Get MS-012 Explore the Microsoft approach to AI Microsoft Quiz Answers
Table of Contents
This learning path examines Microsoft’s AI blueprint guidelines, along with the six principles that form the foundation of its Responsible AI Standard.
Prerequisites:
- Students should have basic functional experience with Microsoft 365 services.
- Students must have a proficient understanding of general IT practices.
Module 1: Examine the Microsoft AI blueprint
This module examines the following key aspects of Microsoft’s AI blueprint: ethical principles, AI safety networks, technical stacks, AI transparency, and guidelines for human-AI interactions.
Important:
The information in this Module applies to the Microsoft 365 Copilot Early Access Program, an invite-only paid preview program for commercial customers. Details are subject to change as copilot becomes generally available.
Note:
This content was partially created with the help of AI. An author reviewed and revised the content as needed. Read more.
Learning objectives:
By the end of this module, you should be able to:
- Define the six ethical principles outlined in Microsoft’s AI blueprint
- Describe Microsoft’s commitment to AI safety networks
- Understand the importance of implementing AI safety brakes
- Describe the foundation stack used in Microsoft’s AI foundation models
- Understand why Microsoft is committed to promoting AI transparency
- Explain Microsoft’s guidelines for human-AI interactions
This module is part of these learning paths:
Quiz 1: Review Microsoft’s ethical principles for AI
Q1. Which Microsoft AI ethical principle helps organizations understand the data and algorithms used to train the AI model, the transformation logic applied to the data, the final model generated, and its associated assets?
- Accountability
- Reliability and Safety
- Transparency
Quiz 2: Examine the technology stack for Microsoft’s AI foundation models
Q1. What type of accelerators does Microsoft use in its supercomputing clusters to train its foundation models?
- CPUs only
- GPUs only
- A combination of CPUs and GPUs
Quiz 3: Knowledge Check
Q1. What is the AIRiskManagementFramework developed by NIST?
- A framework for managing AI risks and promoting trustworthy and responsible development and use of AI systems
- A framework for developing new AI technologies from scratch
- A framework for regulating the use of AI in the government sector
Q2. Which Microsoft AI ethical principle helps organizations understand the data and algorithms used to train the AI model, the transformation logic applied to the data, the final model generated, and its associated assets?
- Accountability
- Reliability and Safety
- Transparency
Q3. What technique does Microsoft use to interrupt an AI system and prevent unsafe or incorrect behavior?
- Tripwire triggers
- Differential privacy
- Reward modeling
Module 2: Examine how Microsoft is committed to Responsible AI
This module examines Microsoft’s Responsible AI Standard, which is an approach to developing, assessing, and deploying AI systems in a safe, trustworthy, and ethical way.
Important:
The information in this Module applies to the Microsoft 365 Copilot Early Access Program, an invite-only paid preview program for commercial customers. Details are subject to change as copilot becomes generally available.
Note:
This content was partially created with the help of AI. An author reviewed and revised the content as needed. Read more.
Learning objectives:
By the end of this module, you should be able to:
- Explain Microsoft’s approach to responsible AI.
- Describe the six principles that make up Microsoft’s AI Standard.
- Explain how Microsoft’s fairness principle is designed to detect bias and mitigate unfair impacts.
- Describe how Microsoft’s reliability and safety principle minimizes unintended harm from AI systems.
- Explain how Microsoft’s security and privacy principle helps prevent abuse and breach of user trust.
- Understand how Microsoft’s inclusiveness principle helps its AI systems be fair, accessible, and empower everyone.
- Discuss how Microsoft’s transparency principle makes its AI systems understandable and interpretable.
- Explain how Microsoft’s accountability principle drives it to continuously monitor its AI systems’ performance and mitigate risks.
This module is part of these learning paths:
Quiz 1: Examine the Microsoft approach to Responsible AI
Q1. Which of the following factors does Microsoft use to guide its actions relating to Responsible AI?
- Its competitors’ actions
- Microsoft technologies like InterpretML and Fairlearn
- Decades of research on AI, grounding, and privacy-preserving machine learning
Quiz 2: Examine Microsoft AI principles – Reliability and Safety
Q1. Which of the following items is an example of the complexity involved in developing safe and reliable AI?
- A deep learning image classifier develops highly intricate feature detection capabilities that emerge from its training process
- A recruiting algorithm trained only on data from current employees
- An autonomous vehicle model may fail to handle icy road conditions if its training emphasized dry scenarios
Quiz 3: Examine Microsoft AI principles – Transparency
Q1. Which of the following items describes Microsoft’s Responsible AI principle related to Transparency?
- AI creators should be responsible for how their systems operate
- AI systems should be understandable and interpretable
- AI systems are fair, accessible, and empower everyone
Quiz 4: Knowledge Check
Q1. What does Microsoft’s Responsible AI standard for Inclusiveness mean?
- AI systems are only available in developed countries
- AI systems work well for some people and disadvantage others
- AI systems must be designed to include all people, communities, and geographies, especially those areas of society historically underrepresented
Q2. What is the purpose of differential privacy?
- To introduce mathematical noise to query results on a dataset to obscure any one individual’s data while preserving overall analytics
- To collect as much raw data as possible
- To generalize data to ensure attackers can distinguish each row in a dataset from at least k-1 other rows
Q3. What is one common type of harm caused by AI systems?
- Harm of innovation
- Harm of creativity
- Harm of allocation
Find More Microsoft Quiz Answers >>
Work Smarter with Microsoft PowerPoint Coursera Quiz Answers
Work Smarter with Microsoft Excel Coursera Quiz Answers
Microsoft Azure Management Tools and Security Solutions Quiz Answers
