Microsoft AI-900 Online Practice Questions and Exam Preparation

Microsoft AI-900 Online Questions & Answers

• Question 51:

  Which statement is an example of a Microsoft responsible AI principle?

  A. AI systems must use only publicly available data
  B. AI systems must be transparent and inclusive
  C. AI systems must keep personal details public
  D. AI systems must protect the interests of the company
  B. AI systems must be transparent and inclusive

• Question 52:

  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 53:

  You have a solution that reads manuscripts in different languages and categorizes the manuscripts based on topic.

  Which types of natural language processing (NLP) workloads does the solution use?

  A. speech recognition and entity recognition
  B. speech recognition and language modeling
  C. translation and key phrase extraction
  D. translation and sentiment analysis
  C. translation and key phrase extraction

  ---

  Explanation

  Natural language processing (NLP) has many uses: sentiment analysis, topic detection, language detection, key phrase extraction, and document categorization.

  Need translation as different languages are used.

  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.

  References:

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

• Question 54:

  DRAG DROP

  Match the types of natural languages processing 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:

  

  

  ---

  Box 1: Entity recognition

  Classify a broad range of entities in text, such as people, places, organisations, date/time and percentages, using named entity recognition. Whereas:- Get a list of relevant phrases that best describe the subject of each record using key phrase extraction.

  Box 2: Sentiment analysis

  Sentiment Analysis is the process of determining whether a piece of writing is positive, negative or neutral.

  Box 3: Translation

  Using Microsoft's Translator text API

  This versatile API from Microsoft can be used for the following: Translate text from one language to another.

  Transliterate text from one script to another.

  Detecting language of the input text.

  Find alternate translations to specific text.

  Determine the sentence length.

  Incorrect Answers:

  Not Natural language processing (NLP), which is used for tasks such as sentiment analysis, topic detection, language detection, key phrase extraction, and document categorization.

  References:

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

• Question 55:

  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 56:

  You build a QnA Maker bot by using a frequently asked questions (FAQ) page.

  You need to add professional greetings and other responses to make the bot more user friendly.

  What should you do?

  A. Increase the confidence threshold of responses
  B. Enable active learning
  C. Create multi-turn questions
  D. Add chit-chat
  D. Add chit-chat

  ---

  Explanation

  References:

  https://docs.microsoft.com/en-us/azure/cognitive-services/qnamaker/how-to/chit-chat-knowledge-base?tabs=v1

• Question 57:

  CORRECT TEXT

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

  Computer vision capabilities can be Deployed to_____________________

  A. Integrate a facial recognition feature into an app.
  B. placeholder
  C. placeholder
  D. placeholder
  A. Integrate a facial recognition feature into an app.

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  Explanation

  Integrate a facial recognition feature into an app.

  

• Question 58:

  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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  References:

  https://docs.microsoft.com/en-us/azure/bot-service/bot-service-overview-introduction?view=azure-bot-service-4.0

• Question 59:

  You are building a conversational AI assistant that will interact with users across multiple platforms.

  Which platforms can AI agents be integrated with?

  A. Voice-assisted systems
  B. Web chat interfaces
  C. Microsoft Teams
  D. Azure Storage
  A. Voice-assisted systems
  B. Web chat interfaces
  C. Microsoft Teams

  ---

  Explanation

  AI agents can operate across multiple interaction channels such as voice assistants, web chat applications, and collaboration platforms like Microsoft Teams. Azure Storage is used for data storage and does not provide a user interaction interface.

• Question 60:

  HOTSPOT

  Select the answer that correctly completes the sentence.

  

  

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  In Azure Machine Learning Designer, the Dataset output visualization feature is specifically used to explore and understand the distribution of values in potential feature columns before model training begins. This capability is critical for data exploration and preprocessing, two essential stages of the machine learning pipeline described in the Microsoft Azure AI Fundamentals (AI-900) and Azure Machine Learning learning paths. When a dataset is imported into Azure Machine Learning Designer, users can right-click on the dataset output port and select "Visualize". This launches the dataset visualization pane, which provides detailed statistical summaries for each column, including: Data type (numeric, categorical, string, Boolean) Minimum, maximum, mean, and standard deviation values for numeric columns Frequency counts and distinct values for categorical columns Missing value counts.

  This visual inspection helps determine which columns should be used as features, which might need normalization or encoding, and which contain missing or irrelevant data. It is a vital step in ensuring the dataset is clean and ready for model training. Let's examine why other options are incorrect: Normalize Data module is used to scale numeric data, not to visualize distributions. Select Columns in Dataset module is used to include or exclude columns, not to analyze them. Evaluation results visualization feature is used after model training to interpret performance metrics like accuracy or recall, not data distributions. Therefore, based on official Microsoft documentation and AI-900 study materials, to explore the distribution of values in potential feature columns, you use the Dataset output visualization feature in Azure Machine Learning Designer.