Microsoft AI-900 Online Practice Questions and Exam Preparation

Microsoft AI-900 Online Questions & Answers

• Question 111:

  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 112:

  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 113:

  HOTSPOT

  Select the answer that correctly completes the sentence.

  

  

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  Box: Named Entity Recognition (NER)

  Named Entity Recognition (NER) is one of the features offered by Azure AI Language, a collection of machine learning and AI algorithms in the cloud for developing intelligent applications that involve written language. The NER feature can identify and categorize entities in unstructured text. For example: people, places, organizations, and quantities.

  Incorrect:

  * key phrase extraction

  The Key Phrase Extraction skill evaluates unstructured text, and for each record, returns a list of key phrases. This skill uses the Key Phrase machine learning models provided by Azure AI Language.

  This capability is useful if you need to quickly identify the main talking points in the record. For example, given input text "The food was delicious and there were wonderful staff", the service returns "food" and "wonderful staff".

  References:

  https://learn.microsoft.com/en-us/azure/ai-services/language-service/named-entity-recognition/overview

• Question 114:

  Which type of machine learning should you use to predict the number of gift cards that will be sold next month?

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

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  Explanation

  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/machine-learning-initialize-model-clustering

• Question 115:

  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/machine-learning/how-to-label-data

• Question 116:

  What is a use case for classification?

  A. predicting how many cups of coffee a person will drink based on how many hours the person slept the previous night.
  B. analyzing the contents of images and grouping images that have similar colors
  C. predicting whether someone uses a bicycle to travel to work based on the distance from home to work
  D. predicting how many minutes it will take someone to run a race based on past race times
  C. predicting whether someone uses a bicycle to travel to work based on the distance from home to work

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  Explanation

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

  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 117:

  Which parameter should you configure to produce a more diverse range of tokens in the responses from a chat solution that uses the Azure OpenAI GPT-3.5 model?

  A. Max response
  B. Past messages included
  C. Presence penalty
  D. Stop sequence
  C. Presence penalty

• Question 118:

  You have a security system that analyzes images from CCTV to provide authorized staff entry into restricted area.

  Which type of computer vision does the system use?

  A. optical character recognition (OCR)
  B. semantic segmentation
  C. facial detection and facial recognition
  D. image analysis
  C. facial detection and facial recognition

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  Explanation

  Embed facial recognition into your apps for a seamless and highly secured user experience. No machine-learning expertise is required. Features include face detection that perceives facial features and attributes - such as a face mask, glasses, or face location - in an image, and identification of a person by a match to your private repository or via photo ID.

  Note: Facial recognition CCTV cameras can take images of faces they film and compare them against a predefined database of images to determine if there is a match.

  References:

  https://azure.microsoft.com/en-in/products/cognitive-services/face

• Question 119:

  HOTSPOT

  Select the answer that correctly completes the sentence.

  

  

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  Box 1: Facial analysis.

  In another change, we [Microsoft] will retire facial analysis capabilities that purport to infer emotional states and identity attributes such as gender, age, smile, facial hair, hair, and makeup. We collaborated with internal and external researchers to understand the limitations and potential benefits of this technology and navigate the tradeoffs. In the case of emotion classification specifically, these efforts raised important questions about privacy, the lack of consensus on a definition of "emotions," and the inability to generalize the linkage between facial expression and emotional state across use cases, regions, and demographics. API access to capabilities that predict sensitive attributes also opens up a wide range of ways they can be misused - including subjecting people to stereotyping, discrimination, or unfair denial of services.

  References:

  https://azure.microsoft.com/en-us/blog/responsible-ai-investments-and-safeguards-for-facial-recognition/300

• Question 120:

  You are building an Al-based loan approval app.

  You need to ensure that the app documents why a loan is approved or rejected and makes the report available to the applicant.

  This is an example of which Microsoft responsible Al principle?

  A. fairness
  B. inclusiveness
  C. transparency
  D. accountability
  C. transparency