Microsoft AI-900 Online Practice Questions and Exam Preparation

Microsoft AI-900 Online Questions & Answers

• Question 81:

  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

  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.

  Box 2: No

  A data holder is obligated to protect the data in an AI system, and privacy and security are an integral part of this system. Personal needs to be secured, and it should be accessed in a way that doesn't compromise an individual's privacy.

  Box 3: No

  Inclusiveness mandates that AI should consider all human races and experiences, and inclusive design practices can help developers to understand and address potential barriers that could unintentionally exclude people. Where possible, speech-to-text, text-to-speech, and visual recognition technology should be used to empower people with hearing, visual, and other impairments.

  References:

  https://docs.microsoft.com/en-us/azure/cloud-adoption-framework/innovate/best-practices/trusted-ai

• Question 82:

  Which two languages can you use to write custom code for Azure Machine Learning designer? Each correct answer presents a complete solution.

  NOTE: Each correct selection is worth one point.

  A. Python
  B. R
  C. C#
  D. Scala
  A. Python
  B. R

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  Explanation

  Use Azure Machine Learning designer for customizing using Python and R code.

  References:

  https://azure.microsoft.com/en-us/services/machine-learning/designer/#features

• Question 83:

  You want to detect multiple objects and their locations within an image.

  Which computer vision technique should you use?

  A. image classification
  B. object detection
  C. OCR
  D. semantic analysis
  B. object detection

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  Explanation

  Object detection identifies objects and provides bounding box coordinates, enabling localization of multiple items within an image.

• Question 84:

  HOTSPOT

  Select the answer that correctly completes the sentence.

  

  

• Question 85:

  Which type of natural language processing (NLP) entity is used to identify a phone number?

  A. regular expression
  B. machine-learned
  C. list
  D. Pattern.any
  C. list

• Question 86:

  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 Microsoft Learn module "Identify features of Computer Vision workloads on Azure", Object Detection is a specific computer vision capability used to identify and locate multiple types of objects within a single image. Unlike image classification, which assigns one label to an entire image, object detection identifies individual objects, their categories, and their positions using bounding boxes or polygons. In practical terms, Object Detection combines two key outputs: Classification - recognizing what the object is (for example, "car", "person", "dog"). Localization - determining where the object appears in the image by drawing bounding boxes around it. This technology is commonly used in scenarios such as traffic monitoring (detecting vehicles and pedestrians), retail shelf analysis (detecting products and inventory levels), and manufacturing quality control (identifying defective parts). Microsoft's Azure Cognitive Services - Custom Vision includes a dedicated Object Detection domain, which allows developers to train custom models to recognize multiple object types within a single image. The service uses deep learning techniques, particularly convolutional neural networks (CNNs), to process pixel patterns and spatial relationships for accurate detection. For contrast: Image Classification identifies only the overall category of an image (e.g., "This is a cat"). Image Description generates captions summarizing the visual content (e.g., "A cat sitting on a couch"). Optical Character Recognition (OCR) detects and extracts text from images, not physical objects. Therefore, per the official AI-900 learning content and Azure documentation, when the goal is to identify multiple types of items within a single image, the correct AI workload is Object Detection.

• Question 87:

  Which two tools can you use to call the Azure OpenAI service? Each correct answer presents a complete solution.

  NOTE: Each correct answer is worth one point.

  A. Azure Command-Line Interface (CLI)
  B. Azure REST API
  C. Azure SDK for Python
  D. Azure SDK for JavaScript
  A. Azure Command-Line Interface (CLI)
  B. Azure REST API

• Question 88:

  You use Azure Machine Learning designer to publish an inference pipeline.

  Which two parameters should you use to consume the pipeline? Each correct answer presents part of the solution.

  NOTE: Each correct selection is worth one point.

  A. the model name
  B. the training endpoint
  C. the authentication key
  D. the REST endpoint
  C. the authentication key
  D. the REST endpoint

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  Explanation

  https://docs.microsoft.com/en-in/learn/modules/create-regression-model-azure-machine-learning-designer/deploy-service

• Question 89:

  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/text-analytics/overviewhttps://azure.microsoft.com/en-gb/services/cognitive-services/speech-services/

• Question 90:

  You plan to develop a bot that will enable users to query a knowledge base by using natural language processing.

  Which two services should you include in the solution? Each correct answer presents part of the solution.

  NOTE: Each correct selection is worth one point.

  A. QnA Maker
  B. Azure Bot Service
  C. Form Recognizer
  D. Anomaly Detector
  A. QnA Maker
  B. Azure Bot Service

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  Explanation

  References:

  https://docs.microsoft.com/en-us/azure/bot-service/bot-service-overview-introduction?view=azure-bot-service-4.0https://docs.microsoft.com/en-us/azure/cognitive-services/luis/choose-natural-language-processing-service