Microsoft AI-900 Online Practice Questions and Exam Preparation

Microsoft AI-900 Online Questions & Answers

• Question 351:

  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 "Identify features of common machine learning types", the classification technique is a type of supervised machine learning used to predict which category or class a new observation belongs to, based on patterns learned from labeled training data. In this scenario, a banking system that predicts whether a loan will be repaid is dealing with a binary outcome-either the loan will be repaid or will not be repaid. These two possible results represent distinct classes, making this problem a classic example of binary classification. During training, the model learns from historical data containing features such as customer income, credit score, loan amount, and repayment history, along with labeled outcomes (repaid or defaulted). After training, it can classify new applications into one of these two categories. The AI-900 curriculum distinguishes between three key supervised and unsupervised learning approaches:

  Classification: Predicts discrete categories (e.g., spam/not spam, fraud/not fraud, will repay/won't repay).

  Regression: Predicts continuous numerical values (e.g., house prices, sales forecast, temperature).

  Clustering: Groups data based on similarity without predefined labels (e.g., customer segmentation). Since the banking problem focuses on predicting a categorical outcome rather than a continuous numeric value, it fits squarely into the classification domain. In Azure Machine Learning, such tasks can be performed using algorithms like Logistic Regression, Decision Trees, or Support Vector Machines (SVMs), all configured for categorical prediction. Therefore, per Microsoft's official AI-900 learning objectives, a banking system predicting whether a loan will be repaid represents a classification type of machine learning problem.

• Question 352:

  You are building an AI system.

  Which task should you include to ensure that the service meets the Microsoft transparency principle for responsible AI?

  A. Ensure that all visuals have an associated text that can be read by a screen reader.
  B. Enable autoscaling to ensure that a service scales based on demand.
  C. Provide documentation to help developers debug code.
  D. Ensure that a training dataset is representative of the population.
  C. Provide documentation to help developers debug code.

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  Explanation

  References:

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

• Question 353:

  In which scenario should you use key phrase extraction?

  A. identifying whether reviews of a restaurant are positive or negative
  B. generating captions for a video based on the audio track
  C. identifying which documents provide information about the same topics
  D. translating a set of documents from English to German
  C. identifying which documents provide information about the same topics

• Question 354:

  Which AI service can you use to interpret the meaning of a user input such as "Call me back later?"

  A. Translator Text
  B. Text Analytics
  C. Speech
  D. Language Understanding (LUIS)
  D. Language Understanding (LUIS)

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  Explanation

  https://docs.microsoft.com/en-us/azure/cognitive-services/luis/what-is-luis

• Question 355:

  DRAG DROP

  Match the types of AI workloads to the appropriate scenarios.

  To answer, drag the appropriate workload type from the column on the left to its scenario on the right. Each workload type 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: Computer vision

  Optical character recognition (OCR), included in Computer Vision, allows you to extract printed or handwritten text from images, such as photos of street signs and products, as well as from documents - invoices, bills, financial reports, articles, and more

  Box 2: Natural language processing

  Choose a natural language processing service for sentiment analysis, topic and language detection, key phrase extraction, and document categorization.

  Box 3: Anomaly detection

  Box 4: Machine Learning (Clustering)

  Clustering is the task of dividing the population or data points into a number of groups such that data points in the same groups are more similar to other data points in the same group and dissimilar to the data points in other groups. It is basically a collection of objects on the basis of similarity and dissimilarity between them.

  References:

  https://docs.microsoft.com/en-us/azure/cognitive-services/computer-vision/overview-ocrhttps://docs.microsoft.com/en-us/azure/architecture/data-guide/technology-choices/natural-language-processinghttps://www.geeksforgeeks.org/clustering-in-machine-learning/

• Question 356:

  Your company wants to build a recycling machine for bottles. The recycling machine must automatically identify bottles of the correct shape and reject all other items.

  Which type of AI workload should the company use?

  A. anomaly detection
  B. conversational AI
  C. computer vision
  D. natural language processing
  C. computer vision

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  Explanation

  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. For example, Computer Vision can determine whether an image contains adult content, find specific brands or objects, or find human faces.

  References:

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

• Question 357:

  A developer wants to automatically train multiple machine learning models and select the best-performing one based on evaluation metrics.

  Which Azure capability should be used?

  A. AutoML
  B. Data labeling
  C. Speech synthesis
  D. Knowledge mining
  A. AutoML

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  Explanation

  AutoML automates model training, hyperparameter tuning, and model selection, allowing developers to quickly find the best-performing model without extensive manual experimentation.

• Question 358:

  HOTSPOT

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

  

  

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  References:

  https://docs.microsoft.com/en-us/azure/architecture/data-science-process/create-features

• Question 359:

  You need to extract printed and handwritten text from scanned documents.

  Which Azure AI service should you use?

  A. Face
  B. Computer Vision OCR
  C. Custom Vision
  D. Speech
  B. Computer Vision OCR

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  Explanation

  Optical Character Recognition (OCR) in the Computer Vision service extracts printed and handwritten text from images and documents, enabling digitization of content.

• Question 360:

  HOTSPOT

  You have the following apps:

  1. App1: Understands the public perception of a brand or topic

  2. App2: Applies profanity filters to speech-to-text

  What does each app use? To answer, select the appropriate options in the answer area.

  NOTE: Each correct selection is worth one point.