Microsoft AI-900 Online Practice Questions and Exam Preparation

Microsoft AI-900 Online Questions & Answers

• Question 1:

  HOTSPOT

  To complete the sentence, select the appropriate option in the answer area.

  

  

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  Box: detection

  Attributes are a set of features that can optionally be detected by the Face - Detect API. The following attributes can be detected:

  * Exposure. The exposure of the face in the image. This attribute returns a value between zero and one and an informal rating of underExposure, goodExposure, or overExposure.

  * Noise. The visual noise detected in the face image. This attribute returns a value between zero and one and an informal rating of low, medium, or high.

  * Occlusion. Whether there are objects blocking parts of the face. This attribute returns a Boolean value for eyeOccluded, foreheadOccluded, and mouthOccluded.

  * Etc.

  References:

  https://learn.microsoft.com/en-us/azure/cognitive-services/computer-vision/concept-face-detection

• Question 2:

  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.

  A. Azure 8ol Service
  B. Azure Machine Learning
  C. Translator
  D. Language Service
  A. Azure 8ol Service
  D. Language Service

• Question 3:

  You need to create a model that labels a collection of your personal digital photographs.

  Which Azure Al service should you use?

  A. Azure Al Language
  B. Azure Al Computer Vision
  C. Azure Al Document Intelligence
  D. Azure Al Custom Vision
  B. Azure Al Computer Vision

• Question 4:

  HOTSPOT

  You have the following apps:

  1. App1: Uses a set of images and photos to extract brand names

  2. App2: Enables touchless access control for buildings

  Which Azure Al Vision service does each app use? To answer, select the appropriate options in the answer area.

  NOTE: Each correct selection is worth one point.

  

  

• Question 5:

  DRAG DROP

  Match the services to the appropriate descriptions. To answer, drag the appropriate service from the column on the left to its description on the right. Each service may be used once, more than once, or not at all.

  NOTE: Each correct match is worth one point

  Select and Place:

  

  

• Question 6:

  HOTSPOT

  You have an app that identifies birds in images. The app performs the following tasks:

  1. Identifies the location of the birds in the image

  2. Identifies the species of the birds in the image

  Which type of computer vision does each task use? To answer, select the appropriate options in the answer area.

  NOTE: Each correct selection is worth one point.

  

  

  ---

  Box 1: Object detection

  Identifies the location of the birds in the image

  Object detection is similar to tagging, but the API returns the bounding box coordinates (in pixels) for each object found in the image.

  Box 2: Image classification

  Identifies the species of the birds in the image

  Image classification applies one or more labels to an entire image. Object detection is similar, but it returns the coordinates in the image where the applied label(s) can be found.

  Incorrect:

  * Automated captioning

  Captioning is the process of converting the audio content of a television broadcast, webcast, film, video, live event, or other production into text, and then displaying the text on a screen, monitor, or other visual display system.

  References:

  https://learn.microsoft.com/en-us/azure/cognitive-services/computer-vision/concept-object-detectionhttps://learn.microsoft.com/en-us/azure/cognitive-services/custom-vision-service/overview

• Question 7:

  What is an example of a Microsoft responsible Al principle?

  A. Al systems should treat people fairly.
  B. Al systems should NOT reveal the details of their design.
  C. Al systems should use black-box models.
  D. Al systems should protect the interests of developers.
  A. Al systems should treat people fairly.

  ---

  Explanation

  This statement aligns with one of Microsoft's six Responsible AI principles, which are: Fairness - AI systems should treat all people fairly and avoid bias. Reliability and Safety Privacy and Security Inclusiveness Transparency Accountability

  The principle of Fairness ensures that AI models do not discriminate based on factors such as race, gender, age, or socioeconomic background. For example, a loan approval or hiring model must provide equal opportunity to all qualified applicants regardless of demographic differences.

  B (Not revealing design details) contradicts Transparency, which promotes openness about AI functionality.

  C (Black-box models) goes against Microsoft's push for Explainable AI.

  D (Protect developers' interests) is not part of Microsoft's Responsible AI framework.

• Question 8:

  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.

