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| Section | Weight | Objectives |
|---|---|---|
| Identify benefits, capabilities, and opportunities for Microsoft's AI apps and services | 35-40% | - Identify benefits and capabilities of Azure AI services
|
| Identify an implementation and adoption strategy for Microsoft's AI apps and services | 20-25% | - Align an AI strategy with Microsoft responsible AI policies
|
| Identify the business value of generative AI solutions | 35-40% | - Identify the foundational concepts of generative AI
|
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NEW QUESTION # 80
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 - Microsoft 365 Copilot enable you to index data from multiple sources to make the data available in Copilot.
Microsoft 365 Copilot enables you to index data from multiple external, non-Microsoft sources- such as Salesforce, Jira, Confluence, and enterprise databases-into the Microsoft Graph to make that data available, searchable, and actionable within Copilot. This is primarily achieved through Microsoft Graph Connectors and Copilot Studio.
Box 2: Yes
Yes - You can build custom Microsoft 365 Copilot connector when the available connectors do not meet your data integration requirements.
Building a custom Microsoft 365 Copilot connector is the recommended approach when pre-built connectors do not meet specific data integration requirements, allowing you to bring external, line-of-business data into the Microsoft Graph for Copilot to reason over.
Box 3: No
No - To use Microsoft 365 Copilot connectors, you need a Microsoft Copilot Studio license.
This is not entirely correct. While Microsoft Copilot Studio is a primary tool for managing extensions, you do not necessarily need a standalone Copilot Studio license to use Microsoft 365 Copilot connectors.
Reference:
https://learn.microsoft.com/en-us/microsoft-365-copilot/extensibility/overview-copilot-connector
https://office365itpros.com/2025/09/29/microsoft-365-copilot-connector
https://learn.microsoft.com/en-us/microsoft-365-copilot/extensibility/cost-considerations
NEW QUESTION # 81
Select the answer that correctly completes the sentence.
Prompt engineering is the process of __________.
Answer:
Explanation:
Explanation:
crafting clear instructions to guide generative AI solutions in generating context-appropriate content.
Prompt engineering is fundamentally about how you communicate intent to a generative AI model so it produces outputs that meet business expectations. The best completion is "crafting clear instructions to guide generative AI solutions in generating context-appropriate content" because it captures the practical, day-to- day discipline: shaping the input (prompt) with the right task framing, constraints, context, and output format.
In real deployments, prompt engineering includes specifying the role and objective (for example, "act as a customer support agent"), providing the necessary context (product details, policy excerpts, audience), adding explicit requirements (tone, length, must/must-not statements), and defining structured output (JSON fields, bullet sections, headings). It can also include adding examples (few-shot prompting), clarifying what to do when information is missing, and instructing the model to cite only provided sources or to ask follow-up questions. These techniques reduce ambiguity, improve consistency, and lower the risk of hallucinations or off-brand responses.
The other options are not accurate definitions. "Integrating AI-powered tools into business workflows" describes solution adoption/integration, not prompt engineering. "Identifying and fixing errors in AI- generated content" is review/editing or quality assurance. "Designing, developing, and training generative AI models" is model development/ML engineering. Prompt engineering operates without changing model weights ; it's about steering model behavior through well-constructed instructions and context.
NEW QUESTION # 82
- 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 # 83
Your company has an AI solution that uses a prebuilt Azure OpenAI model to generate content.
You need to reduce the cost of solution while minimizing the impact on the quality of the generated output.
Which two actions should you perform? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
Answer: D,E
Explanation:
To reduce costs for a prebuilt Azure OpenAI model while maintaining high output quality, you should implement a combination of prompt optimization, caching, and model selection strategies.
[C]
1. Optimize Prompt Engineering
Reducing the number of tokens processed is the most direct way to lower costs.
Prompt Compression: Use tools like LLMLingua to remove redundant text from prompts without losing semantic meaning.
Task Decomposition: Break complex tasks into smaller, focused prompts to improve clarity and reduce expensive retries.
Shorten Context: Summarize long conversation histories or external RAG (Retrieval-Augmented Generation) documents before sending them to the model.
2. Implement Caching Mechanisms
[A]
3. Use Model Routing and Tiering
Matching request complexity to model capability can reduce costs by 30-60%.
*-> Model Cascading: Use a small, cheap model (e.g., GPT-4o-mini) for simple tasks like classification or basic formatting. Route only complex reasoning tasks to high-end models like GPT-4o.
Reasoning Effort Control: For newer models like GPT-5, adjust the reasoning_effort parameter to
"minimal" for straightforward tasks to save on compute.
Reference:
https://ekaivakriti.com/blog/how-to-reduce-openai-costs-by-60-percent/
NEW QUESTION # 84
Which benefit of generative AI enables organizations to accelerate content creation across departments such as marketing, HR, and communications?
Answer: B
Explanation:
Generative AI can produce first drafts of emails, reports, job descriptions, and other business documents. This accelerates content creation and reduces manual effort across organizational functions.
Reference:
https://learn.microsoft.com/en-us/training/modules/build-effective-generative-ai-solutions- organization/1-introduction
NEW QUESTION # 85
......
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