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| Section | Weight | Objectives |
|---|---|---|
| Identify the business value of generative AI solutions | 35–40% | - Foundational concepts of generative AI - Differences between generative AI and other AI types - Selecting generative AI solutions for business needs - Business benefits, ROI, and efficiency gains |
| Identify an implementation and adoption strategy for Microsoft's AI apps and services | 20–25% | - Measuring success and continuous improvement - Organizational readiness and change management - Adoption planning and scaling AI solutions - Responsible AI principles and governance |
| Identify benefits, capabilities, and opportunities for Microsoft's AI apps and services | 35–40% | - Microsoft 365 Copilot features and use cases - Aligning Microsoft AI tools to business requirements - Azure AI services and Azure AI Foundry tools - Microsoft Copilot Studio capabilities |
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NEW QUESTION # 18
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:
To use declarative agents that access work data (such as SharePoint or Graph connectors), users generally need a Microsoft 365 Copilot add-on license.
While Microsoft 365 Copilot Chat is included in most business subscriptions at no extra cost, its native capabilities are primarily limited to web-grounding and basic instructions. Accessing organizational data via agents typically requires one of the following licensing paths:
1. Microsoft 365 Copilot Add-on License
This is the most direct method. It grants full access to:
Declarative agents grounded in tenant data without additional usage fees.
Copilot Studio for authoring and managing these agents.
Embedded Copilot features in Word, Excel, Outlook, and Teams.
2. Pay-As-You-Go (Consumption) Model
If users do not have a full Copilot add-on license, organizations can enable metered usage (consumption-based billing).
Usage-based billing: Interactions with agents that access tenant data (SharePoint, connectors) consume "Copilot credits".
Requirement: This requires an Azure subscription and a billing policy set up in the Microsoft 365 admin center.
Reference:
https://support.microsoft.com/en-gb/topic/how-copilot-chat-works-with-and-without-a-microsoft-
365-copilot-license-5810b659-fbe0-48ee-9fe6-d731fe86cdeb
NEW QUESTION # 19
Hotspot Question
Select the answer that correctly completes the sentence.
Answer:
Explanation:
NEW QUESTION # 20
Select the answer that correctly completes the sentence.
Answer:
Explanation:
Explanation:
Microsoft 365 Copilot connectors are used to bring external line-of-business and third-party content into the Microsoft 365 ecosystem so Copilot can use that information in supported experiences. Connectors help make data from systems outside Microsoft 365 discoverable and usable through Microsoft Graph-based indexing, while still respecting permissions and governance controls. This allows users to ask natural-language questions and receive answers grounded in business data from connected systems. Image and video analysis is associated with vision services, not Copilot connectors. Security incident summaries are aligned with Microsoft Security Copilot. Suggested email replies are a Microsoft 365 Copilot productivity feature in Outlook, not the primary purpose of connectors.
NEW QUESTION # 21
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:
The correct answer is B. Retrieval Augmented Generation (RAG). The requirement is to answer questions based on the content of internal business reports. RAG is the correct architecture because it retrieves relevant information from trusted internal documents at runtime and supplies that information as grounding context to the generative AI model. This allows the agent to generate answers that are more accurate, current, and relevant to the organization's own content without retraining the model. Azure Machine Learning is mainly used for training and deploying predictive models. A Generative Adversarial Network is typically associated with synthetic content generation, especially images, and is not the right fit for document-grounded Q & A.
Natural language processing is too broad; RAG is the specific recommended pattern.
NEW QUESTION # 22
- Select the answer that correctly completes the sentence.
The cost of using generative AI language models is based typically on the number of __________ processed.
Answer:
Explanation:
Explanation:
Most generative AI language model pricing is based on token consumption , which measures the amount of text processed by the model. Tokens are sub-word units used internally by language models (for example, parts of words, whole words, or punctuation). When you send a prompt, the model consumes input tokens (your prompt + any system instructions + retrieved grounding context). When it generates a response, it consumes output tokens (the generated completion). Costs typically scale with the total input + output tokens processed, which is why long prompts, large grounding passages, and lengthy responses increase spend. This also explains why prompt optimization, response length limits, caching, and careful grounding are common cost-control techniques in enterprise solutions.
By contrast, "documents" is too coarse (a document can be 1 page or 500 pages). "Requests" is not the primary unit for most LLM pricing models because request sizes vary dramatically. "Words" is not used because the model's actual compute unit is tokens, and tokenization differs across languages and text patterns.
Therefore, the most accurate completion is tokens .
NEW QUESTION # 23
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