Microsoft AI-900 Online Practice Questions and Exam Preparation

Microsoft AI-900 Online Questions & Answers

• Question 41:

  HOTSPOT

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

  

  

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  Natural language processing (NLP) is used for tasks such as sentiment analysis, topic detection, language detection, key phrase extraction, and document categorization.

  References:

  https://docs.microsoft.com/en-us/azure/architecture/data-guide/technology-choices/natural-language-processing

• Question 42:

  What is an example of a Microsoft responsible Al principle?

  A. Al systems should protect the interests of developers.
  B. Al systems should be in the public domain.
  C. Al systems should be secure and respect privacy.
  D. Al systems should make personal details accessible.
  C. Al systems should be secure and respect privacy.

• Question 43:

  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 44:

  What are two metrics that you can use to evaluate a regression model? Each correct answer presents a complete solution.

  NOTE: Each correct selection is worth one point.

  A. coefficient of determination (R2)
  B. F1 score
  C. root mean squared error (RMSE)
  D. area under curve (AUC)
  E. balanced accuracy
  A. coefficient of determination (R2)
  C. root mean squared error (RMSE)

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  Explanation

  A: R-squared (R2), or Coefficient of determination represents the predictive power of the model as a value between -inf and 1.00. 1.00 means there is a perfect fit, and the fit can be arbitrarily poor so the scores can be negative.

  C: RMS-loss or Root Mean Squared Error (RMSE) (also called Root Mean Square Deviation, RMSD), measures the difference between values predicted by a model and the values observed from the environment that is being modeled.

  Incorrect Answers:

  B: F1 score also known as balanced F-score or F-measure is used to evaluate a classification model.

  D: aucROC or area under the curve (AUC) is used to evaluate a classification model.

  References:

  https://docs.microsoft.com/en-us/dotnet/machine-learning/resources/metrics

• Question 45:

  HOTSPOT

  You have the following apps:

  1. App1: Uses a set of images of tumors to identify whether the tumors are benign or malignant and suggest a treatment

  2. App2: Uses images from cameras to track individual livestock as they move around a farm

  3. App3: Identifies brands in photographs of billboards

  What does each app use?

  To answer, select the appropriate options in the answer area. NOTE: Each correct selection is worth one point.

  

  

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  Let's analyze each application in the context of Microsoft Azure AI Fundamentals (AI-900) and computer vision model types. App1 - Uses a set of images of tumors to identify whether the tumors are benign or malignant and suggest a treatment # Image classificationThis application is performing image classification, where each image (of a tumor) is assigned to a single predefined category - benign or malignant. Image classification models learn patterns from labeled

  training images and predict the correct class for new ones. In this case, the model identifies the type of tumor, a classic binary classification scenario. App2 - Uses images from cameras to track individual livestock as they move around a farm

  # Object detectionThis scenario describes object detection, which not only identifies what objects (in this case, animals) are in an image but also locates them by drawing bounding boxes. Tracking movement requires detecting the position of each animal frame by frame. Object detection models are well-suited for use cases involving counting, tracking, or monitoring objects in a visual scene. App3 - Identifies brands in photographs of billboards # Optical character recognition

  (OCR)This app involves reading and interpreting text (brand names, slogans, or logos) from images of billboards. Optical Character Recognition (OCR), part of Azure AI Vision, extracts textual information from images or scanned documents.

  Once extracted, that text can be analyzed to identify brand names or keywords. Summary: App1 # Image classification App2 # Object detection App3 # Optical character recognition (OCR)

• Question 46:

  Your company manufactures widgets.

  You have 1,000 digital photos of the widgets.

  You need to identify the location of the widgets within the photos.

  What should you use?

  A. Computer Vision Spatial Analysis
  B. Custom Vision object detection
  C. Computer Vision Image Analysis
  D. Custom Vision classification
  B. Custom Vision object detection

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  Explanation

  Object detection is similar to tagging, but the API returns the bounding box coordinates (in pixels) for each object found in the image. For example, if an image contains a dog, cat and person, the Detect operation will list those objects with their coordinates in the image. You can use this functionality to process the relationships between the objects in an image. It also lets you determine whether there are multiple instances of the same object in an image.

