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Microsoft AI-900 (Microsoft Azure AI Fundamentals) Exam is a certification exam that focuses on the basics of Artificial Intelligence (AI) and its applications in Azure. AI-900 exam is designed to help professionals and students understand the core principles of AI, including machine learning, natural language processing, computer vision, and cognitive services. AI-900 Exam also covers the fundamentals of Azure AI services, including Azure Machine Learning, Azure Cognitive Services, and Azure Bot Service.
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Microsoft AI-900 exam is the foundation level certification exam that is designed to validate your foundational knowledge of Artificial Intelligence (AI) and Machine Learning (ML) concepts on the Microsoft Azure platform. Microsoft Azure AI Fundamentals certification exam is ideal for individuals who want to demonstrate their knowledge of AI and ML concepts and how they can be applied to the Azure platform. AI-900 exam is designed to test your understanding of essential AI principles, including ML algorithms, data preparation, and natural language processing.
To pass the Microsoft AI-900 Exam, candidates must demonstrate their knowledge of various AI and machine learning concepts, including data analysis, natural language processing, and computer vision. Candidates must also have a good understanding of how these technologies can be used to solve real-world problems and how they can be implemented in Microsoft Azure. AI-900 exam consists of multiple-choice questions and is timed, with a total of 60 minutes to complete.
NEW QUESTION # 322
Match the facial recognition tasks to the appropriate questions.
To answer, drag the appropriate task from the column on the left to its question on the right. Each task may be used once, more than once, or not at all.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Reference:
https://azure.microsoft.com/en-us/services/cognitive-services/face/#features
NEW QUESTION # 323
Select the answer that correctly completes the sentence.
Answer:
Explanation:
Explanation:
According to the Microsoft Azure AI Fundamentals (AI-900) official study guide and the Microsoft Learn module "Describe features of natural language processing (NLP) workloads on Azure," Natural Language Processing refers to the branch of AI that enables computers to interpret, understand, and generate human language. One of the main NLP workloads identified by Microsoft is speech-to-text conversion, which transforms spoken words into written text.
Creating a text transcript of a voice recording perfectly fits this definition because it involves converting audio language data into text form - a process handled by speech recognition models. These models analyze the acoustic features of human speech, segment phonemes, identify words, and produce a text transcript. On Azure, this function is implemented using the Azure Cognitive Services Speech-to-Text API, part of the Language and Speech services.
Let's examine the other options to clarify why they are incorrect:
* Computer vision workload: Involves interpreting and analyzing visual data such as images and videos (e.g., object detection, facial recognition). It does not deal with speech or audio.
* Knowledge mining workload: Refers to extracting useful information from large amounts of structured and unstructured data using services like Azure Cognitive Search, not transcribing audio.
* Anomaly detection workload: Involves identifying unusual patterns in data (e.g., fraud detection or sensor anomalies), unrelated to language or speech.
In summary, when a system creates a text transcript from spoken audio, it is performing a speech recognition task-classified under Natural Language Processing (NLP). This workload helps make spoken content searchable, analyzable, and accessible, aligning with Microsoft's Responsible AI goal of enhancing accessibility through language understanding.
NEW QUESTION # 324
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
NEW QUESTION # 325
Which two scenarios are examples of a conversational AI workload? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.
Answer: A,B
Explanation:
B: A bot is an automated software program designed to perform a particular task. Think of it as a robot without a body.
C: Automated customer interaction is essential to a business of any size. In fact, 61% of consumers prefer to communicate via speech, and most of them prefer self-service. Because customer satisfaction is a priority for all businesses, self-service is a critical facet of any customer-facing communications strategy.
Incorrect Answers:
D: Early bots were comparatively simple, handling repetitive and voluminous tasks with relatively straightforward algorithmic logic. An example would be web crawlers used by search engines to automatically explore and catalog web content.
Reference:
https://docs.microsoft.com/en-us/azure/architecture/data-guide/big-data/ai-overview
https://docs.microsoft.com/en-us/azure/architecture/solution-ideas/articles/interactive-voice-response-bot
NEW QUESTION # 326
You need to reduce the load on telephone operators by implementing a Chabot to answer simple questions with predefined answers.
Which two Al services should you use to achieve the goal? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
Answer: A,C
Explanation:
According to the Microsoft Azure AI Fundamentals (AI-900) Official Study Guide and the Microsoft Learn module "Explore conversational AI in Microsoft Azure," to create a chatbot that can automatically answer simple, predefined user questions, you need two main Azure AI components - one to handle the conversation interface and another to manage the knowledge and language understanding aspect.
* Azure Bot Service (A)This service is used to create, manage, and deploy chatbots that interact with users through text or voice. The Bot Service provides the framework for conversation management, user interaction, and channel integration (e.g., webchat, Microsoft Teams, Skype). It serves as the backbone of conversational AI applications and supports integration with other cognitive services like the Language Service.
* Language Service (D)The Azure AI Language Service (which now includes Question Answering, formerly QnA Maker) is used to build and manage the knowledge base of predefined questions and answers. This service enables the chatbot to understand user queries and return appropriate responses automatically. The QnA capability allows you to import documents, FAQs, or structured data to create a searchable database of responses for the bot.
Why the other options are incorrect:
* B. Azure Machine Learning: This service is used for building, training, and deploying custom machine learning models, not for chatbot Q&A automation.
* C. Translator: This service performs language translation, which is not required for answering predefined questions unless multilingual support is specifically needed.
Therefore, to implement a chatbot that can answer simple, repetitive user questions and reduce the load on human operators, you combine Azure Bot Service (for interaction) with the Language Service (for question- answering intelligence).
NEW QUESTION # 327
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