Microsoft AI-900 Online Practice Questions and Exam Preparation

Microsoft AI-900 Online Questions & Answers

• Question 11:

  You have insurance claim reports that are stored as text.

  You need to extract key terms from the reports to generate summaries.

  Which type of AI workload should you use?

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

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  Explanation

  Key phrase extraction is the concept of evaluating the text of a document, or documents, and then identifying the main talking points of the document(s). Key phase extraction is a part of Text Analytics. The Text Analytics service is a part of the Azure Cognitive Services offerings that can perform advanced natural language processing over raw text.

  https://docs.microsoft.com/en-us/learn/modules/analyze-text-with-text-analytics-service/2-get-started-azure

  https://docs.microsoft.com/en-us/azure/architecture/data-guide/technology-choices/natural-language-processing

• Question 12:

  When you design an AI system to assess whether loans should be approved, the factors used to make the decision should be explainable.

  This is an example of which Microsoft guiding principle for responsible AI?

  A. transparency
  B. inclusiveness
  C. fairness
  D. privacy and security
  A. transparency

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  Explanation

  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.

  Incorrect Answers:

  B: 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.

  C: Fairness is a core ethical principle that all humans aim to understand and apply. This principle is even more important when AI systems are being developed. Key checks and balances need to make sure that the system's decisions don't discriminate or run a gender, race, sexual orientation, or religion bias toward a group or individual.

  D: 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.

  References:

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

• Question 13:

  HOTSPOT

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

  

  

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  References:

  https://www.baeldung.com/cs/feature-vs-labelhttps://machinelearningmastery.com/discover-feature-engineering-how-to-engineer-features-and-how-to-get-good-at-it/

• Question 14:

  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 15:

  Which three actions improve the quality of responses returned by a generative Al solution that uses GPT-3.5? Each correct answer presents a complete solution.

  NOTE: Each correct answer is worth one point.

  A. Add grounding data to prompts.
  B. Provide additional examples to prompts.
  C. Modify tokenization.
  D. Add training data to prompts.
  E. Modify system messages.
  B. Provide additional examples to prompts.
  D. Add training data to prompts.
  E. Modify system messages.

• Question 16:

  HOTSPOT

  Select the answer that correctly completes the sentence.

  

  

• Question 17:

  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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  The Text Analytics API is a cloud-based service that provides advanced natural language processing over raw text, and includes four main functions: sentiment analysis, key phrase extraction, named entity recognition, and language detection.

  Box 1: Yes

  You can detect which language the input text is written in and report a single language code for every document submitted on the request in a wide range of languages, variants, dialects, and some regional/cultural languages. The language code is paired with a score indicating the strength of the score.

  Box 2: No

  Box 3: Yes

  Named Entity Recognition: Identify and categorize entities in your text as people, places, organizations, date/time, quantities, percentages, currencies, and more. Well-known entities are also recognized and linked to more information on the web.

  References:

  https://docs.microsoft.com/en-us/azure/cognitive-services/text-analytics/overview

• Question 18:

  DRAG DROP

  Match the principles of responsible AI to appropriate requirements.

  To answer, drag the appropriate principles from the column on the left to its requirement on the right. Each principle may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.

  NOTE: Each correct selection is worth one point.

  Select and Place:

  

  

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  References:

  https://docs.microsoft.com/en-us/azure/cloud-adoption-framework/innovate/best-practices/trusted-aihttps://docs.microsoft.com/en-us/learn/modules/responsible-ai-principles/4-guiding-principles

• Question 19:

  HOTSPOT

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

  

  

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  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.

  Incorrect Answers:

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

  2. 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/algorithm-module-reference/linear-regressionhttps://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/machine-learning-initialize-model-clustering

• Question 20:

  A machine learning model produces a sentiment score ranging from -1 to 1 for each piece of text.

  How should this value be interpreted?

  A. It represents the probability of classification only
  B. It represents sentiment ranging from negative to positive
  C. It represents the number of entities in the text
  D. It represents the number of tokens in the text
  B. It represents sentiment ranging from negative to positive

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  Explanation

  Sentiment analysis models often output scores where negative values indicate negative sentiment, positive values indicate positive sentiment, and values close to zero represent neutral sentiment.