To learn more about Microsoft Responsible AI Practices click here: Empowering responsible AI practices | Microsoft AI Azure AI Services for Nonprofits Microsoft Azure offers a suite of AI services that enable nonprofits to build intelligent applications. Some key services include: Azure Machine Learning: A comprehensive platform for building, training, and deploying machine learning models. It supports a wide range of machine learning frameworks and tools, helping nonprofits analyze data and make informed decisions. To learn more or get started with Azure Machine Learning click here: Azure Machine Learning - ML as a Service | Microsoft Azure Azure AI Bot Service: A service for building conversational AI applications. It provides tools for creating, testing, and deploying chatbots that can interact with users through various channels, improving donor engagement and support services. To learn more or get started with Azure AI Bot Service click here: Azure AI Bot Service | Microsoft Azure Azure Cognitive Services: A collection of APIs that enable developers to add AI capabilities to their applications. These services include vision, speech, language, and decision-making APIs, which can be used for tasks like image recognition, language translation, and sentiment analysis. To learn more about the various Cognitive Service please click here: Azure AI Services – Using AI for Intelligent Apps | Microsoft Azure Conclusion AI has the potential to transform the nonprofit sector by enhancing efficiency, driving innovation, and providing valuable insights. By understanding AI workloads, adhering to responsible AI practices, and leveraging Azure AI services, nonprofits can unlock the full potential of AI to better serve their communities and achieve their missions. Embrace the power of AI to take your nonprofit organization to new heights and make a greater impact. For a deeper dive into the fundamental concepts of AI, please visit the module Fundamental AI Concepts. This resource will provide you with essential insights and a solid foundation to enhance your knowledge in the ever-evolving field of artificial intelligence.
Leveraging Power Platform Connectors in Copilot Studio for Nonprofits
Nonprofits can greatly benefit from using Microsoft Power Platform connectors in Copilot Studio. These connectors act as proxies or "wrappers" around APIs, enabling Microsoft Copilot Studio, Microsoft Power Automate, Microsoft Power Apps, and Azure Logic Apps to interact with various apps and services. By using connectors, nonprofits can streamline their operations, automate workflows, and enhance their engagement with stakeholders. What Are Power Platform Connectors? Connectors allow you to access a wide range of services, both within and outside the Microsoft ecosystem, to perform various tasks automatically. These connectors are categorized into: Standard Connectors: Included with all Copilot Studio plans (e.g., SharePoint). Premium Connectors: Available in select Copilot Studio plans. Custom Connectors: Enable connections to any publicly available API for services not covered by existing connectors. Integration with Copilot Studio Microsoft Power Platform connectors are essential tools that extend the functionality of Copilot Studio agents by connecting to various external services and applications. This integration allows nonprofits to create more dynamic, responsive, and useful agents tailored to their specific needs and processes. You can call connectors as connector actions in your agent, from an Action node in conversational topics, and through cloud flows as actions or within topics. Adding a Connector Action Select Add Node: On the authoring canvas, click the Add node (+) icon. Choose Connector: In the node selection window, select Call an action > Connectors (preview), and search for the connector you want to add. Configure Inputs and Outputs: Set up the required and optional inputs and outputs for your experience. By default, the connection is configured to use user credentials. For more information about supported authentication modes, see the section on configuring user authentication for actions. Using Connectors with Agent Author's Credentials Connector actions require valid credentials. By default, these actions ask users to provide their credentials for the associated service when invoked. To use the agent author's credentials or a proxy account's credentials: Configure Authenticated Channel: Set up your agent to use an authenticated channel. Add Connector Action: Add a connector action to your agent as a plugin action and configure it. Set Authentication Method: Go to the connector action properties and under End user authentication, select Agent author authentication. Publish and Test: Publish the changes and test the experience in the Test your agent pane or your desired channel. Sharing Connections To share your connection with others: Navigate to PowerApps: Go to make.powerapps.com. Select Connections: In the left navigation bar, select Connections. Share Connection: Choose the connection, click Share, search for the desired user, and select them. Set Permissions: Under Permission, next to the user, select Can use + share. Practical Applications for Nonprofits Automated Donor Management: Use connectors to integrate with CRM systems like Dynamics 365 or Salesforce to automate donor communications and manage donor data efficiently. Volunteer Coordination: Connect to scheduling tools and communication platforms to streamline volunteer sign-ups, scheduling, and notifications. Event Management: Integrate with event management services to automate the planning, coordination, and follow-up for fundraising events. Resource Allocation: Use connectors to manage and track resources, ensuring they are allocated efficiently to various projects and initiatives. Social Media Engagement: Connect to social media platforms like Twitter and Facebook to automate posts, track engagement, and respond to inquiries. By leveraging Power Platform connectors, nonprofits can enhance their operational efficiency, improve stakeholder engagement, and focus more on their mission-driven activities.Scaling AI: Strategies for Transforming Your Nonprofit
