Book Image

Microsoft Power Platform Functional Consultant: PL-200 Exam Guide

By : Julian Sharp
Book Image

Microsoft Power Platform Functional Consultant: PL-200 Exam Guide

By: Julian Sharp

Overview of this book

The Power Platform Functional Consultant Associate (PL-200) exam tests and validates the practical skills of Power Platform users who are proficient in developing solutions by combining the tools in Power Platform and the Microsoft 365 ecosystem based on business needs. This certification guide offers complete, up-to-date coverage of the PL-200 exam so you can prepare effectively for the exam. Written in a clear, succinct way with self-assessment questions, exam tips, and mock exams with detailed explanations of solutions, this book covers common day-to-day activities involved in configuring Power Platform, such as managing entities, creating apps, implementing security, and managing system change. You'll also explore the role of a functional consultant in creating a data model in the Microsoft Dataverse (formerly Common Data Service). Moving ahead, you'll learn how to design the user experience and even build model-driven and canvas apps. As you progress, the book will show you how to manage automation and create chatbots. Finally, you'll understand how to display your data with Power BI and integrate Power Platform with Microsoft 365 and Microsoft Teams. By the end of this book, you'll be well-versed with the essential concepts and techniques required to prepare for the PL-200 certification exam.
Table of Contents (34 chapters)
Section 1: Introduction
Section 2: Microsoft Dataverse
Section 3: Power Apps
Section 4: Automation
Section 5: Power Virtual Agents
Section 6: Integrations

Preparing data for a model

ML models are very dependent upon the datasets used to train and test the given model. A frequent problem in ML is overfitting. Overfitting means that the model does not generalize well from training data to unseen data, especially data that is unlike the training data. Common causes include the presence of  bias in the training data, meaning the model cannot distinguish between the signal and the noise.

AI Builder implements many techniques to avoid such problems, but you will need to supply AI Builder with enough data to be able to create a model. The more data and the more varied the data, the better the model will behave.

AI Builder requires the training and test data to be stored in entities in the Common Data Service. If the data does not reside in the Common Data Service, you will need to import the data. You may need to create a custom entity for this data.

AI Builder provides a set of examples and labs with sample data that you can use to...