  The object detection function applies tags based on the objects or living things identified in the image.

  Incorrect:

  * Computer Vision Spatial Analysis

  Spatial analysis detects and locates human presence in video footage and outputs by using a bounding box around a human body. The AI models don't detect faces or determine individuals' identities or demographics.

  References:

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

• Question 47:

  DRAG DROP

  Match the tool to the Azure Machine Learning task.

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

  NOTE: Each correct match is worth one point.

  Select and Place:

  

  

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  Box 1: The Azure portal

  Box 2: Machine Learning designer

  Box 3: Automated machine learning (automated ML)

  Automated machine learning, also referred to as automated ML or AutoML, is the process of automating the time-consuming, iterative tasks of machine learning model development. It allows data scientists, analysts, and developers to build ML models with high scale, efficiency, and productivity all while sustaining model quality.

  References:

  https://docs.microsoft.com/en-us/azure/machine-learning/concept-automated-ml

• Question 48:

  HOTSPOT

  You are developing a model to predict events by using classification.

  You have a confusion matrix for the model scored on test data as shown in the following exhibit.

  

  Use the drop-down menus to select the answer choice that completes each statement based on the information presented in the graphic.

  NOTE: Each correct selection is worth one point.

  

  

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  Box 1: 11

  

  TP = True Positive.

  The class labels in the training set can take on only two possible values, which we usually refer to as positive or negative. The positive and negative instances that a classifier predicts correctly are called true positives (TP) and true negatives (TN), respectively. Similarly, the incorrectly classified instances are called false positives (FP) and false negatives (FN).

  Box 2: 1,033

  FN = False Negative

  5

  11

  1,033

  13,951 References:

  https://docs.microsoft.com/en-us/azure/machine-learning/studio/evaluate-model-performance

• Question 49:

  HOTSPOT

  Select the answer that correctly completes the sentence.

  

  

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  According to the Microsoft Azure AI Fundamentals (AI-900) study materials and Microsoft's Responsible AI guidelines, customers must obtain approval based on their intended usage before accessing and deploying Azure OpenAI Service.

  This requirement ensures that Microsoft upholds its commitment to Responsible AI principles, which include fairness, reliability and safety, privacy and security, inclusiveness, transparency, and accountability. The Azure OpenAI Service provides access to powerful language models such as GPT series and Codex, which can generate, summarize, and understand natural language and code. Because of the potential for misuse-such as generating harmful content, misinformation, or unethical automation-Microsoft enforces a use case review and approval process before granting customers access to the service. This process involves submitting an application describing the intended purpose, deployment method, and compliance measures. Only after Microsoft validates that the proposed use aligns with responsible AI practices will access be approved. This aligns with Microsoft's documented commitment that "customers are required to submit an application that describes their intended use of the Azure OpenAI Service," ensuring that all deployments follow ethical and legal standards. This approval step helps maintain transparency and prevent harmful or non-compliant use cases such as deepfake generation, biased automation, or malicious chatbot deployment.

  Other options listed in the question are incorrect: Commit to a minimum level of expenditure - Microsoft does not require financial commitments for ethical approval. Pay an upfront fee - Payment is handled through normal Azure billing, not a special fee. Provide credit card details - Not a responsible AI requirement; this is standard for any Azure subscription. Therefore, the correct and verified answer per Microsoft's Responsible AI framework and Azure AI-900 study

• Question 50:

  Which two resources can you use to analyze code and generate explanations of code function and code comments?

  Each correct answer presents a complete solution. NOTE: Each correct answer is worth one point.

  A. the Azure OpenAI DALL-E model
  B. the Azure OpenAI Whisper model
  C. the Azure OpenAI GPT-4 model
  D. the GitHub Copilot service
  A. the Azure OpenAI DALL-E model
  C. the Azure OpenAI GPT-4 model