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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Describe features of Natural Language Processing (NLP) workloads on Azure | 15-20% | - Identify Azure AI services for NLP - Describe Azure capabilities for NLP - Identify common NLP tasks |
| Topic 2: Describe features of Generative AI workloads on Azure | 15-20% | - Identify responsible AI considerations for generative AI - Describe generative AI concepts - Describe Azure OpenAI Service capabilities |
| Topic 3: Describe fundamental principles of machine learning on Azure | 30-35% | - Describe core machine learning concepts - Describe Azure Machine Learning capabilities - Identify common machine learning tasks - Describe features of no-code automated ML |
| Topic 4: Describe AI workloads and considerations | 15-20% | - Identify guiding principles for responsible AI - Identify features of common AI workloads |
| Topic 5: Describe features of computer vision workloads on Azure | 15-20% | - Describe Azure capabilities for computer vision - Identify common computer vision tasks - Identify Azure AI services for computer vision |
>> Microsoft AI-900 Exam Quiz <<
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NEW QUESTION # 310
You need to predict the sea level in meters for the next 10 years.
Which type of machine learning should you use?
Answer: A
Explanation:
In the most basic sense, regression refers to prediction of a numeric target.
Linear regression attempts to establish a linear relationship between one or more independent variables and a numeric outcome, or dependent variable.
You use this module to define a linear regression method, and then train a model using a labeled dataset. The trained model can then be used to make predictions.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/linear-regression
NEW QUESTION # 311
Match the Azure Cognitive Services to the appropriate Al workloads.
To answer, drag the appropriate service from the column on the left to its workload on the right. Each service may be used once, more than once, or not at all.
NOTE: Each correct match is worth one point.
Answer:
Explanation:
Explanation:
The correct matches are Custom Vision, Form Recognizer, and Face - each corresponding to a distinct capability under Azure Cognitive Services as described in the Microsoft Azure AI Fundamentals (AI-900) official study guide and Microsoft Learn modules on Computer Vision workloads.
* Custom Vision # Identify objects in an imageThe Custom Vision service is part of the Azure Cognitive Services suite that enables developers to train custom image classification and object detection models.
Unlike the prebuilt Computer Vision API, Custom Vision allows users to upload their own labeled images and teach the model to recognize specific objects relevant to their business context. The AI-900 syllabus explains that Custom Vision is ideal for tasks such as identifying products on a shelf, categorizing images, or detecting defects in manufacturing.
* Form Recognizer # Automatically import data from an invoice to a databaseForm Recognizer is a document processing AI service that extracts structured data from forms, receipts, and invoices. It uses optical character recognition (OCR) combined with layout and key-value pair detection to automatically capture information such as invoice numbers, amounts, and vendor names. The AI-900 study materials highlight this service under the Document Intelligence category, emphasizing its ability to streamline data entry and business automation workflows by importing extracted data directly into databases or applications.
* Face # Identify people in an imageThe Face service provides advanced facial detection and recognition capabilities. It can locate faces in images, compare similarities between faces, identify known individuals, and even detect facial attributes such as age or emotion. The AI-900 course classifies this under Computer Vision services for person identification and security-related use cases such as access control or identity verification.
Thus, each mapping aligns precisely with the AI-900 official learning outcomes on Cognitive Services capabilities:
* Custom Vision # Object recognition
* Form Recognizer # Data extraction from forms
* Face # People identification
# Final verified configuration:
* Custom Vision # Identify objects in an image
* Form Recognizer # Automatically import data from an invoice to a database
* Face # Identify people in an image
NEW QUESTION # 312
You are processing photos of runners in a race.
You need to read the numbers on the runners' shirts to identity the runners in the photos.
Which type of computer vision should you use?
Answer: C
Explanation:
Explanation
Optical character recognition (OCR) allows you to extract printed or handwritten text from images and documents.
Reference:
https://docs.microsoft.com/en-us/azure/cognitive-services/computer-vision/overview-ocr
NEW QUESTION # 313
You need to provide content for a business chatbot that will help answer simple user queries.
What are three ways to create question and answer text by using QnA Maker? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.
Answer: A,B,E
Explanation:
Explanation
Automatic extraction
Extract question-answer pairs from semi-structured content, including FAQ pages, support websites, excel files, SharePoint documents, product manuals and policies.
Reference:
https://docs.microsoft.com/en-us/azure/cognitive-services/qnamaker/concepts/content-types
NEW QUESTION # 314
You plan to deploy an Azure Machine Learning model as a service that will be used by client applications.
Which three processes should you perform in sequence before you deploy the model? To answer, move the appropriate processes from the list of processes to the answer area and arrange them in the correct order.
Answer:
Explanation:
Explanation:
Graphical user interface, text, application, chat or text message Description automatically generated
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/concept-ml-pipelines
NEW QUESTION # 315
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