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  • Integrate Dataflows Gen2 and Pipelines in Microsoft Fabric

    Dataflows Gen2 provide an excellent option for data transformations in Microsoft Fabric. The combination of dataflows and pipelines is useful when you need to perform additional operations on the transformed data.

    Data pipelines are a common concept in data engineering and offer a wide variety of activities to orchestrate. Some common activities include:

    • Copy data
    • Incorporate Dataflow
    • Add Notebook
    • Get metadata
    • Execute a script or stored procedure

    Pipelines provide a visual way to complete activities in a specific order. You can use a dataflow for data ingestion, transformation, and landing into a Fabric data store. Then incorporate the dataflow into a pipeline to orchestrate extra activities, like execute scripts or stored procedures after the dataflow has completed.

  • GitHub Copilot on GitHub.com

    GitHub Copilot extends beyond your local development environment to provide AI assistance directly on GitHub.com. When working with repositories, issues, pull requests, and discussions on the GitHub web interface, you can leverage Copilot’s capabilities to streamline your workflow and enhance collaboration.

    In this unit, we’ll cover:

    • How to access GitHub Copilot on GitHub.com
    • GitHub Copilot agent tasks on GitHub.com
    • Repository exploration and documentation
    • Pull request assistance
    • Issue management
    • Code review and collaboration
    • GitHub Copilot error explanation in GitHub Actions

    Accessing Copilot on GitHub.com

    Copilot is integrated throughout the GitHub web interface, appearing as a chat button or inline suggestions in various contexts. You can access Copilot features in several areas:

    • Repository pages – Get explanations of code, documentation, and project structure
    • Issues and pull requests – Generate summaries, suggest solutions, and draft responses
    • Discussions – Help formulate responses and provide technical insights
    • Code review – Analyze changes and suggest improvements

    GitHub Copilot agent tasks on GitHub.com

    When using Copilot on GitHub.com, you can perform various agent-driven tasks:

    Screenshot showing various GitHub Copilot agent tasks available on GitHub.com including repository exploration, pull request assistance, and issue management.

    These tasks can run in the background for you while you focus on other work.

    Repository exploration and documentation

    • Code explanation: Ask Copilot to explain complex code sections, functions, or entire files
    • Project overview: Get AI-generated summaries of repository purpose, architecture, and key components
    • Documentation generation: Create or improve README files, API documentation, and code comments

    Example: “Explain the main functionality of this repository and its key components”

    Screenshot of GitHub Copilot providing code explanation and repository overview on a GitHub repository page.

    Pull request assistance

    GitHub Copilot on GitHub.com significantly accelerates your pull request workflow by automating many time-consuming review and documentation tasks:

    • PR summaries: Generate comprehensive summaries of changes made in a pull request, helping reviewers quickly understand the scope and impact of modifications
    • Review suggestions: Get recommendations for code improvements and potential issues before formal review, reducing review cycles
    • Merge conflict resolution: Receive guidance on resolving conflicts between branches, streamlining the merge process
    • Documentation updates: Automatically suggest updates to README files, changelogs, and other documentation based on code changes
  • GitHub Copilot Chat

    GitHub Copilot Chat is an advanced feature of the GitHub Copilot ecosystem, designed to provide developers with an interactive, conversational AI assistant directly within their development environment. It allows developers to have natural language conversations about their code, ask questions, and receive intelligent responses and suggestions in real-time. In this unit, we cover:

    • How to generate code using GitHub Copilot Chat.
    • Debugging using GitHub Copilot Chat.
    • How to get code explanations using GitHub Copilot Chat.
    • Using slash commands to perform actions with GitHub Copilot.
    • Using custom GitHub Copilot agents to improve prompts.

    To access Copilot in your Integrated Development Environment(IDE), click the chat icon on the left navbar.

    Screenshot of Chat.

    GitHub Copilot Chat is beneficial in certain scenarios:

    • Complex code generation When you need to implement complex algorithms, data structures, or generate boilerplate code for specific design patterns, Copilot Chat can help streamline the process. It can help create intricate regular expressions, construct detailed SQL queries, or develop advanced data structures like a Bubble sort in Python.
  • Introduction

    GitHub Copilot is an advanced AI-powered coding assistant that can dramatically enhance developer efficiency across every stage of your development workflow. GitHub Copilot saves time for developers, enabling them to concentrate on higher-level problem-solving and innovation by automating routine tasks, providing relevant code completion, and generating entire blocks of code that accelerate development cycles from initial coding to pull request completion.

