Microsoft Fundamentals AI-900 Questions & Answers

• Question 21:

  DRAG DROP

  Match the types of machine learning to the appropriate scenarios.

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

  NOTE: Each correct selection is worth one point.

  Select and Place:

  

  Correct Answer:

  

  Box 1: Regression

  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.

  Box 2: Classification

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

  Box 3: Clustering 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. Reference: https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/linear-regression

• Question 22:

  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:

  

  Correct Answer:

  

  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.

  Reference:

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

• Question 23:

  DRAG DROP

  Match the Microsoft guiding principles for responsible AI to the appropriate descriptions.

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

  NOTE: Each correct selection is worth one point.

  Select and Place:

  

  Correct Answer:

  

  Box 1: 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.

  Box 2: Fairness Fairness: AI systems should treat everyone fairly and avoid affecting similarly situated groups of people in different ways. For example, when AI systems provide guidance on medical treatment, loan applications, or employment, they should make the same recommendations to everyone with similar symptoms, financial circumstances, or professional qualifications.

  We believe that mitigating bias starts with people understanding the implications and limitations of AI predictions and recommendations. Ultimately, people should supplement AI decisions with sound human judgment and be held accountable for consequential decisions that affect others.

  Box 3: Privacy and security As AI becomes more prevalent, protecting privacy and securing important personal and business information is becoming more critical and complex. With AI, privacy and data security issues require especially close attention because access to data is essential for AI systems to make accurate and informed predictions and decisions about people. AI systems must comply with privacy laws that require transparency about the collection, use, and storage of data and mandate that consumers have appropriate controls to choose how their data is used

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

• Question 24:

  HOTSPOT

  Select the answer that correctly completes the sentence.

  Hot Area:

  

  Correct Answer:

  

• Question 25:

  HOTSPOT

  Select the answer that correctly completes the sentence.

  Hot Area:

  

  Correct Answer:

  

  Box 1: Regression

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

  Scenarios for regression machine learning models

  Regression machine learning models are used in many industries. A few scenarios are:

  Using characteristics of houses, such as square footage and number of rooms, to predict home prices.

  Using characteristics of farm conditions, such as weather and soil quality, to predict crop yield.

  Using characteristics of a past campaign, such as advertising logs, to predict future advertisement clicks.

  Note: Loan chargeoff prediction with SQL Server, example.

  Solution details

  A charged off loan is a loan that is declared by a creditor (usually a lending institution) that an amount of debt is unlikely to be collected, usually when the loan repayment is severely delinquent by the debtor. Given that high chargeoff has a

  negative impact on lending institutions’ year-end financials, lending institutions often monitor loan chargeoff risk very closely to prevent loans from getting charged-off.

  There are multiple benefits for lending institutions to equip with loan chargeoff prediction data. Charging off a loan is the last resort that the bank will do on a severely delinquent loan, with the prediction data at hand, the loan officer could offer

  personalized incentives like lower interest rate or longer repayment period to help customers to keep making loan payments and thus prevent the loan of getting charged off. To get to this type of prediction data, often credit unions or banks

  manually handcraft the data based on customers' past payment history and performed simple statistical regression analysis.

  Reference: https://learn.microsoft.com/en-us/training/modules/create-regression-model-azure-machine-learning-designer/2-regression-scenarios

• Question 26:

  HOTSPOT

  Select the answer that correctly completes the sentence.

  Hot Area:

  

  Correct Answer:

  

  Box: Regression

  You can create a regression model with Azure Machine Learning designer.

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

  Reference:

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

• Question 27:

  HOTSPOT

  Select the answer that correctly completes the sentence.

  Hot Area:

  

  Correct Answer:

  

  Box 1: Numeric

  For regression problems, the label column must contain numeric data that represents the response variable. Ideally the numeric data represents a continuous scale.

  Reference: https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/train-model

• Question 28:

  You need to identify groups of rows with similar numeric values in a dataset. Which type of machine learning should you use?

  A. clustering

  B. regression

  C. classification

  Correct Answer: A

  Clustering is an unsupervised machine learning technique used to group similar entities based on their features. Learn how to create clustering models using Azure Machine Learning designer.

  Scenarios for clustering machine learning models

  Clustering machine learning models are used in many industries. A few scenarios are:

  Cluster customer attribute data into segments for marketing analysis.

  Cluster geographic coordinates into regions of high traffic in a city for a ride-share application.

  Cluster written feedback into topics to prioritize customer service changes.

  Reference: https://learn.microsoft.com/en-us/training/modules/create-clustering-model-azure-machine-learning-designer/2-clustering-scenarios

• Question 29:

  HOTSPOT

  Select the answer that correctly completes the sentence.

  Hot Area:

  

  Correct Answer:

  

• Question 30:

  HOTSPOT

  Select the answer that correctly completes the sentence.

  Hot Area:

  

  Correct Answer:

  

  Reference: https://learn.microsoft.com/en-us/azure/machine-learning/concept-designer