Microsoft Microsoft Certifications AI-900 Questions & Answers

• Question 201:

  Which two components can you drag onto a canvas in Azure Machine Learning designer? Each correct answer presents a complete solution. NOTE: Each correct selection is worth one point.

  A. dataset

  B. compute

  C. pipeline

  D. module

  Correct Answer: AD

  You can drag-and-drop datasets and modules onto the canvas.

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

• Question 202:

  Which metric can you use to evaluate a classification model?

  A. true positive rate

  B. mean absolute error (MAE)

  C. coefficient of determination (R2)

  D. root mean squared error (RMSE)

  Correct Answer: A

  What does a good model look like?

  An ROC curve that approaches the top left corner with 100% true positive rate and 0% false positive rate will be the best model. A random model would display as a flat line from the bottom left to the top right corner. Worse than random

  would dip below the y=x line.

  Reference:

  https://docs.microsoft.com/en-us/azure/machine-learning/how-to-understand-automated-ml#classification

• Question 203:

  What are three Microsoft guiding principles for responsible AI? Each correct answer presents a complete solution. NOTE: Each correct selection is worth one point.

  A. knowledgeability

  B. decisiveness

  C. inclusiveness

  D. fairness

  E. opinionatedness

  F. reliability and safety

  Correct Answer: CDF

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

• Question 204:

  You run a charity event that involves posting photos of people wearing sunglasses on Twitter. You need to ensure that you only retweet photos that meet the following requirements:

  1.

  Include one or more faces.

  2.

  Contain at least one person wearing sunglasses. What should you use to analyze the images?

  A. the Verify operation in the Face service

  B. the Detect operation in the Face service

  C. the Describe Image operation in the Computer Vision service

  D. the Analyze Image operation in the Computer Vision service

  Correct Answer: B

  Reference: https://docs.microsoft.com/en-us/azure/cognitive-services/face/overview

• Question 205:

  Your company is exploring the use of voice recognition technologies in its smart home devices. The company wants to identify any barriers that might unintentionally leave out specific user groups. This an example of which Microsoft guiding principle for responsible AI?

  A. accountability

  B. fairness

  C. inclusiveness

  D. privacy and security

  Correct Answer: C

  Reference:

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

  AI systems should empower everyone and engage people. AI should bring benefits to all parts of society, regardless of physical ability, gender, sexual orientation, ethnicity, or other factors.

  https://docs.microsoft.com/en-us/learn/modules/get-started-ai-fundamentals/7-understand-responsible-ai

• Question 206:

  You are designing an AI system that empowers everyone, including people who have hearing, visual, and other impairments. This is an example of which Microsoft guiding principle for responsible AI?

  A. fairness

  B. inclusiveness

  C. reliability and safety

  D. accountability

  Correct Answer: B

  Inclusiveness: At Microsoft, we firmly believe everyone should benefit from intelligent technology, meaning it must incorporate and address a broad range of human needs and experiences. For the 1 billion people with disabilities around the world, AI technologies can be a game-changer.

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

• Question 207:

  You are building an AI system.

  Which task should you include to ensure that the service meets the Microsoft transparency principle for responsible AI?

  A. Ensure that all visuals have an associated text that can be read by a screen reader.

  B. Enable autoscaling to ensure that a service scales based on demand.

  C. Provide documentation to help developers debug code.

  D. Ensure that a training dataset is representative of the population.

  Correct Answer: C

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

• Question 208:

  A company employs a team of customer service agents to provide telephone and email support to customers.

  The company develops a webchat bot to provide automated answers to common customer queries.

  Which business benefit should the company expect as a result of creating the webchat bot solution?

  A. increased sales

  B. a reduced workload for the customer service agents

  C. improved product reliability

  Correct Answer: B

• Question 209:

  For a machine learning progress, how should you split data for training and evaluation?

  A. Use features for training and labels for evaluation.

  B. Randomly split the data into rows for training and rows for evaluation.

  C. Use labels for training and features for evaluation.

  D. Randomly split the data into columns for training and columns for evaluation.

  Correct Answer: B

  https://docs.microsoft.com/en-us/azure/machine-learning/algorithm-module-reference/split-data

• Question 210:

  You build a machine learning model by using the automated machine learning user interface (UI). You need to ensure that the model meets the Microsoft transparency principle for responsible AI. What should you do?

  A. Set Validation type to Auto.

  B. Enable Explain best model.

  C. Set Primary metric to accuracy.

  D. Set Max concurrent iterations to 0.

  Correct Answer: B

  Model Explain Ability.

  Most businesses run on trust and being able to open the ML "black box" helps build transparency and trust. In heavily regulated industries like healthcare and banking, it is critical to comply with regulations and best practices. One key aspect

  of this is understanding the relationship between input variables (features) and model output. Knowing both the magnitude and direction of the impact each feature (feature importance) has on the predicted value helps better understand and

  explain the model. With model explain ability, we enable you to understand feature importance as part of automated ML runs.

  Reference:

  https://azure.microsoft.com/en-us/blog/new-automated-machine-learning-capabilities-in-azure-machine-learning-service/