Microsoft AI-900 Online Practice Questions and Exam Preparation

Microsoft AI-900 Online Questions & Answers

• Question 261:

  Which feature of the Azure Al Language service should you use to automate the masking of names and phone numbers in text data?

  A. Personally Identifiable Information (Pll) detection
  B. entity linking
  C. custom text classification
  D. custom named entity recognition (NER)
  A. Personally Identifiable Information (Pll) detection

• Question 262:

  DRAG DROP

  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.

  Select and Place:

  

  

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  Box 1: verification

  Face verification: Check the likelihood that two faces belong to the same person and receive a confidence score.

  Box 2: similarity

  Box 3: Grouping

  Box 4: identification

  Face detection: Detect one or more human faces along with attributes such as: age, emotion, pose, smile, and facial hair, including 27 landmarks for each face in the image.

  References:

  https://azure.microsoft.com/en-us/services/cognitive-services/face/#features

• Question 263:

  Which service should you use to extract text, key/value pairs, and table data automatically from scanned documents?

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

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  Explanation

  Accelerate your business processes by automating information extraction. Form Recognizer applies advanced machine learning to accurately extract text, key/value pairs, and tables from documents. With just a few samples, Form Recognizer tailors its understanding to your documents, both on-premises and in the cloud. Turn forms into usable data at a fraction of the time and cost, so you can focus more time acting on the information rather than compiling it.

  References:

  https://azure.microsoft.com/en-us/services/cognitive-services/form-recognizer/

• Question 264:

  HOTSPOT

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

  

  

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  References:

  https://docs.microsoft.com/en-us/azure/cognitive-services/custom-vision-service/getting-started-build-a-classifier

• Question 265:

  HOTSPOT

  Select the answer that correctly completes the sentence.

  

  

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  According to the Microsoft Azure AI Fundamentals (AI-900) curriculum and Microsoft Learn's modules on Computer Vision, object detection is the AI technique used to identify and locate multiple objects within an image. Unlike simple image classification, which only labels an entire image with a single category (for example, "This is a product"), object detection not only identifies the type of object but also pinpoints its exact position by providing bounding boxes and coordinates within the image. In the scenario described - identifying the location of products on a conveyor belt - the system must be able to detect multiple items simultaneously and determine their spatial positions. Object detection algorithms (such as YOLO, Faster R-CNN, or SSD) are specifically designed for this purpose. This allows automation systems, like robotic arms or quality inspection systems, to track product locations in real time for sorting, packaging, or defect detection. Let's evaluate the other options: Image classification only determines what is in the image, not where it is located. It cannot handle multiple objects or their positions.

  Image processing involves operations like resizing, filtering, or adjusting contrast, not understanding object placement. Optical character recognition (OCR) extracts text from images and documents, unrelated to locating physical items. Thus, per Microsoft Learn's AI-900 guidance, object detection is the correct computer vision capability when a task requires both identification and spatial localization of items in an image or video stream. # Final Answer: Object detection

• Question 266:

  You need to develop a chatbot for a website. The chatbot must answer users' questions based on the information in the following documents:

  1. A product troubleshooting guide in a Microsoft Word document

  2. A frequently asked questions (FAQ) list on a webpage

  Which service should you use to process the documents?

  A. Azure Bot Service
  B. Language Understanding
  C. Text Analytics
  D. QnA Maker
  D. QnA Maker

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  Explanation

  References:

  https://docs.microsoft.com/en-us/azure/cognitive-services/QnAMaker/Overview/overview

• Question 267:

  HOTSPOT

  Select the answer that correctly completes the sentence.

  

  

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  "When evaluating the performance of a model, the confusion matrix displays the predicted and actual positives and negatives by using a grid of 0 and 1 values." According to the Microsoft Azure AI Fundamentals (AI-900) official study materials and Microsoft Learn module "Identify features of common machine learning types", a confusion matrix is a tool used to evaluate the performance of classification models. It visually summarizes how many predictions were correctly or incorrectly classified by comparing the predicted labels to the actual (true) labels. A confusion matrix is a table, typically 2x2 for binary classification, with the following components:

  True Positives (TP): The model correctly predicted the positive class.

  True Negatives (TN): The model correctly predicted the negative class.

  False Positives (FP): The model incorrectly predicted the positive class.

  False Negatives (FN): The model incorrectly predicted the negative class. The confusion matrix allows data scientists and analysts to derive important performance metrics such as accuracy, precision, recall, and F1-score, which together provide a more complete understanding of how well a model performs beyond a single number. In Microsoft Learn's AI-900 curriculum, the confusion matrix is highlighted as a key visualization tool that "compares actual values to predicted values to evaluate classification performance." The grid format (using 0s and 1s for predicted classes) helps identify where misclassifications occur. By contrast: AUC metric (Area Under Curve) and ROC curve evaluate model discrimination ability. Threshold defines decision cutoffs but doesn't display classifications. Therefore, based on the official Microsoft AI-900 study guide and Microsoft Learn resources, the correct answer is Confusion Matrix, as it provides a grid view comparing actual versus predicted values in classification models.

• Question 268:

  A company wants to create illustrations automatically based on written descriptions provided by designers.

  Which AI model should be used to generate images from text prompts?

  A. GPT-4
  B. Generative AI model for image generation
  C. Computer Vision image analysis
  D. Image tagging
  B. Generative AI model for image generation

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  Explanation

  Generative AI models designed for image generation can create images from natural language descriptions. These models learn patterns from large datasets and generate new images that match the prompt. GPT-4 primarily generates text, while computer vision and image tagging analyze existing images rather than create new ones.

• Question 269:

  HOTSPOT

  For each of the following statements, select Yes if the statement is true. Otherwise, select No.

  NOTE: Each correct selection is worth one point.

  

  

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  References:

  https://docs.microsoft.com/en-gb/azure/cognitive-services/qnamaker/concepts/data-sources-and-contenthttps://docs.microsoft.com/en-us/azure/cognitive-services/luis/choose-natural-language-processing-service

• Question 270:

  You are creating an app to help employees write emails and reports based on user prompts.

  What should you use?

  A. Azure Al Speech
  B. Azure OpenAI in Foundry Models
  C. Azure Al Vision
  D. Azure Machine Learning studio
  B. Azure OpenAI in Foundry Models

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  Explanation

  For an app that helps employees write emails and reports based on user prompts, you need a text generation model capable of understanding natural language instructions and producing coherent, contextually appropriate output. Azure OpenAI GPT models-available through Azure AI Foundry (formerly Azure OpenAI Studio)-are specifically designed for such generative tasks. By integrating GPT-3.5 or GPT-4, the app can analyze prompts like "Write a professional email to a client about project updates" and automatically generate polished text in seconds. The other options do not fit:

  A. Azure AI Speech: Converts spoken language to text or text to speech; not suitable for generating written content.

  C. Azure AI Vision: Processes and analyzes images or video content.

  D. Azure Machine Learning Studio: Used for training, testing, and deploying custom ML models, not directly for content generation. Therefore, to create a writing-assistance app for emails and reports, the correct solution is

  B. Azure OpenAI in Foundry Models using GPT-based language generation.