Microsoft AI-900 Online Practice Questions and Exam Preparation

Microsoft AI-900 Online Questions & Answers

• Question 171:

  HOTSPOT

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

  

  

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  In machine learning, if you have labeled data, that means your data is marked up, or annotated, to show the target, which is the answer you want your machine learning model to predict.

  In general, data labeling can refer to tasks that include data tagging, annotation, classification, moderation, transcription, or processing.

  Incorrect Answers:

  Not features: 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://www.cloudfactory.com/data-labeling-guide

• Question 172:

  What is an example of unsupervised machine learning?

  A. classification
  B. clustering
  C. regression
  D. binary classification
  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 173:

  HOTSPOT

  Select the answer that correctly completes the sentence.

  

  

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  Handwriting OCR (optical character recognition) is the process of automatically extracting handwritten information from paper, scans and other low-quality digital documents.

  References:

  https://vidado.ai/handwriting-ocr

• Question 174:

  You need to implement a pre-built solution that will identify well-known brands in digital photographs.

  Which Azure Cognitive Services service should you use?

  A. Custom Vision
  B. Form Recognizer
  C. Face
  D. Computer Vision
  D. Computer Vision

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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 Computer 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 Computer 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/cognitive-services/computer-vision/concept-brand-detection

• Question 175:

  Which service should you use to extract text, key/value pairs, and table data automatically from scanned documents?

  A. Custom Vision
  B. Face
  C. Form Recognizer
  D. Language
  C. Form Recognizer

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  Explanation

  Form Recognizer applies advanced machine learning to accurately extract text, key-value pairs, tables, and structures from documents.

  References:

  https://azure.microsoft.com/en-us/services/form-recognizer/

• Question 176:

  Which two actions can you perform by using the Azure OpenAI DALL-E model? Each correct answer presents a complete solution.

  NOTE: Each correct answer is worth one point.

  A. Create images.
  B. Use optical character recognition (OCR).
  C. Detect objects in images.
  D. Modify images.
  E. Generate captions for images.
  A. Create images.
  D. Modify images.

• Question 177:

  DRAG DROP

  Match the types of machine learning to the appropriate scenarios.

  To answer, drag the appropriate machine learning type from the column on the left to its scenario on the right. Each machine learning 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: Regression

  In the most basic sense, regression refers to prediction of a numeric target.

  Linear regression attempts to establish a linear relationship between one or more independent variables and a numeric outcome, or dependent variable.

  You use this module to define a linear regression method, and then train a model using a labeled dataset. The trained model can then be used to make predictions.

  Box 2: Classification

  Classification is a machine learning method that uses data to determine the category, type, or class of an item or row of data.

  Box 3: Clustering

  Clustering, in machine learning, is a method of grouping data points into similar clusters. It is also called segmentation.

  Over the years, many clustering algorithms have been developed. Almost all clustering algorithms use the features of individual items to find similar items. For example, you might apply clustering to find similar people by demographics. You might use clustering with text analysis to group sentences with similar topics or sentiment.

  References:

  https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/linear-regression

• Question 178:

  You are developing a Chabot solution in Azure.

  Which service should you use to determine a user's intent?

  A. Translator
  B. Azure Cognitive Search
  C. Speech
  D. Language
  D. Language

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  Explanation

  In Azure, the Language service unifies several natural language capabilities, including LUIS, QnA Maker, and Text Analytics, into one comprehensive service. To determine a user's intent in a chatbot, you use the Conversational Language Understanding (CLU) feature of the Language service, which is the evolution of LUIS. CLU helps chatbots and applications comprehend natural language input by identifying the intent (the purpose of the user's statement) and extracting entities (important details). For example, when a user types "Book a meeting for tomorrow," the model recognizes the intent (BookMeeting) and the entity (tomorrow). The other options do not determine intent: Translator (A) is used for language translation. Azure Cognitive Search (B) retrieves documents based on search queries. Speech (C) converts audio to text but doesn't analyze meaning.

  References:

  Microsoft Learn - "Build conversational language understanding models with Azure Language service."

• Question 179:

  You have a solution that analyzes social media posts to extract the mentions of city names and the city names discussed most frequently.

  Which type of natural language processing (NLP) workload does the solution use?

  A. speech recognition
  B. sentiment analysis
  C. key phrase extraction
  D. entity recognition
  C. key phrase extraction

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  Explanation

  Key phrase extraction is one of the features offered by Azure Cognitive Service for Language, a collection of machine learning and AI algorithms in the cloud for developing intelligent applications that involve written language. Use key phrase extraction to quickly identify the main concepts in text. For example, in the text "The food was delicious and the staff were wonderful.", key phrase extraction will return the main topics: "food" and "wonderful staff".

  References:

  https://learn.microsoft.com/en-us/azure/cognitive-services/language-service/key-phrase-extraction/overview

• Question 180:

  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's Responsible AI Framework, the Reliability and Safety principle ensures that AI systems operate consistently, accurately, and as intended, even when confronted with unexpected data or edge cases. It emphasizes that AI systems must be tested, validated, and monitored to ensure stable performance and to prevent harm caused by inaccurate or unreliable outputs. In the given scenario, the AI system is designed not to provide predictions when key fields contain unusual or missing values. This approach demonstrates that the system is built to avoid unreliable or unsafe outputs that could result from incomplete or corrupted data. Microsoft explicitly outlines that reliable AI systems must handle data anomalies and input validation properly to prevent incorrect predictions.

  Here's how the other options differ: Inclusiveness ensures accessibility for all users, including those with disabilities or from different backgrounds. It's unrelated to prediction control or data reliability. Privacy and Security protects sensitive data and ensures proper handling of personal information, not system prediction logic. Transparency ensures that users understand how an AI system makes its decisions but doesn't address prediction reliability. Thus, stopping a prediction when data is incomplete or abnormal directly supports the Reliability and Safety principle - it ensures that the AI model functions correctly under valid conditions and avoids unintended or harmful outcomes. This principle aligns with Microsoft's Responsible AI guidance, which highlights that AI solutions must "operate reliably and safely, even under unexpected conditions, to protect users and maintain trust."