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NEW QUESTION # 77
For each of the following statements, select Yes if the statement is true. Otherwise, select No. NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Answer Area
* Allowing AI models to make autonomous decisions supports the Microsoft responsible AI principle of accountability. Answer: No
* Regularly testing AI models for fairness and inclusiveness helps ensure they align with Microsoft's Responsible AI principles. Answer: Yes
* Protecting user data and limiting access to personal information supports the Microsoft responsible AI principles of privacy and security. Answer: Yes Microsoft's Responsible AI principles emphasize that people and organizations must remain accountable for AI systems and their outcomes. Accountability is strengthened by governance, human oversight, clear ownership, auditability, and processes to review and address issues-not by letting models make unchecked autonomous decisions. Therefore, statement 1 is No : increasing autonomy can actually increase risk unless paired with human-in-the-loop controls and clear escalation paths, because accountability requires clear responsibility for decisions and impacts.
Statement 2 is Yes because fairness and inclusiveness are explicitly supported through ongoing evaluation.
Regular testing helps detect disparate impact, performance gaps across user groups, and unintended bias introduced by data drift or changes in usage patterns. It's not a one-time activity; it's continuous assurance that the system behaves appropriately as conditions change.
Statement 3 is Yes because privacy and security are directly supported by protecting personal/sensitive data, enforcing least privilege access, and implementing controls such as data loss prevention, encryption, access logging, and strong identity governance. Limiting access to personal information reduces exposure and supports compliance obligations while aligning with privacy-by-design and secure-by-design expectations for AI-enabled solutions.
NEW QUESTION # 78
For each of the following statements, select Yes if the statement is true. Otherwise, select No. NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Answer Area
* Allowing AI models to make autonomous decisions supports the Microsoft responsible AI principle of accountability. Answer: No
* Regularly testing AI models for fairness and inclusiveness helps ensure they align with Microsoft's Responsible AI principles. Answer: Yes
* Protecting user data and limiting access to personal information supports the Microsoft responsible AI principles of privacy and security. Answer: Yes Microsoft's Responsible AI principles emphasize that people and organizations must remain accountable for AI systems and their outcomes. Accountability is strengthened by governance, human oversight, clear ownership, auditability, and processes to review and address issues-not by letting models make unchecked autonomous decisions. Therefore, statement 1 is No : increasing autonomy can actually increase risk unless paired with human-in-the-loop controls and clear escalation paths, because accountability requires clear responsibility for decisions and impacts.
Statement 2 is Yes because fairness and inclusiveness are explicitly supported through ongoing evaluation.
Regular testing helps detect disparate impact, performance gaps across user groups, and unintended bias introduced by data drift or changes in usage patterns. It's not a one-time activity; it's continuous assurance that the system behaves appropriately as conditions change.
Statement 3 is Yes because privacy and security are directly supported by protecting personal/sensitive data, enforcing least privilege access, and implementing controls such as data loss prevention, encryption, access logging, and strong identity governance. Limiting access to personal information reduces exposure and supports compliance obligations while aligning with privacy-by-design and secure-by-design expectations for AI-enabled solutions.
NEW QUESTION # 79
For each of the following statements, select Yes if the statement is true. Otherwise, select No. NOTE: Each correct selection is worth one point
Answer:
Explanation:
Explanation:
* For a user to access organizational data from a mobile device, the user needs a Microsoft 365 Copilot license. No
* To reason over your organizational data by using Microsoft Graph, you need a Microsoft 365 Copilot license. Yes
* To use the Analyst agent, you need a Microsoft 365 Copilot license. Yes Top of Form Bottom of Form The key distinction here is between Copilot Chat capabilities available with a standard Microsoft 365 subscription and the full Microsoft 365 Copilot add-on that enables richer, in-context experiences grounded in organizational data.
Mobile access to organizational data does not inherently require a Microsoft 365 Copilot license. Microsoft's Microsoft 365 Copilot app (and related mobile experiences) can provide Copilot Chat for work/school accounts with a Microsoft 365 license, so simply accessing organizational content on a mobile device is not the same as having the paid Copilot add-on. The statement claims a Copilot license is required just to access org data from mobile, which is too broad-there are mobile Microsoft 365 apps that access org data without the Copilot add-on.
Reasoning over organizational data via Microsoft Graph is a core value proposition of Microsoft 365 Copilot.
Microsoft documents explain that Microsoft 365 Copilot connects LLMs to your organization's content and context through Microsoft Graph and generates responses "anchored" in organizational data. That deeper integration is tied to the Microsoft 365 Copilot experience (an add-on license).
Analyst is a "reasoning agent" within Microsoft 365 Copilot and Microsoft states that users with a Microsoft
365 Copilot license can use Analyst (with defined usage limits). Therefore, the Analyst agent requires the Microsoft 365 Copilot license.
NEW QUESTION # 80
Your company stores hundreds of internal business reports.
You need to recommend a generative AI solution that uses an agent to answer questions based on the content in the reports.
What should you include in the recommendation?
Answer: C
Explanation:
A RAG-based generative AI solution for hundreds of internal reports uses an agent to query a vector database, ensuring answers are grounded in proprietary data, minimizing hallucinations.
The system parses reports into embeddings, retrieves relevant chunks via semantic search, and uses an LLM to generate precise, cited answers.
Key Components & Architecture
Data Ingestion & Embedding: Convert thousands of PDFs, docs, or text files into vector embeddings stored in a vector database (e.g., Pinecone, Azure AI Search, Milvus).
Agentic Workflow: Implement an intelligent agent that decomposes complex user questions into sub-queries, searches multiple data sources, and refines answers.
Retrieval Mechanism: Use hybrid search (combining semantic and keyword search) for high accuracy in finding relevant report snippets.
Generation & Grounding: The LLM receives the prompt with retrieved content to generate answers, improving quality and reducing errors.
Incorrect:
[Not D]
The GAN can be used to improve the quality of document embeddings or to generate realistic, synthetic training data for the agent's semantic search, particularly in scenarios where data is unstructured or sparse.
NEW QUESTION # 81
Your company plans to use an AI-powered solution to analyze customer feedback for insights related to future product designs. You need to mitigate the privacy risks associated with the solution. What is the best approach to achieve the goal? Select the BEST answer.
Answer: D
Explanation:
The strongest privacy risk mitigation for analyzing customer feedback is to minimize personal data exposure while preserving the analytical value of the text. A is best because anonymizing (or de-identifying) the dataset removes direct identifiers (names, emails, phone numbers, addresses, account IDs) and reduces the likelihood of privacy breaches, unauthorized re-identification, or inadvertent leakage in model outputs. This aligns with privacy-by-design and the general principle of data minimization: only retain the information necessary for the business purpose.
B is usually impractical and undermines business value and auditability; organizations often need retention windows for validation, traceability, and improvement. C is not the best privacy mitigation: keeping data attributable to individuals increases privacy exposure; while deletion-on-request is important for compliance, it's not the primary mechanism to reduce privacy risk during analysis. D is explicitly poor practice; privacy reviews should occur throughout the lifecycle (requirements, design, data acquisition, testing, deployment, monitoring), not only at the end. Therefore, anonymizing/removing PII at the source is the best first-line approach.
NEW QUESTION # 82
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