Scaling AI in a nonprofit context involves integrating AI technologies across various functions to maximize benefits such as improved decision-making, increased productivity, and enhanced service delivery. This can help nonprofits better serve their communities and achieve their missions more effectively. Key Strategies for Scaling AI in Nonprofits 1. Develop a Clear AI Strategy: Align the AI strategy with the nonprofit's mission and goals. Identify specific AI use cases that can drive value, such as automating administrative tasks, improving donor management, or enhancing service delivery. Create a roadmap for implementation, detailing resources, timelines, and milestones. Microsoft Tools that can help: Azure AI: Helps in developing and implementing AI strategies aligned with organizational goals. Power BI: Assists in identifying and visualizing AI use cases and creating implementation roadmaps. 2. Invest in AI Infrastructure: Utilize scalable cloud platforms like Azure to support data processing, storage, and analysis. Leverage tools like Azure Machine Learning to develop and deploy AI models efficiently. Microsoft Tools that can help: Azure: Provides scalable cloud infrastructure for data processing and storage. Azure Machine Learning: Facilitates the development and deployment of AI models. 3. Foster a Data-Driven Culture: Promote data literacy within the organization and encourage staff to use data in decision-making processes. Provide training and resources on AI and data analytics to build a data-driven culture. Microsoft Tools that can help: Microsoft Learn: Offers training resources on AI and data analytics. Power BI: Enhances data literacy by providing tools for data visualization and analysis. 4. Leverage Pre-Built AI Solutions: Use pre-built AI solutions and services to accelerate the scaling process. For example, Azure Cognitive Services offers APIs for vision, speech, language, and decision-making that can be integrated into nonprofit applications without building models from scratch. Microsoft Tool that can help: Azure AI Services: Provides pre-built AI solutions for vision, speech, language, and decision-making. 5. Implement Governance and Ethical Guidelines: Establish governance and ethical guidelines to ensure responsible and transparent use of AI. Address concerns related to bias, privacy, and accountability by following frameworks like Microsoft's Responsible AI principles. Microsoft Tool that can help: Microsoft Responsible AI: Offers frameworks and guidelines for ethical AI use. Real-World Examples of Scaling AI in Nonprofits: Disaster Response: AI can improve efficiency in disaster response by automating data collection and analysis, enabling faster and more accurate decision-making. Education: Nonprofits focused on education can use AI to provide personalized support to students, such as automating responses to common inquiries and offering tailored learning resources. Fundraising: AI can enhance fundraising efforts by analyzing donor data to identify potential major donors and optimize fundraising campaigns. Conclusion Scaling AI in a nonprofit organization requires a strategic approach, robust infrastructure, and a data-driven culture. By developing a clear AI strategy, investing in the right tools and technologies, and fostering an environment that embraces data and AI, nonprofits can unlock the full potential of AI and drive significant impact. Embrace the power of AI to take your nonprofit organization to new heights and better serve your community. For more detailed information and a comprehensive guide on scaling AI in your organization, you can explore the Scale AI in your organization module on Microsoft Learn.How Nonprofits Can Benefit from DeepSeek R1 on Azure AI Foundry and GitHub
In the ever-evolving landscape of technology, nonprofits often face unique challenges in leveraging advanced tools to enhance their operations and impact. The recent release of DeepSeek R1 on Azure AI Foundry and GitHub presents a remarkable opportunity for nonprofits to harness the power of cutting-edge AI. This blog explores how nonprofits can benefit from this innovative model and integrate it into their workflows to drive greater efficiency and effectiveness. What is DeepSeek R1? DeepSeek R1 is a powerful AI model that focuses on logical inference, mathematical problem-solving, and real-time decision-making. It is part of a diverse portfolio of over 1,800 AI models available on Azure AI Foundry, including frontier, open-source, industry-specific, and task-based models. By making DeepSeek R1 accessible on both Azure AI Foundry and GitHub, Microsoft aims to democratize advanced AI capabilities, enabling organizations of all sizes to integrate AI seamlessly into their