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Microsoft AI-900 Exam, also known as the Microsoft Azure AI Fundamentals exam, is a certification exam that tests individuals on their knowledge of artificial intelligence and its applications in Microsoft Azure. AI-900 exam is designed for individuals who are interested in learning about AI and its practical use in the cloud, and for those who are looking to gain a basic understanding of AI terminology, concepts and technologies.
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Microsoft AI-900, also known as the Microsoft Azure AI Fundamentals Exam, is a certification exam offered by Microsoft that assesses an individual's knowledge of artificial intelligence (AI) and machine learning (ML) concepts and their ability to apply them using Microsoft Azure technologies. AI-900 Exam is intended for individuals who are interested in understanding AI and ML technologies and their applications in the Azure ecosystem. The AI-900 exam is considered an entry-level certification and is a great starting point for those who want to build a career in AI and ML.
NEW QUESTION # 60
You are processing photos of runners in a race.
You need to read the numbers on the runners' shirts to identify the runners in the photos. Which type of computer vision should you use?
Answer: C
Explanation:
The correct answer is B. Optical Character Recognition (OCR).
Optical Character Recognition (OCR) is a feature of Azure AI Vision that converts printed or handwritten text within images into machine-readable text. In this scenario, the goal is to read runner numbers on shirts from race photos. OCR can identify and extract these numbers, allowing them to be associated with specific participants.
Option analysis:
* A. Image classification: Categorizes entire images (e.g., "runner," "crowd"), not text.
* B. Optical Character Recognition (OCR) - # Correct. Extracts alphanumeric text from images.
* C. Object detection: Identifies and locates objects (e.g., shoes, cars) but doesn't read text.
* D. Facial recognition: Identifies individuals by matching facial features to known identities, not by reading numbers.
Therefore, to read and extract runner numbers from photos, the correct computer vision technique is Optical Character Recognition (OCR).
NEW QUESTION # 61
You are developing a conversational AI solution that will communicate with users through multiple channels including email, Microsoft Teams, and webchat.
Which service should you use?
Answer: A
Explanation:
According to the Microsoft Azure AI Fundamentals official study guide and Microsoft Learn module
"Describe features of conversational AI workloads on Azure", Azure Bot Service is the core Azure platform for building, testing, deploying, and managing conversational agents or chatbots. These bots can communicate with users across multiple channels, including email, Microsoft Teams, Slack, Facebook Messenger, and webchat.
Azure Bot Service integrates deeply with the Bot Framework SDK and Azure Cognitive Services such as Language Understanding (LUIS) or Azure AI Language, enabling natural language processing and multi- channel message delivery. The service abstracts away channel management, meaning that developers can build one bot logic that connects seamlessly to several communication platforms.
Option analysis:
* A. Text Analytics is a Cognitive Service used for text mining tasks like key phrase extraction, language detection, and sentiment analysis - not for building chatbots.
* C. Translator provides language translation but cannot manage conversations or multi-channel delivery.
* D. Form Recognizer extracts structured information from documents and forms - unrelated to conversational interaction.
The AI-900 course explicitly defines Azure Bot Service as "a managed platform that enables intelligent, multi- channel conversational experiences between users and bots." This service allows businesses to unify chat experiences across multiple digital communication channels.
Thus, based on the official Microsoft Learn content and AI-900 syllabus, the best and verified answer is B.
Azure Bot Service, as it is the designated Azure solution for deploying a single conversational AI experience accessible from multiple platforms such as email, Teams, and webchat.
NEW QUESTION # 62
You need to build an image tagging solution for social media that tags images of your friends automatically.
Which Azure Cognitive Services service should you use?
Answer: A
NEW QUESTION # 63
Your company is exploring the use of voice recognition technologies in its smart home devices. The company wants to identify any barriers that might unintentionally leave out specific user groups.
This an example of which Microsoft guiding principle for responsible AI?
Answer: A
Explanation:
Reference:
https://docs.microsoft.com/en-us/learn/modules/responsible-ai-principles/4-guiding-principles AI systems should empower everyone and engage people. AI should bring benefits to all parts of society, regardless of physical ability, gender, sexual orientation, ethnicity, or other factors.
https://docs.microsoft.com/en-us/learn/modules/get-started-ai-fundamentals/7-understand-responsible-ai
NEW QUESTION # 64
To complete the sentence, select the appropriate option in the answer area.
Answer:
Explanation:
Explanation:
According to the Microsoft Azure AI Fundamentals (AI-900) official study guide and the Microsoft Learn module "Describe core concepts of machine learning on Azure", labeling is the process of assigning correct output values (labels) to training data before model training. In supervised learning, every input in the dataset must be paired with its corresponding output so the algorithm can learn the relationship between the two.
In this scenario, the task is to assign classes to images before training a classification model-for example, marking images as "cat," "dog," or "bird." This process defines the target variable (label) that the model will later predict. During training, the classification model uses these labeled examples to learn patterns and distinguish between categories.
Microsoft's official materials clearly define labeling as:
"The process of tagging data with the correct answer so that the model can learn to make predictions." Labeling is a crucial early step in the machine learning lifecycle, especially for image classification and natural language processing (NLP) tasks. Without accurate labels, the model cannot learn correctly and its predictions will be unreliable.
Let's briefly clarify why the other options are incorrect:
* Evaluation refers to testing the model after training to measure accuracy or performance using metrics like precision, recall, or F1 score.
* Feature engineering involves creating or selecting the most relevant input features from raw data but does not involve tagging output labels.
* Hyperparameter tuning adjusts parameters (like learning rate or depth of a tree) to optimize model performance after labeling and training have begun.
Thus, assigning classes to images prior to model training is definitively a Labeling task.
NEW QUESTION # 65
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