Microsoft AI-900 Online Practice Questions and Exam Preparation

Microsoft AI-900 Online Questions & Answers

• Question 31:

  DRAG DROP

  Match the computer vision service to the appropriate Al workload.

  To answer, drag the appropriate service from the column on the left to its workload on the right. Each service may be used once, more than once, or not at all.

  NOTE: Each correct match is worth one point.

  Select and Place:

  

  

• Question 32:

  You are developing a solution that uses the Text Analytics service.

  You need to identify the main talking points in a collection of documents.

  Which type of natural language processing should you use?

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

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  Explanation

  What is document and conversation summarization (preview), key features

  There are two types of document summarization this API provides:

  * Extractive summarization: Produces a summary by extracting salient sentences within the document.

  -> Multiple extracted sentences: These sentences collectively convey the main idea of the document. They're original sentences extracted from the input document's content.

  -> Etc.

  * Abstractive summarization References:

  https://learn.microsoft.com/en-us/azure/cognitive-services/language-service/summarization/overview?tabs=document-summarization

• Question 33:

  You plan to build a conversational Al solution that can be surfaced in Microsoft Teams. Microsoft Cortana, and Amazon Alexa.

  Which service should you use?

  A. Azure Bot Service
  B. Azure Cognitive Search
  C. Language service
  D. Speech
  A. Azure Bot Service

• Question 34:

  HOTSPOT

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

  

  

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  References:

  https://gallery.azure.ai/Experiment/Student-Loan-Repayment-Rate-Prediction

• Question 35:

  You need to identify the main topics discussed in a collection of documents.

  Which natural language processing task should you use?

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

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  Explanation

  Key phrase extraction identifies important topics or concepts within text, making it useful for summarization and topic identification.

• Question 36:

  What ate two common use cases for generative Al solutions? Each correct answer presents a complete solution.

  NOTE: Each correct selection is worth one point.

  A. generating draft responses for customer service agents
  B. creating original artwork from textual descriptions
  C. predicting sales revenue based on historical data
  D. classifying email messages as spam or non-spam
  A. generating draft responses for customer service agents
  B. creating original artwork from textual descriptions

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  Explanation

  Generative AI focuses on creating new content rather than just analyzing existing data. As per Microsoft's AI-900 curriculum and Azure OpenAI documentation, typical use cases include generating text, images, code, or other creative outputs based on input prompts.

  A. Generating draft responses for customer service agents - # Correct. GPT-based models can automatically generate draft replies to customer queries, enabling agents to refine responses and increase efficiency.

  B. Creating original artwork from textual descriptions - # Correct. DALL-E, available through Azure OpenAI, can produce unique images based on natural language prompts. Options C and D are incorrect because they involve predictive or classification models, not generative ones:

  C. Predicting sales revenue # Regression (machine learning).

  D. Classifying email messages # Classification (machine learning).

• Question 37:

  HOTSPOT

  You have a large dataset that contains motor vehicle sales data.

  You need to train an automated machine learning (automated ML) model to predict vehicle sale values based on the type of vehicle.

  Which task should you select? To answer, select the appropriate task in the answer area.

  NOTE: Each correct selection is worth one point.

  

  

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  Box 1: Regression

  AutoML Regression

  How to configure 1. Add the AutoML Regression component to your pipeline.

  2. Specify the Target Column you want the model to output 3. (Optional) View addition configuration settings: additional settings you can use to better control the training job. Otherwise, defaults are applied based on experiment selection and data.

  4. Etc.

  Box 2: View additional configuration settings

  Note: AutoML uses several methods to forecast time series values. These methods can be roughly assigned to two categories:

  Time series models that use historical values of the target quantity to make predictions into the future.

  Regression, or explanatory, models that use predictor variables to forecast values of the target.

  References:

  https://learn.microsoft.com/en-us/azure/machine-learning/component-reference-v2/regressionhttps://learn.microsoft.com/en-us/azure/machine-learning/concept-automl-forecasting-methods

• Question 38:

  A company wants to coordinate multiple AI services including conversational language understanding, language processing, and computer vision within a single solution.

  Which component can be used to orchestrate these services?

  A. Orchestrator workflow
  B. Data labeling project
  C. Azure resource group
  D. Storage account
  A. Orchestrator workflow

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  Explanation

  An orchestrator workflow coordinates multiple AI services and controls how they interact within a solution. This allows different services such as language processing and computer vision to work together. Resource groups and storage accounts manage infrastructure but do not orchestrate AI workflows.

• Question 39:

  Which type of machine learning should you use to identify groups of people who have similar purchasing habits?

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

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  Explanation

  Clustering is a machine learning task that is used to group instances of data into clusters that contain similar characteristics. Clustering can also be used to identify relationships in a dataset

  References:

  https://docs.microsoft.com/en-us/dotnet/machine-learning/resources/tasks

• Question 40:

  Predicting how many vehicles will travel across a bridge on a give day is an example of _______.

  Select the answer that correctly completes the sentence.

  A. regression
  B. translation
  C. classification
  D. clustering
  A. regression

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  Explanation

  Regression is a supervised machine learning technique used to predict numeric values.

  References:

  https://docs.microsoft.com/en-us/learn/modules/create-regression-model-azure-machine-learning-designer/