    GitHub Copilot offers flexible interaction options tailored to your workflow, meeting you wherever you work in your development environment. Whether through code completion in your IDE, conversational chat for complex problem-solving, collaborative features on GitHub.com, or command-line assistance, Copilot seamlessly integrates across environments to enhance your development experience and productivity. Understanding these interaction modes is key to unlocking the full potential of GitHub Copilot and optimizing your coding workflow to rapidly deliver quality code changes.

    This module covers the different methods for interacting with GitHub Copilot, guiding you on when, where, and how to use these methods to effectively communicate your goals to Copilot and provide it with the necessary information to complete your tasks.

    In this module you’ll learn about:

    • How to utilize GitHub Copilot’s auto-suggestions, multiple suggestions pane, and its ability to adapt to different coding styles to accelerate development workflows.
    • How to provide context to GitHub Copilot through inline comments, block comments, docstrings, and other types of comments to enhance code generation accuracy and speed.
    • Interacting with GitHub Copilot through natural language conversations to generate complex code, debug issues, obtain code explanations, and streamline development tasks in real-time.
    • How to improve the relevance of GitHub Copilot Chat’s suggestions by using scope referencing, slash commands, and agents to quickly complete routine tasks.
    • Leveraging GitHub Copilot on GitHub.com for repository exploration, pull request assistance, agent tasks and collaborative code review workflows.
    • How to interact with GitHub Copilot in CLI to get command explanations, suggestions, and execute commands to automate terminal workflows.
    • How to set up GitHub Copilot CLI configurations, aliases, and m
  • Code completion with GitHub Copilot

    GitHub Copilot code completion features live directly within your IDE, where you write and review your code. GitHub Copilot integrates seamlessly with editors like Visual Studio Code or JetBrains, offering features such as autosuggestions, a multiple suggestions pane, and support for various coding styles. You primarily interact with GitHub Copilot through these IDE tools, and understanding how and where to use them helps you optimize its powerful code generation abilities.

    In this unit, we cover:

    • GitHub Copilot supported languages
    • Auto suggestions
    • Multiple suggestions pane
    • Support for different coding styles in suggestions
    • How GitHub Copilot incorporates coding comments for suggestions

    GitHub Copilot supported languages

    GitHub Copilot provides robust support for a wide range of programming languages and frameworks, with strong capabilities in:

    • Python
    • JavaScript
    • Java
    • TypeScript
    • Ruby
    • Go
    • C#
    • C++

    While these languages receive exceptional support, GitHub Copilot can assist with many other languages and frameworks as well.

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  • Describe the capabilities of Copilot in the Dynamics 365 customer engagement apps

    Artificial intelligence (AI) is changing how companies do business. Not only can it provide organizations with important insights, but it also helps streamline how individuals do their work. Microsoft’s Copilot uses AI to allow individuals to communicate their needs using natural language and provides them with relevant assistance based on the details they provide.

    Copilot can be used throughout Dynamics 365 customer engagement applications. Copilot can help users easily retrieve information about their customers and streamline things such as data entry and task completion.

    Microsoft 365 Copilot for Sales

    Copilot for Sales is an AI assistant designed for sellers to maximize productivity and close more deals. It brings next-generation AI and sales insights from their CRM platform into the Microsoft 365 productivity tools that they use daily like Microsoft Outlook, Microsoft Teams, and Microsoft Word.

    Sellers can work more efficiently and improve customer experiences with email assistance, personalized sales content creation, AI-generated insights, and recommendations for next steps. Sales managers can empower their teams with AI-powered manager insights and collaboration tools. Copilot for Sales can also be customized according to your business needs. Copilot for Sales integrates seamlessly with Microsoft Dynamics 365 Sales and Salesforce Sales Cloud and can be configured to connect to other sales solutions.

    Copilot in Dynamics 365 Sales

    Copilot is an AI assistant in Dynamics 365 Sales that helps sales teams be more productive and efficient in their daily work. It has a chat interface that helps sellers quickly summarize their opportunity and lead records, stay updated on recent changes, prepare for meetings, and read the latest news about their accounts. Sellers can chat with Copilot in natural language or use predefined prompts to get the information they need.

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  • Pillars of a great architecture

    The cloud has changed how business applications for organizations are designed and implemented. As a result, solution architectures can now be pulled together from one or more SaaS services that are working together to form a complete solution. In solving customer’s business problems, solution architects should be comfortable using the following services to build their overall solution:

    • Dynamics 365
    • Microsoft 365
    • AppSource
    • Extending with Microsoft Power Platform
    • Microsoft Azure (used to fill in the remaining gaps)
    • Microsoft Copilot

    Design a great business application solution

    While one single blueprint doesn’t exist for illustrating what a great business application solution architecture looks like, certain concepts apply, regardless of the unique customer challenges that you’re solving. While the following sections don’t provide a complete list, focusing on these concepts helps you build better overall solutions.

