Microsoft AI-900 Online Practice Questions and Exam Preparation

Microsoft AI-900 Online Questions & Answers

• Question 21:

  HOTSPOT

  Select the answer that correctly completes the sentence.

  

  

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  This question is drawn from the Microsoft Azure AI Fundamentals (AI-900) syllabus section "Describe features of natural language processing (NLP) workloads on Azure." According to the Microsoft Learn materials, Natural Language Processing (NLP) is a branch of artificial intelligence that allows computers to analyze, understand, and generate human language. NLP enables machines to work with text or speech data in a way that extracts meaning, sentiment, and intent.

  Microsoft defines NLP as enabling scenarios such as language detection, text classification, key phrase extraction, sentiment analysis, and named entity recognition. The example given-classifying emails as "work-related" or "personal"-is a text classification task, which falls under NLP capabilities. The AI model processes the textual content of emails, identifies linguistic patterns, and categorizes them based on the detected topic or context. Let's analyze the other options: Predict the number of future car rentals # This is a forecasting task, handled by machine learning regression models, not NLP. Predict which website visitors will make a transaction # This is a classification or prediction problem in machine learning,

  not NLP, since it deals with behavioral or numerical data rather than language. Stop a process in a factory when extremely high temperatures are registered # This is an IoT or anomaly detection scenario, focusing on sensor data, not language understanding. Therefore, only classifying email messages as work-related or personal correctly represents an NLP use case. It illustrates how NLP can analyze written text and make intelligent categorizations-a key capability covered in AI-900's natural language workloads section.

• Question 22:

  What are three stages in a transformer model? Each correct answer presents a complete solution.

  NOTE: Each correct answer is worth one point.

  A. object detection
  B. embedding calculation
  C. tokenization
  D. next token prediction
  E. anonymization
  B. embedding calculation
  C. tokenization
  D. next token prediction

• Question 23:

  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 24:

  Which Azure Machine Learning designer component is used to divide a dataset into subsets?

  A. Train Model
  B. Score Model
  C. Split Data
  D. Join Data
  C. Split Data

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  Explanation

  The Split Data module is used to divide datasets into training and validation/test sets for model training and evaluation.

• Question 25:

  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: Knowledge mining

  You can use Azure Cognitive Search's knowledge mining results and populate your knowledge base of your chatbot.

  Box 3: Natural language processing

  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 26:

  You need to develop a web-based AI solution for a customer support system. Users must be able to interact with a web app that will guide them to the best resource or answer.

  Which service should you integrate with the web app to meet the goal?

  A. Azure Al Language Service
  B. Face
  C. Azure Al Translator
  D. Azure Al Custom Vision
  A. Azure Al Language Service

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  Explanation

  Azure AI Language Service is designed for natural language processing, which can be used to understand and respond to user queries. It is suitable for integrating with a web-based AI solution for a customer support system to guide users to the best resource or answer.

  References:

  https://azure.microsoft.com/en-us/services/cognitive-services/qna-maker/

• Question 27:

  HOTSPOT

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

  

  

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  Reliability and safety: 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.

  inclusiveness

  accountability

  reliability and safely

  fairness References:

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

• Question 28:

  What is the maximum image size that can be processed by using the prebuilt receipt model in Azure Al Document Intelligence?

  A. 5 MB
  B. 10MB
  C. 50 MB
  D. 100 MB
  D. 100 MB

• Question 29:

  HOTSPOT

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

  

  

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  Two-class classification provides the answer to simple two-choice questions such as Yes/No or True/False.

• Question 30:

  You need to predict the sea level in meters for the next 10 years.

  Which type of machine learning should you use?

  A. classification
  B. regression
  C. clustering
  B. regression

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  Explanation

  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.

  References:

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