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所在平台: Udemy |
课程主页: https://www.udemy.com/course/azure-ai-fundamentals-ai-900-practice-tests/
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课程名称：Azure AI Fundamentals AI-900 - 练习测试（2025） 概述： 欢迎参加Azure AI Fundamentals AI-900练习测试课程！本课程完全更新至2025年5月2日的官方考试更新，旨在帮助您成功通过Azure AI Fundamentals（AI-900）认证考试，提供高质量的练习测试，真实反映考试内容。 课程内容： - 338道最新考试题目，并附有详细解答。 - 真实考试模拟：练习测试设计仿照官方Azure AI Fundamentals考试的格式、风格和难度，确保您获得真实的测试体验。 - 全面覆盖：练习测试涵盖AI-900考试的所有领域和目标，包括： - 描述人工智能工作负载和考虑因素 - 描述Azure上机器学习的基本原则 - 描述Azure上计算机视觉工作负载的特性 - 描述Azure上自然语言处理（NLP）工作负载的特性 - 描述Azure上生成AI工作负载的特性 - 详细解答：每道题目都附有详细解答，帮助您理解概念和正确答案背后的推理，有助于加深知识并确保您充分准备。 - 多样化的题型：包括多个选择题、多重响应题以及情景题，全面准备您应对考试当天的各种情况。 - 绩效追踪：通过测试回顾功能跟踪进度，识别自己的优势和需要改进的区域，有效聚焦学习。 课程示例题： 1. 问题关于如何将无声的教学视频脚本转化为音频，正确答案为“语音合成”，并详细解释了各个选项的对错。 2. 问题关于交通监控系统中如何从CCTV视频中提取车牌号码，正确答案为“文本提取”，同样提供了每个选项的合理解释。 通过本课程，您将获得全面的知识和丰富的练习，有助于您顺利通过Azure AI Fundamentals (AI-900) 认证考试。
** Course is fully up to date in respect to the official exam update of May 2nd, 2025**Welcome to the Azure AI Fundamentals AI-900 - Practice Test Course!Are you preparing for the Azure AI Fundamentals (AI-900) certification exam? This course is designed to help you succeed by providing high-quality practice tests that closely mirror the real exam.What You'll Get:338 Latest exam questions with detailed explanations to each answerRealistic Exam Simulation: My practice tests are designed to reflect the format, style, and difficulty of the official Azure AI Fundamentals exam. This ensures you get a realistic testing experience.Comprehensive Coverage: The practice tests cover all the domains and objectives of the AI-900 exam, including:Describe Artificial Intelligence workloads and considerationsDescribe fundamental principles of machine learning on AzureDescribe features of computer vision workloads on AzureDescribe features of Natural Language Processing (NLP) workloads on AzureDescribe features of generative AI workloads on AzureDetailed Explanations: Each question comes with a detailed explanation to help you understand the concepts and reasoning behind the correct answers. This is crucial for deepening your knowledge and ensuring you're fully prepared. For each question, I have explained why an answer is correct and have also explained why other options are incorrect. You will also find supporting reference links for a quick read.Variety of Questions: You'll find a mix of multiple-choice, multiple-response, and scenario-based questions to fully prepare you for what to expect on exam day.Performance Tracking: Keep track of your progress with the test review feature. Identify your strengths and areas for improvement to focus your study efforts effectively.Sneak peak into what you will get inside the course:Q1. You have 100 instructional videos that do NOT contain any audio. Each instructional video has a script. You need to generate a narration audio file for each video based on the script. Which type of workload should you use?language modelingspeech recognitionspeech synthesistranslationAnswer(s):speech synthesis is CORRECT because it refers to the process of generating spoken language by a machine based on written input. In this scenario, you have a script (written input) for each video, and you want to generate a narration audio file (spoken language). Therefore, speech synthesis is the appropriate workload to use.language modeling is INCORRECT because language modeling deals with predicting the next word in a sequence based on the previous words. It is commonly used in tasks like text generation or autocomplete suggestions. While it can generate text, it doesn't convert text to speech.speech recognition is INCORRECT because speech recognition is the process of converting spoken language into written text. In this scenario, you already have the script in written form and need to convert it to spoken language, which is the opposite of what speech recognition does.translation is INCORRECT because translation involves converting text or speech from one language to another. The scenario doesn't mention any requirement for translating between languages, only generating narration from a script.***Q2. Select the answer that correctly completes the following sentence. "A traffic monitoring system that collects vehicle registration numbers from CCTV footage is an example of _________________ in the Azure AI Vision service."image classificationobject detectionspatial analysistext extractionAnswer(s):text extraction is CORRECT. The objective is to obtain vehicle registration numbers from the CCTV recordings. Utilizing OCR algorithms for text extraction is the ideal option. These algorithms can identify and pull out textual data from pictures, facilitating the retrieval of registration numbers from video frames. This allows for automated tracking and assessment of traffic data by extracting pertinent textual details.image classification is INCORRECT because it involves categorizing an entire image into one of several predefined categories. It doesn't extract specific information like text from the image.object detection is INCORRECT because it identifies and locates objects within an image. While it can detect vehicles, it doesn't specifically extract text like registration numbers.spatial analysis is INCORRECT because it analyzes the movements and interactions of people within a given space. It's not focused on extracting text from images.
