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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 Microsoft 365 Copilot and Microsoft Copilot
|
| Identify the business value of generative AI solutions | 35-40% | - Identify the foundational concepts of generative AI
|
| 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 # 16
Which business requirement most closely relates to grounding a generative AI model?
Answer: D
NEW QUESTION # 17
Your company is deploying Microsoft 365 Copilot. The deployment must provide users with access to the Researcher agent to search across data in Microsoft SharePoint.
You need to recommend a licensing plan for the solution.
What should you recommend?
Answer: C
Explanation:
To use the Researcher agent for searching across Microsoft SharePoint data, you need the Microsoft 365 Copilot add-on license.
The Researcher agent is a first-party reasoning agent included in the core experience for users with a paid Copilot license. It is designed for multi-step, in-depth research grounded in your Microsoft Graph data, including SharePoint, OneDrive, emails, and Teams.
Required Licensing Components
To deploy this capability, your organization must have:
1. A Qualifying Base License:
Enterprise: Microsoft 365 E3 or E5, or Office 365 E3 or E5.
Business: Microsoft 365 Business Standard or Business Premium.
*-> 2. Microsoft 365 Copilot Add-on:
This paid license (typically ~$30/user/month) unlocks the Researcher agent and the ability to search internal tenant data like SharePoint.
Reference:
https://learn.microsoft.com/en-us/copilot/microsoft-365/faq-researcher
NEW QUESTION # 18
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:
Microsoft Foundry is positioned as a unified platform experience for building, optimizing, and governing AI applications and agents. Microsoft explicitly emphasizes "fleetwide security and governance" and the ability to build and manage AI in a unified environment, which directly supports statement 1 being Yes : it is designed to help organizations build and operate generative AI solutions with centralized governance controls (for example, environment setup, data isolation, access control, and operational management).
For statement 2, Foundry supports scaling as demand increases . Microsoft documentation for Foundry- related model usage notes that as usage grows, Foundry can automatically increase quotas by moving users to higher tiers (and allows requesting additional quota). This is a concrete scalability mechanism tied to increased workload demand, so the statement is Yes .
For statement 3, Foundry is not limited to text-only generative AI. Microsoft provides "Azure Vision in Foundry Tools," which delivers computer vision capabilities such as analyzing images, reading text (OCR), and other image-processing features. That means Foundry can be used for image recognition/computer vision workloads, so the statement is Yes .
NEW QUESTION # 19
HOTSPOT - 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
* Larger datasets can increase the cost of a generative AI solution that uses an Azure Machine Learning workspace. Answer: Yes
* The cost of consuming Azure OpenAI models is primarily identified by the number of input and output tokens processed. Answer: Yes
* The cost of custom generative AI solutions always remains the same regardless of the model version or capability used. Answer: No
* Yes - In Azure Machine Learning, cost is driven by the resources you consume to store, process, and train. Larger datasets typically require more storage, more data transfer, and more compute time for preprocessing, training, evaluation, and experimentation. Even if you are not training foundation models, handling larger corpora can increase pipeline duration and the number/size of compute instances used, which increases overall cost.
* Yes - For Azure OpenAI usage under Standard (on-demand), pricing is primarily tied to token-based consumption (input tokens + output tokens). The more context you send and the longer the generated responses, the more tokens you consume, and the higher the cost. This is why prompt optimization, response length controls, and grounding strategies matter for cost management.
* No - Costs vary with model choice and capability. Different model families and versions have different price points, and larger/more capable models generally cost more per token or per unit of throughput. Additionally, architecture choices (RAG, vector search, caching), usage patterns, and throughput requirements can significantly change total cost-so it is never "always the same."
NEW QUESTION # 20
Which business requirement most closely relates to grounding a generative AI model?
Answer: D
Explanation:
Ensuring that verified company data sources are used for response generation relates to grounding a generative AI model by anchoring its outputs in trusted, domain-specific, or enterprise-specific information. This process bridges the gap between the general knowledge a model has from its training data and the specific, up-to-date facts required for accurate, trustworthy business applications.
Reference:
https://decagon.ai/glossary/what-is-ai-grounding
NEW QUESTION # 21
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