Microsoft AI-900 Online Practice Questions and Exam Preparation

Microsoft AI-900 Online Questions & Answers

• Question 71:

  DRAG DROP

  Match the machine learning tasks to the appropriate scenarios.

  To answer, drag the appropriate task from the column on the left to its scenario 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: Model evaluation

  The Model evaluation module outputs a confusion matrix showing the number of true positives, false negatives, false positives, and true negatives, as well as ROC, Precision/Recall, and Lift curves.

  Box 2: Feature engineering

  Feature engineering is the process of using domain knowledge of the data to create features that help ML algorithms learn better. In Azure Machine Learning, scaling and normalization techniques are applied to facilitate feature engineering.

  Collectively, these techniques and feature engineering are referred to as featurization.

  Note: Often, features are created from raw data through a process of feature engineering. For example, a time stamp in itself might not be useful for modeling until the information is transformed into units of days, months, or categories that are relevant to the problem, such as holiday versus working day.

  Box 3: Feature selection

  In machine learning and statistics, feature selection is the process of selecting a subset of relevant, useful features to use in building an analytical model. Feature selection helps narrow the field of data to the most valuable inputs. Narrowing the field of data helps reduce noise and improve training performance.

  References:

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

• Question 72:

  HOTSPOT

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

  

  

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  https://en.wikipedia.org/wiki/Tay_(bot)

  "To build trust, it's critical that AI systems operate reliably, safely, and consistently under normal circumstances and in unexpected conditions. These systems should be able to operate as they were originally designed, respond safely to unanticipated conditions, and resist harmful manipulation. It's also important to be able to verify that these systems are behaving as intended under actual operating conditions. How they behave and the variety of conditions they can handle reliably and safely largely reflects the range of situations and circumstances that developers anticipate during design and testing. We believe that rigorous testing is essential during system development and deployment to ensure AI systems can respond safely in unanticipated situations and edge cases, don't have unexpected performance failures, and don't evolve in ways that are inconsistent with original expectations"

  inclusiveness

  privacy and security

  reliability and safety

  transparency References:

  https://docs.microsoft.com/en-us/learn/modules/responsible-ai-principles/4-guiding-principles

• Question 73:

  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 74:

  A web application allows users to upload images which are then analyzed to detect objects and identify features.

  Can computer vision services evaluate user-uploaded images?

  A. Yes
  B. No
  A. Yes

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  Explanation

  Computer vision services can analyze images from many sources, including user uploads. Once an image is submitted, the service can perform tasks such as object detection, tagging, or text recognition.

• Question 75:

  HOTSPOT

  Select the answer that correctly completes the sentence.

  

  

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  Azure's Computer Vision service gives you access to advanced algorithms that process images and return information based on the visual features you're interested in.

  * Optical Character Recognition (OCR)

  * Spatial Analysis

  * Image Analysis

  The Image Analysis service extracts many visual features from images, such as objects, faces, adult content, and auto-generated text descriptions. Follow the Image Analysis quickstart to get started.

  In the Microsoft Azure AI Fundamentals (AI-900) curriculum, computer vision capabilities refer to artificial intelligence systems that can analyze and interpret visual content such as images and videos. The Azure AI Vision and Face API services provide pretrained models for detecting, recognizing, and analyzing visual information, enabling developers to build intelligent applications that understand what they "see." When asked how computer vision capabilities can be deployed, the correct answer is to integrate a face detection feature into an app. This aligns with Microsoft Learn's module "Describe features of computer vision workloads," which explains that computer vision can identify objects, classify images, detect faces, and extract text (OCR). The Face API, a part of Azure AI Vision, specifically provides face detection, verification, and emotion recognition capabilities. Integrating these services into an application allows it to perform actions such as: Detecting human faces in photos or video streams. Recognizing facial attributes like age, emotion, or head pose. Enabling secure authentication based on face recognition. The other options are incorrect because they relate to different AI workloads: Develop a text-based chatbot for a website: This falls under Conversational AI, implemented with Azure Bot Service or Conversational Language Understanding (CLU). Identify anomalous customer behavior on an online store: This task relates to machine learning and anomaly detection models, not computer vision. Suggest automated responses to incoming email: This uses Natural Language Processing (NLP) capabilities, not visual analysis.

  Therefore, the correct and Microsoft-verified completion of the statement is: "Computer vision capabilities can be deployed to integrate a face detection feature into an app."

