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| Section | Weight | Objectives |
|---|---|---|
| Identify the business value of generative AI solutions | 35-40% | - Identify the foundational concepts of generative AI
|
| 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
|
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NEW QUESTION # 82
Select the answer that correctly completes the sentence.
Answer:
Explanation:
Explanation:
An AI champions program includes users who advocate for AI by learning, sharing, and identifying use cases.
Champions are not primarily the infrastructure team, analytics team, or executive strategy group. Their value is adoption enablement. They help colleagues understand AI tools, share practical usage patterns, collect feedback, and discover department-specific opportunities where AI can improve productivity or decision- making. Strategy and governance are usually handled by leadership, AI councils, legal, risk, compliance, security, and technical stakeholders. Infrastructure is handled by IT and engineering teams. Champions sit closer to end users and help translate the AI strategy into practical daily usage. This makes them critical for scaling proficiency and adoption across the organization.
NEW QUESTION # 83
Your company plans to use generative AI to help project managers and engineers work with construction blueprints stored as PDF files.
You need to recommend a generative AI solution that meets the following business requirements:
- Processes both images and text
- Summarizes the design of a building
- Answers user questions about a building's design
- Extracts information from blueprints, such as the location of
electrical, heating, and plumbing systems
What should you recommend?
Answer: B
Explanation:
The decisive requirement is the first one: "Processes both images and text." Blueprints are inherently visual (floor plans, schematic diagrams, spatial layouts) and textual (labels, dimensions, annotations). A multi-modal model can ingest the PDF as imagery and text together and reason across both - which is what every one of the four requirements depends on.
Summarizing a building's design, answering questions about it, and locating electrical/heating/plumbing systems all require understanding the visual diagram, not just reading words off the page.
NEW QUESTION # 84
Your company has a Microsoft 365 subscription and uses Microsoft 365 Copilot Chat. Some users need to build and use declarative agents that can access work data. Which type of license should you recommend for the users?
Answer: C
Explanation:
The requirement is specific: users must build and use declarative agents that can access work data (tenant data
/ organizational context). Microsoft's licensing guidance for Copilot extensibility ties use of declarative agents to having the appropriate Copilot entitlement that enables tenant grounding and organizational data access. In Microsoft's cost and licensing considerations for declarative agents, Microsoft states that to use a declarative agent, users must have a Microsoft 365 Copilot add-on license (or an equivalent Copilot Chat add- on path tied to eligible licensing). Therefore, among the provided options, the best recommendation is A.
Option B (Copilot Studio user license) is primarily about authoring/building agents in Copilot Studio, but it is not, by itself, the licensing prerequisite that grants end users the right to use those agents with full Microsoft
365 Copilot capabilities and work-data grounding inside the Microsoft 365 Copilot environment. Publishing
/building can be separate from the end-user entitlement to use the agent with organizational context.
Option C (Copilot Chat pay-as-you-go) can enable usage-based access to declarative agents in some configurations, but the question asks for the best license recommendation for users who need work-data access through declarative agents. The Microsoft 365 Copilot add-on is the straightforward, fully supported entitlement for that scenario.
NEW QUESTION # 85
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 - A barrier to AI adoption can include data limitations and lock of AI readiness and skills.
Those are two of the most significant hurdles organizations face today. In fact, a 2025 PEX Report found that 52% of professionals cite data quality and availability as their primary challenge, closely followed by a lack of internal expertise at 49%.
Box 2: Yes
Yes - Organizations often struggle with AI adoptions because they prioritize technology selection before defining clear business use cases.
It is essentially the "hammer looking for a nail" problem; organizations often get dazzled by the
"hammer" (AI) and start swinging before they even know what they are trying to build.
Starting with technology rather than business value is a primary reason why 88% to 95% of AI pilots fail to deliver measurable results. This "technology-first" trap often leads to "random acts of AI"-expensive experiments that succeed in a lab but fail to solve any real operational bottlenecks.
Box 3: Yes
Yes - A lack of cross-functional collaboration is a common barrier to AI adoption.
That is spot on-it's often the "human" silos, not the hardware, that stall AI progress. While technical hurdles like data quality are significant, research consistently identifies a lack of cross- functional collaboration as a primary "hidden" barrier to successful AI adoption.
Reference:
https://www.ibm.com/think/insights/ai-adoption-challenges
https://www.rapidops.com/blog/why-ai-fails/
https://www.gartner.com/peer-community/post/preferred-tactics-building-effective-collaboration- cross-functional-teams-involved-ai-governance-risk-management-e-g-joint
NEW QUESTION # 86
Hotspot Question
Select the answer that correctly completes the sentence.
Answer:
Explanation:
Explanation:
Box: model inaccuracy
When a generative AI model produces output that seems realistic but contains incorrect information, the behavior is known as _______________.
That specific behavior-where the AI generates plausible-sounding but factually incorrect information-is known as hallucination.
While "model inaccuracy" is a broad way to describe it, "hallucination" specifically refers to when a generative AI model-like a large language model (LLM)-produces incorrect, misleading, or entirely fabricated information while presenting it as fact with a confident and plausible tone.
Reference:
https://www.techtimes.com/articles/314230/20260122/ai-hallucinations-explained-why-generative- ai-often-produces-inaccurate-results.htm
NEW QUESTION # 87
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