Microsoft AI-900 Online Practice Questions and Exam Preparation

Microsoft AI-900 Online Questions & Answers

• Question 191:

  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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  Box 1: Yes

  Custom Vision functionality can be divided into two features. Image classification applies one or more labels to an image. Object detection is similar, but it also returns the coordinates in the image where the applied label(s) can be found.

  Box 2: Yes

  The Custom Vision service uses a machine learning algorithm to analyze images. You, the developer, submit groups of images that feature and lack the characteristics in question. You label the images yourself at the time of submission.

  Then, the algorithm trains to this data and calculates its own accuracy by testing itself on those same images.

  Box 3: No

  Custom Vision service can be used only on graphic files.

  References:

  https://docs.microsoft.com/en-us/azure/cognitive-services/Custom-Vision-Service/overview

• Question 192:

  DRAG DROP

  You plan to apply Text Analytics API features to a technical support ticketing system.

  Match the Text Analytics API features to the appropriate natural language processing scenarios.

  To answer, drag the appropriate feature from the column on the left to its scenario on the right. Each feature 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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  Box1: Sentiment analysis

  Sentiment Analysis is the process of determining whether a piece of writing is positive, negative or neutral.

  Box 2: Broad entity extraction

  Broad entity extraction: Identify important concepts in text, including key

  Key phrase extraction/ Broad entity extraction: Identify important concepts in text, including key phrases and named entities such as people, places, and organizations.

  Box 3: Entity Recognition

  Named Entity Recognition: Identify and categorize entities in your text as people, places, organizations, date/time, quantities, percentages, currencies, and more. Well-known entities are also recognized and linked to more information on the web.

  References:

  https://docs.microsoft.com/en-us/azure/architecture/data-guide/technology-choices/natural-language-processinghttps://azure.microsoft.com/en-us/services/cognitive-services/text-analytics

• Question 193:

  You need to extract structured data such as tables and key/value pairs from invoices.

  Which service should you use?

  A. Form Recognizer
  B. Computer Vision
  C. Text Analytics
  D. Custom Vision
  A. Form Recognizer

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  Explanation

  Form Recognizer extracts structured data like key/value pairs and tables from documents such as invoices and receipts.

• Question 194:

  What is an example of the Microsoft responsible AI principle of transparency?

  A. ensuring that opportunities are allocated equally to all applicants
  B. helping users understand the decisions made by an AI system
  C. ensuring that developers are accountable for the solutions they create
  D. ensuring that the privileged data of users is stored in a secure manner
  B. helping users understand the decisions made by an AI system

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  Explanation

  Transparency

  Achieving transparency helps the team to understand the data and algorithms used to train the model, what transformation logic was applied to the data, the final model generated, and its associated assets. This information offers insights about how the model was created, which allows it to be reproduced in a transparent way.

  Each predictive value should be broken down into individual features or vectors by importance or impact and deliver thorough prediction explanations that can be exported into a business report for audit and compliance reviews, customer transparency, and business readiness.

  Transparency Notes provide our customers with information about the intended uses, capabilities, and limitations of our AI platform services.

  References:

  https://learn.microsoft.com/en-us/azure/cloud-adoption-framework/innovate/best-practices/trusted-aihttps://www.microsoft.com/en-us/ai/responsible-ai

• Question 195:

  Which format should you use to send requests to a REST API endpoint for Azure OpenAI?

  A. CSV
  B. ISON
  C. XML
  D. YAML
  B. ISON

• Question 196:

  You need to predict the income range of a given customer by using the following dataset.

  

  Which two fields should you use as features? Each correct answer presents a complete solution.

  NOTE: Each correct selection is worth one point.

  A. Education Level
  B. Last Name
  C. Age
  D. Income Range
  E. First Name
  A. Education Level
  C. Age

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  Explanation

  First Name, Last Name, Age and Education Level are features. Income range is a label (what you want to predict). First Name and Last Name are irrelevant in that they have no bearing on income. Age and Education level are the features you should use.

• Question 197:

  HOTSPOT

  Select the answer that correctly completes the sentence.

  

  

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  According to the Microsoft Azure AI Fundamentals (AI-900) Official Study Guide and the Microsoft Learn module "Explore computer vision in Microsoft Azure," computer vision is a field of artificial intelligence that enables computers to interpret and understand visual information from the world - such as images or videos. In this scenario, the task is to count the number of animals in an area based on a video feed. This requires the system to: Detect the presence of animals in each frame of the video (object detection). Track and count them across multiple frames as they move. These are classic computer vision tasks, as they involve analyzing visual inputs (video or image data) and identifying objects (in this case, animals). Azure provides services such as Azure Computer Vision, Custom Vision, and Video Indexer, which can perform object detection, counting, and activity recognition using AI models trained on visual datasets. Why the other options are incorrect:

  Forecasting: Involves predicting future values based on historical data (e.g., predicting sales or weather), not analyzing video feeds. Knowledge mining: Focuses on extracting insights from large text-based document repositories, not images or videos. Anomaly detection: Identifies unusual patterns in numeric or time-series data, not visual objects. Therefore, identifying and counting animals in video footage falls under computer vision, since it uses AI to visually detect, classify, and quantify objects in real-time or recorded feeds.

  References:

  https://docs.microsoft.com/en-us/azure/cognitive-services/computer-vision/overviewhttps://docs.microsoft.com/en-us/azure/cognitive-services/computer-vision/intro-to-spatial-analysis-public-preview

• Question 198:

  DRAG DROP

  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.

  Select and Place:

  

  

• Question 199:

  What is a form of unsupervised machine learning?

  A. multiclass classification
  B. clustering
  C. binary classification
  D. regression
  B. clustering

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  Explanation

  As outlined in the AI-900 study guide and Microsoft Learn's "Explore fundamental principles of machine learning" module, clustering is a core example of unsupervised machine learning. In unsupervised learning, the model is trained on data without labeled outcomes. The goal is to discover patterns or groupings naturally present in the data. Clustering algorithms, such as K-means, DBSCAN, or Hierarchical clustering, analyze similarities among data points and group them into clusters. For example, clustering can group customers by purchasing behavior or segment products by shared characteristics - all without predefined labels. Supervised learning, by contrast, uses labeled data (input-output pairs) to train a model that predicts outcomes. This includes:

  A. Multiclass classification - Predicts more than two categories (e.g., classifying images as dog, cat, or bird).

  C. Binary classification - Predicts two categories (e.g., spam vs. not spam).

  D. Regression - Predicts continuous numeric values (e.g., price prediction). Therefore, the only option representing unsupervised learning is clustering, which enables data discovery without predefined labels.

• Question 200:

  You have a bot that identifies the brand names of products in images of supermarket shelves.

  Which service does the bot use?

  A. AI enrichment for Azure Search capabilities
  B. Computer Vision Image Analysis capabilities
  C. Custom Vision Image Classification capabilities
  D. Language Understanding capabilities
  C. Custom Vision Image Classification capabilities

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  Explanation

  Brand detection is a specialized mode of object detection that uses a database of thousands of global logos to identify commercial brands in images or video. You can use this feature, for example, to discover which brands are most popular on social media or most prevalent in media product placement.

  The Azure AI Vision service detects whether there are brand logos in a given image; if there are, it returns the brand name, a confidence score, and the coordinates of a bounding box around the logo.

  The built-in logo database covers popular brands in consumer electronics, clothing, and more. If you find that the brand you're looking for is not detected by the Azure AI Vision service, you could also try creating and training your own logo detector using the Custom Vision service.

  References:

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