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NEW QUESTION # 26
Hotspot Question
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:
NEW QUESTION # 27
Hotspot Question
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:
Box 1: No
No - A generative AI solution is well-suited to predict next-quarter sales trends.
Predictive AI, not generative AI, is designed to forecast future trends and outcomes.
While Generative AI (GenAI) and Predictive AI both use historical data and machine learning, their primary purposes are distinct: Generative AI is designed to create new, original content (text, images, code), whereas Predictive AI is designed to forecast future trends and outcomes.
Box 2: Yes
Yes - A generative AI solution is well-suited to summarize lengthy policy documents.
Generative AI solutions are exceptionally well-suited to summarize lengthy policy documents, offering a transformative approach to document management that enhances efficiency, reduces manual labor, and improves accuracy. These systems can analyze hundreds of pages in minutes, extracting key obligations, deadlines, and critical information while removing redundancy.
Box 3: Yes
Yes - A generative AI solution can create product descriptions from product specifications.
Generative AI solutions significantly streamline e-commerce by transforming raw product specifications-such as materials, dimensions, and technical features-into engaging, human- readable product descriptions. By leveraging Large Language Models (LLMs) and Natural Language Processing (NLP), these tools can automate content creation for thousands of items in minutes, ensuring consistency in brand voice and improving search engine optimization (SEO) Reference:
https://nfina.com/generative-ai-vs-predictive-ai/
https://artificio.ai/blog/generative-ai-for-document-summarization-and-insights
https://describely.ai/blog/ai-generated-product-descriptions-for-ecommerce
NEW QUESTION # 28
Your company creates a custom Azure Machine Learning model that uses a generative AI assistant. The model initially delivers strong results. However, six months later, the model predictions become noticeably less accurate. What is a possible cause of the issue?
Answer: A
Explanation:
A common reason models degrade after being successful in production is data drift (also called concept drift).
Over time, the distribution of input data changes-for example, customer behavior shifts, product catalog changes, seasonality changes, new categories appear, sensors get recalibrated, or business processes evolve.
When the model sees data that differs from what it was trained on, its predictions can become less accurate.
This is exactly what option A describes and is the most likely "six months later" cause.
Option B is not a primary explanation for reduced predictive accuracy. More compute can improve throughput
/latency, but it does not inherently improve correctness of predictions. If anything, compute constraints typically cause timeouts or slower responses, not a systematic accuracy drop.
Option C (trained incorrectly) would usually manifest earlier-poor performance from the start-unless the
"incorrectness" is that the model was trained on a snapshot that later became stale (which again maps back to drift). The correct operational response is to monitor for drift, validate performance regularly, and retrain
/refresh the model using newer representative data and updated features/labels.
NEW QUESTION # 29
- Select the answer that correctly completes the sentence.
Adopting Microsoft responsible AI principles is important for your organization because the principles
__________.
Answer:
Explanation:
Explanation:
promote the ethical and accountable use of AI.
Microsoft's Responsible AI principles exist to guide organizations toward trustworthy, ethical, and accountable development and use of AI systems. Among the provided options, " promote the ethical and accountable use of AI " is the most accurate and comprehensive statement because it reflects the purpose of the principles themselves: ensuring AI is built and deployed in a way that respects people, reduces harm, and assigns clear responsibility for outcomes.
The other choices are either too absolute or too narrow. "Ensure that AI models deliver consistent and equitable results" is aspirational but not guaranteed-responsible AI practices reduce bias and improve reliability, but they cannot ensure perfect consistency or equity across all contexts. "Help organizations increase trust in fully autonomous AI systems" is not the intent; responsible AI emphasizes oversight and accountability, not pushing toward full autonomy. "Standardize model development practices across AI teams" can be a side benefit of governance, but it's not the primary reason to adopt responsible AI principles.
In practice, adopting these principles leads to concrete actions: defining governance and accountability, performing fairness and safety evaluations, protecting privacy and security, being transparent with users, and monitoring systems after deployment. These actions build stakeholder trust and enable sustainable adoption, but the foundational reason is that the principles provide a framework for ethical and accountable AI use.
NEW QUESTION # 30
Hotspot Question
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:
Box 1: Yes
Yes - For a user to access organizational data from a mobile device, the user needs a Microsoft
365 Copilot license.
To access, summarize, and query organizational data (such as emails, chats, documents in SharePoint/OneDrive, and calendar items) via Microsoft 365 Copilot on a mobile device, a user must have a Microsoft 365 Copilot license assigned to them.
This license is an add-on to a qualifying base subscription (such as Microsoft 365 E3, E5, Business Standard, or Business Premium).
Box 2: Yes
Yes - To reason over your organizational data by using Microsoft Graph, you need a Microsoft
365 Copilot license.
To use the advanced AI reasoning capabilities of Microsoft 365 Copilot-specifically to analyze, summarize, and query your organizational data (emails, chats, documents, meetings) via Microsoft Graph-you need a Microsoft 365 Copilot license.
Box 3: Yes
Yes - To use the Analyst agent, you need a Microsoft 365 Copilot license To use the Analyst agent, you generally need a Microsoft 365 Copilot add-on license.
While a basic version of Copilot Chat is available for many Microsoft 365 and Office 365 subscribers at no extra cost, advanced "Frontier" agents like Analyst and Researcher are specifically built for deep reasoning and multi-step tasks, which are reserved for licensed Copilot users.
Reference:
https://learn.microsoft.com/en-us/copilot/microsoft-365/microsoft-365-copilot-minimum- requirements
https://learn.microsoft.com/en-us/copilot/faq
https://it.osu.edu/news/2025/07/22/new-microsoft-365-copilot-agents-available-research-and- analysis
NEW QUESTION # 31
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