operations. Benefits for Nonprofits Enhanced Decision-Making: DeepSeek R1's advanced reasoning capabilities can help nonprofits make more informed decisions. Whether it's optimizing resource allocation, planning fundraising campaigns, or analyzing program outcomes, this model can provide valuable insights to guide strategic choices. Improved Efficiency: Nonprofits often operate with limited resources and staff. By automating complex tasks and processes, DeepSeek R1 can free up valuable time for nonprofit teams to focus on their core mission. For example, the model can assist in data analysis, grant application reviews, and volunteer coordination. Cost-Effective Solutions: One of the key advantages of using DeepSeek R1 on Azure AI Foundry is the cost-efficiency it offers. Nonprofits can leverage state-of-the-art AI capabilities without significant infrastructure investment. The model's availability on GitHub also provides access to a wealth of resources and step-by-step guides to facilitate integration. Scalability and Flexibility: As nonprofits grow and their needs evolve, DeepSeek R1 can scale to meet increasing demands. The model's integration with Azure AI Foundry ensures a trusted, scalable, and enterprise-ready platform, allowing nonprofits to expand their AI capabilities as needed. Security and Compliance: Microsoft has implemented rigorous safety evaluations and security reviews for DeepSeek R1, ensuring that nonprofits can deploy AI solutions confidently. Built-in content filtering and the Safety Evaluation System provide additional safeguards to maintain a secure and compliant environment. Getting Started with DeepSeek R1 To start using DeepSeek R1, nonprofits can follow these steps: Sign Up for Azure: If you don't have an Azure subscription, you can sign up for an Azure account to access Azure AI Foundry. Explore the Model Catalog: Search for DeepSeek R1 in the model catalog on Azure AI Foundry. Open the model card to obtain the inference API and key and access the playground to try out prompts. Create the Resources: Azure AI model inference is a capability in Azure AI Services resources in Azure. You can create model deployments under the resource to consume their predictions. 4. Give the project a name, for example "my-project". 5. Once completed, your project is ready to be configured: Azure AI model inference is a Preview feature that needs to be turned on in Azure AI Foundry. At the top navigation bar, over the right corner, select the Preview features icon. A contextual blade shows up at the right of the screen. Turn on the Deploy models to Azure AI model inference service feature. 6. Add DeepSeek-R1 model deployment: Go to Model catalog section in Azure AI Foundry portal and find the model DeepSeek-R1 model. Accept the terms on those cases by selecting Subscribe and deploy. 7. You can set deployment options now. By default, the deployment is named after the model. This name helps route requests to the right model and can be customized. 8. Azure picks a default AI Services connection for your project. Use Customize to change it. DeepSeek-R1 runs under the Global Standard type for better speed and performance. 9. Select Deploy. 10. Use the model in playground: You can get started by using the model in the playground to have an idea of the model capabilities. -On the deployment details page, select Open in playground option in the top bar. -In the Deployment drop down, the deployment you created has been automatically selected. -Configure the system prompt as needed. In general, reasoning models don't use system messages in the same way that other types of models. 11. Use the model in code: Use the Azure AI model inference endpoint and credentials to connect to the model: 12. You can use the Azure AI Inference package to consume the model in code: By embracing DeepSeek R1, nonprofits can unlock new possibilities and drive greater impact in their communities. This powerful AI model offers a cost-effective, scalable, and secure solution to enhance decision-making, improve efficiency, and support the mission-driven work of nonprofit organizations. References: DeepSeek R1 is now available on Azure AI Foundry and GitHub | Microsoft Azure BlogModernizing Legacy Applications in your Nonprofit
In this blog, we’ll explore how nonprofits can modernize their existing applications to enhance security without starting from scratch. By leveraging Microsoft Azure’s powerful tools, organizations can strengthen their defenses, improve performance, and ensure their applications remain secure and scalable for the future. Securing Legacy Applications Without Rebuilding from Scratch For many nonprofits, starting over isn’t an option—they need to secure and modernize the applications they already have. Fortunately, Microsoft Azure provides solutions that help organizations enhance security without requiring a complete rebuild: ✅ Containerization with Azure Kubernetes Service (AKS) – Nonprofits can containerize legacy applications and host them in a secure, scalable environment, reducing vulnerabilities without rewriting the entire application. This approach helps keep security updates and compliance requirements in check while maintaining the existing software functionality. ✅ Incremental Modernization with Cloud-Native Services – Instead of a full-scale rebuild, nonprofits can gradually modernize their applications by integrating cloud-native services. This could involve migrating databases to Azure SQL, implementing