AI-900: 115 Practice Questions, Dumps, Tips | PDFs (Exam Cram💡) #ai #artificialintelligence

The video begins with an introduction to the Azure AI 900 certification, emphasizing its importance as a fundamental certification in Azure Artificial Intelligence. It discusses who should pursue the certification, the exam details, question format, and how to obtain a free Microsoft Azure voucher. The presenter also guides on how to schedule the exam via Pearson VUE and Certiport and outlines the resources needed to prepare for the AI 900 exam. Additionally, the video offers a series of 115 questions covering the entire course content, providing exam tips and concept understanding. It concludes with a giveaway PDF version of the video series containing all questions and answers.

This paragraph delves into the specifics of the AI 900 exam, including the passing score, the update date of the English version, and the various sections of the exam with their respective percentages. It also addresses how to schedule the exam and the cost involved, offering a solution to get a free Microsoft voucher for the exam. The presenter discusses the question formats that can be expected in the AI 900 certification exam, such as multiple-choice and matching types, and encourages viewers to subscribe for upcoming series on real exam questions and answers.

The presenter explains how to prepare for the AI 900 exam using Microsoft Learn, which offers free study materials, hands-on labs, and videos. It also mentions the value of real exam questions and answers for thorough preparation. The video provides information on other fundamental exam series like AZ900 and DP900, and introduces a series on AZ104 with 190 real exam questions. The presenter summarizes the key points covered in the video, including the purpose of AI 900, exam details, question formats, how to get a free voucher, and how to schedule the exam using Pearson VUE.

The video explores AI services such as QnA Maker, Language Understanding, Text Analytics, and Speech to Text, illustrating their applications with examples. It discusses the retirement of QnA Maker service and provides insights into other services. The presenter also addresses questions related to responsible AI, including transparency, inclusiveness, privacy, and security, and their importance in building trust in AI systems. The video concludes with an encouragement for viewers to like, subscribe, and share the content for a wider audience reach.

This segment focuses on various machine learning techniques, specifically regression, clustering, and classification. It explains the concept of regression in predicting numeric values and when to use it, as well as the principles of responsible AI, including reliability and safety. The video also covers the use of Azure Machine Learning Designer for creating training pipelines and the importance of splitting data into training and validation sets. It concludes with a clarification on the use of Custom Vision and Cognitive Services for image classification and object detection tasks.

The video discusses the use of object detection in computer vision for locating items in images and the importance of selecting the right AI workload for different tasks. It also covers the principles of responsible AI, including inclusiveness and fairness, and their application in various scenarios. The presenter provides a detailed explanation of how to interpret a confusion matrix, a vital tool for evaluating the performance of classification models. The video concludes with an encouragement for viewers to engage with the content through likes, subscriptions, and sharing.

The video explores the capabilities of the Text Analytics service, including language detection and entity recognition. It also delves into the principles of responsible AI, specifically focusing on reliability and safety, and how they apply to AI systems. The presenter explains the importance of handling unusual or missing values in AI systems and provides a clear understanding of when to use regression and classification in machine learning. The video concludes with a series of yes/no questions related to the application of AI in various scenarios.

This segment discusses the application of AI in creating knowledge bases for chatbots, specifically using QnA Maker and Azure Bot Service. It covers the process of importing documents into a knowledge base with minimal effort. The video also explains the concept of automated machine learning, its capabilities, and its benefits in model development. It concludes with a series of yes/no questions that test the understanding of automated machine learning and its applications.

The video focuses on the use of regression in predicting numeric values, such as rainfall amounts or temperature changes, and distinguishes it from classification. It emphasizes the importance of choosing the right machine learning technique based on the type of prediction required. The presenter also discusses the principles of responsible AI, including fairness and reliability, and their significance in AI system development. The video concludes with a comprehensive review of the principles and their implications in various AI applications.

The final segment of the video discusses feature selection in machine learning models, its role in training weather models, and its importance in predicting taxi fares based on trip distance. It also addresses the principles of responsible AI, particularly reliability and safety, in the context of self-driving cars. The video concludes with a summary of the 115 questions covered in the series, providing a broad overview of the AI 900 certification exam content.