    Security

    Data is one of the most valuable assets of an organization, and ensuring proper use and access to this data is essential. In the security pillar, you’re focused on securing access to your architecture through authentication and protecting your application and data from network vulnerabilities. This process includes ensuring that you’re working with the appropriate teams to enable features like Azure Conditional Access and Data Loss Prevention policies. Additionally, you confirm appropriate use of your solution of secrets, certificates, and other techniques to ensure that access to data and services don’t fall into the wrong hands.

    You must think about security throughout the entire life cycle of your application, from design and implementation to deployment and operations.

    Customers entrust their data to your organization; you must ensure that only the right users have access. Beyond perimeter control through authentication, you must implement a security model that enforces access to the data that users are allowed to use. You must ensure that the security constructs that are established don’t place undue burden on your architects and prevent staff from doing their job, thus making the system unmaintainable.

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  • Solution architect role during project phases

    A solution architect’s daily role on a customer project will vary greatly with the size, approach, and phase of the project. This unit explores some of the common project activities that a solution architect will either be responsible for or, at a minimum, participate in.

    The discussion here will be without regard to a specific implementation methodology.

    Presales

    The primary activity of presales is supporting the sales team on landing the project. Occasionally, the solution architect is dedicated to presales activities. In other cases, solution architects are only pulled in as needed. With presales, the focus is on the minimal effort that is required to land the project while ensuring that the sales team does not oversell what you can deliver. Activities during this phase of the engagement can primarily be categorized as follows:

    • Request for Proposal (RFP) responses – Handling tough technical questions that the sales team could not complete and reviewing other’s responses to ensure that they are feasible and aligned with the level of effort estimated.
    • Introductory customer meetings – Typically, along with the account team, the solution architect can participate as a technical resource to help field questions on possible solutions or outcomes. For the solution architect, this situation is a great opportunity to learn about the customer’s current environment, needs, and their desired outcomes. Later in this learning path, you will explore techniques to prepare for these meetings and how to make the most of the encounter.
    • Proof of concepts /demos – While the solution architect might not be the one building the POC/demo, they are often indispensable in helping to envision and craft what the POC or demo will highlight. Solution architects are expected to have a deep awareness of the different pre-built applications and available integrations that can be combined to highlight a proposed solution. They should also help the team decide what parts of a proposed solution are worth building out to highlight the proposed approach.
    • Solution envisioning – While this might be part of a customer meeting, it can also happen independently to generate ideas on how to approach a customer’s problem. Often, the presales stage of a project will be high-level, but it can also be done later in the project during each of the sprints in a more detailed exercise. Solution envisioning is simply considering the customer’s needs and then developing those needs into a concept of a proposed solution.

    A key skill for a solution architect in this phase of a project is the ability to communicate with all levels of a customer’s organization. Often, solution architects translate technical topics into a more business focused discussion.

    Solution architects must also be skilled at answering questions in a way that is accurate but does not create two more questions for every one answered.

    While solution architects are not required to be an expert on product licensing, they should have a grasp of the implications of licensing for their proposed solutions. In some cases, they might be required to architect within the license constraints for what a customer has purchased or is willing to purchase.

    In this phase, a skilled solution architect can spot opportunities that the rest of the sales team might have missed and then highlight these opportunities to the team to allow upsell of services. Alternatively, solution architects can suggest different approaches that might better address a requirement or need.

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  • Augment cognition with generative AI

    Generative AI can benefit public sector organizations by increasing comprehension and learning and augmenting cognition, especially in the following areas:

    • Helping fraud investigators find evidence by extracting insights from data
    • Performing multimodal image analysis by gathering insights quickly from open-source intelligence with AI processing
    • Creating knowledge hubs to organize repositories, surface insights, and empower teams to find information more efficiently

    Augmented cognition through AI provides a digital sidekick that helps employees think better and handle complex tasks easily.

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  • Enhance productivity with generative AI

    One of the ways that AI can make a significant impact in the public sector is by enhancing productivity. When public sector organizations use AI, they can empower the workforce and open up new avenues of efficiency and creativity in areas like:

    • Simplifying case management by helping case workers manage heavy workloads with AI-generated drafts, insights, and automation
    • Simplifying rule making and accelerating the process by using AI to help draft and refine policies, gather public comments, and then analyze the comments
    • Empowering constituents to engage with public sector organizations more effectively, which results in shorter wait times, faster resolution, and informed support
    • Helping IT teams migrate legacy apps by documenting their functionalities, translating legacy code, and creating translations of app functions

    Let’s explore each one of these use cases.

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