  References:

  https://docs.microsoft.com/en-us/azure/cognitive-services/computer-vision/overview

• Question 76:

  You build a machine learning model by using the automated machine learning user interface (UI).

  You need to ensure that the model meets the Microsoft transparency principle for responsible AI.

  What should you do?

  A. Set Validation type to Auto.
  B. Enable Explain best model.
  C. Set Primary metric to accuracy.
  D. Set Max concurrent iterations to 0.
  B. Enable Explain best model.

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  Explanation

  Model Explain Ability.

  Most businesses run on trust and being able to open the ML "black box" helps build transparency and trust. In heavily regulated industries like healthcare and banking, it is critical to comply with regulations and best practices. One key aspect of this is understanding the relationship between input variables (features) and model output. Knowing both the magnitude and direction of the impact each feature (feature importance) has on the predicted value helps better understand and explain the model. With model explain ability, we enable you to understand feature importance as part of automated ML runs.

  References:

  https://azure.microsoft.com/en-us/blog/new-automated-machine-learning-capabilities-in-azure-machine-learning-service/

• Question 77:

  Capturing text from images is an example of which type of Al capability?

  A. text analysis
  B. optical character recognition (OCR)
  C. image description
  D. object detection
  B. optical character recognition (OCR)

• Question 78:

  HOTSPOT

  Select the answer that correctly completes the sentence.

  

  

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  The question describes a process where an AI system generates text that describes an image - for example, "A dog playing with a ball in the park." This process is an example of image classification, a core workload in computer vision that allows a system to recognize and categorize the content of an image. According to the Microsoft Azure AI Fundamentals (AI-900) official study guide and Microsoft Learn module "Identify Azure services for computer vision," image classification involves analyzing the pixels of an image and assigning one or more predefined categories or labels to it. In more advanced implementations, image classification models are combined with caption generation algorithms to produce descriptive text. For example, Azure AI Vision can generate captions and tags that describe an image's content, such as "outdoor scene," "a person riding a bicycle," or "a group of people smiling." Let's review the other options to clarify why they are incorrect: Facial detection: Identifies the presence and location of human faces in an image, but does not generate descriptive text. Object detection: Identifies and locates multiple objects within an image by drawing bounding boxes, not by describing the overall scene. Optical character recognition (OCR): Extracts text from images or scanned documents (for example, reading a street sign), but it doesn't create descriptive language about what's depicted.

  Therefore, the correct answer is Image classification, as it aligns with the AI-900 learning objective that describes this task as recognizing and categorizing the main content of an image, often leading to caption generation in modern vision models such as those in Azure AI Vision.

• Question 79:

  In which two scenarios can you use the Form Recognizer service? Each correct answer presents a complete solution.

  NOTE: Each correct selection is worth one point.

  A. Extract the invoice number from an invoice.
  B. Translate a form from French to English.
  C. Find image of product in a catalog.
  D. Identity the retailer from a receipt.
  A. Extract the invoice number from an invoice.
  D. Identity the retailer from a receipt.

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  Explanation

  References:

  https://azure.microsoft.com/en-gb/services/cognitive-services/form-recognizer/#features

• Question 80:

  Which two scenarios are examples of a conversational AI workload? Each correct answer presents a complete solution.

  NOTE: Each correct selection is worth one point.

  A. a telephone answering service that has a pre-recorder message
  B. a chatbot that provides users with the ability to find answers on a website by themselves
  C. telephone voice menus to reduce the load on human resources
  D. a service that creates frequently asked questions (FAQ) documents by crawling public websites
  B. a chatbot that provides users with the ability to find answers on a website by themselves
  C. telephone voice menus to reduce the load on human resources

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  Explanation

  B: A bot is an automated software program designed to perform a particular task. Think of it as a robot without a body.

  C: Automated customer interaction is essential to a business of any size. In fact, 61% of consumers prefer to communicate via speech, and most of them prefer self-service. Because customer satisfaction is a priority for all businesses, self-service is a critical facet of any customer-facing communications strategy.

  Incorrect Answers:

  D: Early bots were comparatively simple, handling repetitive and voluminous tasks with relatively straightforward algorithmic logic. An example would be web crawlers used by search engines to automatically explore and catalog web content.

  References:

  https://docs.microsoft.com/en-us/azure/architecture/data-guide/big-data/ai-overviewhttps://docs.microsoft.com/en-us/azure/architecture/solution-ideas/articles/interactive-voice-response-bot