Microsoft AI-900 Online Practice Questions and Exam Preparation

Microsoft AI-900 Online Questions & Answers

• Question 381:

  DRAG DROP

  Match the tasks to the appropriate machine learning models.

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

  NOTE: Each correct selection is worth one point.

  Select and Place:

  

  

• Question 382:

  HOTSPOT

  Select the answer that correctly completes the sentence.

  A traffic monitoring system that collects vehicle registration numbers from CCTV footage is an example of ______________ in the Azure AI Computer Vision service.

  

  

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  Text extraction. According to the Microsoft Azure AI Fundamentals (AI-900) study guide and Microsoft Learn documentation for Azure AI Vision (formerly Computer Vision), text extraction-also known as Optical Character Recognition (OCR)-is the computer vision capability that detects and extracts printed or handwritten text from images and video frames. In this scenario, a traffic monitoring system collects vehicle registration numbers (license plates) from CCTV footage. These registration numbers are alphanumeric text that must be read and converted into digital form for processing, storage, or analysis. The Azure AI Vision service's OCR (text extraction) feature performs this function. It analyzes each frame from the video feed, detects text regions (the license plates), and converts the visual text into machine-readable text data. This process is widely used in Automatic Number Plate Recognition (ANPR) systems that support law enforcement, toll booths, and parking management solutions. The OCR model can handle variations in font, lighting, and angle to accurately extract license plate numbers. The other options describe different vision capabilities: Image classification assigns an image to a general category (e.g., "car," "truck," or "bike"), not text extraction. Object detection identifies and locates objects in images using bounding boxes (e.g., detecting the car itself), but not the text written on the car. Spatial analysis tracks people or objects in a defined physical space (e.g., counting individuals entering a building), not reading text. Therefore, for a traffic monitoring system that identifies vehicle registration numbers from CCTV footage, the most accurate Azure AI Vision capability is Text extraction (OCR).

• Question 383:

  A developer wants to extract structured data from documents such as recipes using a prebuilt AI model.

  Which Azure AI capability provides prebuilt document models?

  A. Azure AI Computer Vision
  B. Azure AI Document Intelligence
  C. Azure AI Speech
  D. Azure AI Language
  B. Azure AI Document Intelligence

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  Explanation

  Azure AI Document Intelligence provides prebuilt models designed to extract structured information from documents such as invoices, receipts, and forms. These models can identify fields and return structured outputs.

• Question 384:

  HOTSPOT

  Select the answer that correctly completes the sentence.

  

  

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  According to the Microsoft Azure AI Fundamentals (AI-900) official study materials and Microsoft Learn module "Describe features of common AI workloads," an anomaly detection workload is designed to identify data points or patterns that deviate significantly from what is expected or normal. These anomalies often indicate irregularities, faults, or potential issues that require attention. In this scenario, the AI system monitors temperature data from a large machine. Normally, the machine operates within a predictable temperature range. When the AI detects sudden or unexpected temperature spikes or drops - behavior that does not match the historical pattern - it flags these occurrences as anomalies. This type of workload is fundamental in predictive maintenance and industrial monitoring, where it helps detect equipment failures, safety hazards, or energy inefficiencies before they escalate. Microsoft's AI-900 curriculum emphasizes that anomaly detection workloads are often used in: Industrial IoT systems (detecting abnormal sensor readings or machine behavior) Finance (fraud detection or unusual transaction monitoring) Cybersecurity (detecting irregular network traffic or access patterns) Operations (identifying abnormal variations in production data) The Azure service used for this purpose is Azure Anomaly Detector, part of Azure Cognitive Services, which uses advanced statistical and machine learning models to automatically detect outliers in time-series data such as temperature, pressure, or transaction logs. By comparison: Computer vision handles image or video analysis. Knowledge mining extracts insights from large document collections.

  Natural Language Processing (NLP) interprets human language. Thus, based on the official Microsoft AI-900 study guide and Microsoft Learn, the correct and verified answer is An anomaly detection workload, since detecting unusual temperature fluctuations precisely fits this AI workload type.

• Question 385:

  You need to create a training dataset and validation dataset from an existing dataset.

  Which module in the Azure Machine Learning designer should you use?