  A. Generate the questions and answers from an existing webpage.
  B. Use automated machine learning to train a model based on a file that contains the questions.
  C. Manually enter the questions and answers.
  D. Connect the bot to the Cortana channel and ask questions by using Cortana.
  E. Import chit-chat content from a predefined data source.
  A. Generate the questions and answers from an existing webpage.
  C. Manually enter the questions and answers.
  E. Import chit-chat content from a predefined data source.

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  Explanation

  Automatic extraction

  Extract question-answer pairs from semi-structured content, including FAQ pages, support websites, excel files, SharePoint documents, product manuals and policies.

  References:

  https://docs.microsoft.com/en-us/azure/cognitive-services/qnamaker/concepts/content-types

• Question 9:

  DRAG DROP

  Match the types of machine learning to the appropriate scenarios.

  To answer, drag the appropriate machine learning type from the column on the left to its scenario on the right. Each machine learning type may be used once, more than once, or not at all.

  NOTE: Each correct selection is worth one point.

  Select and Place:

  

  

  ---

  Box 1: Image classification

  Image classification is a supervised learning problem: define a set of target classes (objects to identify in images), and train a model to recognize them using labeled example photos.

  Box 2: Object detection

  Object detection is a computer vision problem. While closely related to image classification, object detection performs image classification at a more granular scale. Object detection both locates and categorizes entities within images.

  Box 3: Semantic Segmentation

  Semantic segmentation achieves fine-grained inference by making dense predictions inferring labels for every pixel, so that each pixel is labeled with the class of its enclosing object ore region.

  References:

  https://developers.google.com/machine-learning/practica/image-classificationhttps://docs.microsoft.com/en-us/dotnet/machine-learning/tutorials/object-detection-model-builderhttps://nanonets.com/blog/how-to-do-semantic-segmentation-using-deep-learning/

• Question 10:

  HOTSPOT

  Select the answer that correctly completes the sentence.

  

  

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  Box: Power Virtual Agents

  Build conversational experiences with Power Virtual Agents and Azure Bot Service Azure Bot Service provides an integrated development environment for bot building. Its integration with Power Virtual Agents, a fully hosted low-code platform, enables developers of all technical abilities build conversational AI bots - no code needed.

  Bot development on your terms Create virtual agents using a guided graphical interface with no code, and for advanced scenarios, extend using Microsoft Power Automate and Azure Bot Services.

  Incorrect:

  * Microsoft Bot Framework

  Bot Framework SDKs for developing bots in C#, JavaScript, Python, or Java.

  According to the Microsoft Azure AI Fundamentals (AI-900) official study guide and the Microsoft Learn module "Describe features of conversational AI workloads," the correct answer is Power Virtual Agents. This service is part of the Microsoft Power Platform and is specifically designed to enable users to create intelligent chatbots without writing any code. Power Virtual Agents (PVA) provides a no-code/low-code environment where business users and developers can collaboratively design conversational experiences. It integrates built-in natural language processing (NLP) models to understand user intent and respond intelligently to text or speech inputs. The platform's interface allows chatbot creators to design dialogues visually, connect to back-end data via Power Automate, and publish bots on websites, Teams, or other communication channels. This approach is highlighted in Microsoft Learn as an ideal solution for organizations that want to deploy conversational bots quickly without requiring specialized AI or programming expertise. PVA automatically leverages Microsoft's language understanding models, allowing it to interpret user input and map it to predefined topics or actions.

  Let's analyze the other options:

  Azure Health Bot: A specialized solution for the healthcare industry that provides prebuilt medical compliance and healthcare content. It is not a general-purpose, no-code chatbot builder.

  Microsoft Bot Framework: A developer-focused SDK for building highly customized bots through code, offering maximum flexibility but not no-code functionality. Therefore, the most appropriate choice that "can be used to build no-code apps that use built-in natural language processing models" is Power Virtual Agents - the official Microsoft no-code chatbot solution for conversational AI workloads.

  References:

  https://azure.microsoft.com/en-us/products/power-virtual-agents/https://azure.microsoft.com/en-us/products/bot-services/