API-driven architectures, or introducing automation through Azure Logic Apps. This phased approach enhances security, improves performance, and allows for future scalability without disrupting core operations. ✅ Azure SQL Database – Helps nonprofits move from outdated, on-premises databases to a fully managed cloud database, reducing maintenance efforts while improving security, performance, and compliance. ✅ Azure API Management – Allows organizations to connect legacy systems with modern cloud-based services by securely exposing APIs, enabling seamless integration and extended functionality. Understanding Your Options When considering the modernization of legacy applications, there are several strategies that organizations can adopt, each with its own benefits and considerations: Rehost (Lift-and-Shift) This strategy is all about speed and simplicity. It involves moving applications from their current environment to a new one with minimal or no changes to the code. This allows organizations to quickly transition to the cloud without altering the core functionality of their applications. Replatform Replatforming sits between rehosting and refactoring. It requires making some code changes so that applications can take advantage of cloud technologies. This approach allows organizations to benefit from cloud capabilities without needing a complete overhaul of their applications. Refactor (or Repackage) Refactoring focuses on enhancing productivity and speed by making minimal code changes. This strategy ensures that applications can connect easily to a cloud-first environment, optimizing their performance and scalability. Rearchitect For organizations that need enhanced cloud scalability, rearchitecting is the way to go. This approach involves modifying and extending the application's functionality and code to better utilize cloud resources, ensuring improved performance and scalability. Rebuild (or Rewrite) When existing applications have limited functionality or lifespan, rebuilding them using cloud solutions might be necessary. Although this approach requires significant effort, it provides a fresh start with modern capabilities and extended lifespans. Replace If an application no longer meets current or future business needs, even after rebuilding, replacing it with a ready-made solution may be the best option. This approach can be quicker than rebuilding and allows organizations to focus on other priorities. However, it may also pose challenges such as business process interruptions and limitations on future modernization efforts. Nonprofit Considerations Wrapping up, we agree that nonprofits rely on technology to drive their missions, but outdated applications can pose serious security risks. We've covered how organizations don’t have to start from scratch to modernize and secure their systems. By leveraging Microsoft Azure’s powerful tools—like containerization, cloud-native services, and secure database management—nonprofits can enhance security, improve performance, and ensure long-term scalability. Here is one thing to consider: Nonprofits may not have the technical team to assist with these processes, but understanding these strategies is crucial. This knowledge can empower them in conversations with development partners, ensuring they are fully aware and engaged throughout the modernization journey. By being informed, nonprofits can make better decisions, ask the right questions, and collaborate effectively with their partners to achieve their modernization goals. Modernization isn’t just about keeping up with technology; it’s about protecting the trust nonprofits have built with their donors, volunteers, and communities. Whether it’s securing legacy applications or embedding security into new software development through the Secure Software Development Lifecycle (SSDLC), taking proactive steps today ensures a more resilient and secure future. On the contrary, for nonprofits that do want to start over from scratch with building new applications, integrating security from the start is essential. Learn more about how SSDLC can strengthen your organization’s software security here: Building Secure Software from the Ground Up: Why It Matters for Nonprofits | Microsoft Community Hub Want to explore nonprofit application modernization further? Check out this guide: What is Application Modernization? | Microsoft Azure.Fine-Tune and Integrate Custom Phi-3 Models with Prompt Flow in Azure AI Studio
Phi-3 is a family of small language models (SLMs) developed by Microsoft that delivers exceptional performance and cost-effectiveness. In this tutorial, you will learn how to fine-tune the Phi-3 model and integrate the custom Phi-3 model with Prompt flow in Azure AI Studio. By leveraging Azure AI / ML Studio, you will establish a workflow for deploying and utilizing custom AI models.Fine-Tune and Integrate Custom Phi-3 Models with Prompt Flow: Step-by-Step Guide
In this tutorial, you will learn how to fine-tune the Phi-3 model and integrate it with Prompt Flow. By leveraging Azure Machine Learning, and Prompt flow you will establish a workflow for deploying and utilizing custom AI models.Evaluating Generative AI Models with Azure Machine Learning
LLM evaluation assesses the performance of a large language model on a set of tasks, such as text classification, sentiment analysis, question answering, and text generation. The goal is to measure the model's ability to understand and generate human-like language.
