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Microsoft AI-900 certification exam is a fundamental level exam that tests one's knowledge of Microsoft Azure AI services. Microsoft Azure AI Fundamentals certification exam is designed for individuals who want to learn about the basic principles of artificial intelligence and how it can be used to drive business outcomes. AI-900 exam is intended for individuals who have a basic understanding of cloud computing concepts and some experience with Microsoft Azure.

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Microsoft Azure AI Fundamentals Sample Questions (Q114-Q119):

NEW QUESTION # 114
You need to develop a web-based AI solution for a customer support system. Users must be able to interact with a web app that will guide them to the best resource or answer.
Which service should you use?

Answer: C

Explanation:
QnA Maker is a cloud-based API service that lets you create a conversational Question:-and-answer layer over your existing data. Use it to build a knowledge base by extracting Questions and answers from your semi- structured content, including FAQs, manuals, and documents. Answer users' Questions with the best answers from the QnAs in your knowledge base-automatically. Your knowledge base gets smarter, too, as it continually learns from user behavior.
Incorrect Answers:
A: Azure Custom Vision is a cognitive service that lets you build, deploy, and improve your own image classifiers. An image classifier is an AI service that applies labels (which represent classes) to images, according to their visual characteristics. Unlike the Computer Vision service, Custom Vision allows you to specify the labels to apply.
D: Azure Cognitive Services Face Detection API: At a minimum, each detected face corresponds to a faceRectangle field in the response. This set of pixel coordinates for the left, top, width, and height mark the located face. Using these coordinates, you can get the location of the face and its size. In the API response, faces are listed in size order from largest to smallest.
Reference:
https://azure.microsoft.com/en-us/services/cognitive-services/qna-maker/


NEW QUESTION # 115
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:

Explanation

Reference:
https://docs.microsoft.com/en-us/azure/bot-service/bot-service-manage-channels?view=azure-bot-service-4.0


NEW QUESTION # 116
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:


Reference:
https://docs.microsoft.com/en-gb/azure/cognitive-services/qnamaker/concepts/data-sources-and-content
https://docs.microsoft.com/en-us/azure/cognitive-services/luis/choose-natural-language-processing-service QnA maker conversational AI service and has nothing to do with SQL database You can easily create a user support bot solution on Microsoft Azure using a combination of two core technologies:
- QnA Maker. This cognitive service enables you to create and publish a knowledge base with built-in natural language processing capabilities.
- Azure Bot Service. This service provides a framework for developing, publishing, and managing bots on Azure.
https://docs.microsoft.com/en-us/learn/modules/build-faq-chatbot-qna-maker-azure-bot-service/2-get-started-qna-bot LUIS is used to understand user intent from utterances.
Creating a language understanding application with Language Understanding consists of two main tasks. First you must define entities, intents, and utterances with which to train the language model - referred to as authoring the model. Then you must publish the model so that client applications can use it for intent and entity prediction based on user input.
https://docs.microsoft.com/en-us/azure/cognitive-services/luis/choose-natural-language-processing-service


NEW QUESTION # 117
To complete the sentence, select the appropriate option in the answer area.

Answer:

Explanation:

Reference:
https://docs.microsoft.com/en-us/azure/cloud-adoption-framework/innovate/best-practices/trusted-ai


NEW QUESTION # 118
Which Azure Machine Learning capability should you use to quickly build and deploy a predictive model without extensive coding?

Answer: A

Explanation:
According to the Microsoft Azure AI Fundamentals (AI-900) curriculum and Microsoft Learn's "Explore Automated Machine Learning in Azure Machine Learning" module, Automated ML (AutoML) is the Azure Machine Learning capability that allows users to quickly build, train, and deploy predictive models with minimal or no coding experience.
Automated ML automatically performs tasks that would normally require expert data science knowledge, such as:
* Selecting appropriate algorithms (e.g., decision trees, logistic regression, random forests)
* Performing hyperparameter tuning to optimize model accuracy
* Handling missing data and feature scaling automatically
* Generating performance metrics and best model recommendations
This feature is especially useful for business analysts, developers, or beginners who want to leverage machine learning for predictions (like sales forecasting, churn analysis, or demand prediction) without having to write complex Python code.
Other options explained:
* A. ML pipelines automate and organize workflows for model training and deployment but still require pre-built models.
* B. Copilot is an AI-powered assistant embedded in Microsoft tools for productivity, not a model training feature.
* C. DALL-E is an image generation model under Azure OpenAI, not a predictive modeling tool.
Thus, per official Microsoft Learn content, Automated Machine Learning is the correct capability to quickly build and deploy predictive models with minimal coding.


NEW QUESTION # 119
......

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