  A. Select Columns in Dataset
  B. Add Rows
  C. Split Data
  D. Join Data
  C. Split Data

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  Explanation

  A common way of evaluating a model is to divide the data into a training and test set by using Split Data, and then validate the model on the training data. Use the Split Data module to divide a dataset into two distinct sets. The studio currently supports training/validation data splits

  References:

  https://docs.microsoft.com/en-us/azure/machine-learning/how-to-configure-cross-validation-data-splits2

• Question 386:

  Which term is used to describe uploading your own data to customize an Azure OpenAI model?

  A. completion
  B. grounding
  C. fine-tuning
  D. prompt engineering
  C. fine-tuning

• Question 387:

  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.

  

  

• Question 388:

  DRAG DROP

  You plan to use Azure Cognitive Services to develop a voice controlled personal assistant app.

  Match the Azure Cognitive Services to the appropriate tasks.

  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 selection is worth one point.

  Select and Place:

  

  

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  Box 1: Speech

  The Speech service provides speech-to-text and text-to-speech capabilities with an Azure Speech resource. You can transcribe speech to text with high accuracy, produce natural-sounding text-to-speech voices, translate spoken audio, and use speaker recognition during conversations.

  Box 2: Language service

  Build applications with conversational language understanding, a Cognitive Service for Language feature that understands natural language to interpret user goals and extracts key information from conversational phrases. Create multilingual, customizable intent classification and entity extraction models for your domain-specific keywords or phrases across 96 languages.

  Box 3: Speech

  Incorrect:

  Not Translator text: Text translation is a cloud-based REST API feature of the Translator service that uses neural machine translation technology to enable quick and accurate source-to-target text translation in real time across all supported languages.

  References:

  https://docs.microsoft.com/en-us/azure/cognitive-services/speech-service/overviewhttps://azure.microsoft.com/en-us/services/cognitive-services/conversational-language-understanding/https://docs.microsoft.com/en-us/azure/cognitive-services/translator/text-translation-overview

• Question 389:

  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 Azure Al Language Service's question answering? Each correct answer presents a complete solution. NOTE: Each correct and ask questions by selection is worth one point.

  A. Connect the bot to the Cortana channel using Cortana.
  B. Import chit-chat content from a predefined data source.
  C. Manually enter the questions and answers.
  D. Use Azure Machine Learning Automated ML to train a model based on a file that contains question and answer pairs.
  E. Generate the questions and answers from an existing webpage.
  B. Import chit-chat content from a predefined data source.
  C. Manually enter the questions and answers.
  E. Generate the questions and answers from an existing webpage.

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  Explanation

  Import chit-chat content from a predefined data source

  Manually enter the questions and answers, Generate the questions and answers from an existing webpage.

  According to Microsoft Learn and the Azure AI Fundamentals (AI-900) study guide, the Question Answering feature of the Azure AI Language Service (formerly part of QnA Maker) allows developers to create a knowledge base (KB) that enables a chatbot to answer common questions automatically. This knowledge base can be built in three main ways: Import chit-chat content (B):Azure provides predefined chit-chat datasets that can be imported to make a bot more conversational and natural. This includes small talk such as greetings, acknowledgments, and polite responses (for example, "How are you?" # "I'm doing great, thanks!"). Importing this content enriches the bot's personality and improves user engagement. Manually enter questions and answers (C):Developers can manually add pairs of questions and answers directly into the question answering knowledge base. This approach is suitable for custom FAQs or domain-specific content. It gives complete control over how each question is phrased and what answer is returned, ensuring high precision and clarity. Generate questions and answers from an existing webpage (E):Azure AI Language can automatically extract Q&A pairs from a website's FAQ or support page. This is done by providing the webpage URL to the service, which scans the page and builds a knowledge base from the detected questions and corresponding answers. The other options are incorrect: A (Cortana channel) relates to bot deployment, not knowledge creation. D (Automated ML) is used for predictive modeling, not for building Q&A datasets. Thus, the verified correct answers are B, C, and E.

• Question 390:

  You have a frequently asked questions (FAQ) PDF file.

  You need to create a conversational support system based on the FAQ.

  Which service should you use?

  A. QnA Maker
  B. Text Analytics
  C. Computer Vision
  D. Language Understanding (LUIS)
  A. QnA Maker

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  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.

  References:

  https://azure.microsoft.com/en-us/services/cognitive-services/qna-maker/