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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Identify an implementation and adoption strategy for Microsoft AI apps and services | 20–25% | - AI adoption strategy
|
| Topic 2: Identify the business value of generative AI solutions | 35–40% | - AI value identification
|
| Topic 3: Identify benefits, capabilities, and opportunities for Microsoft AI apps and services | 35–40% | - Microsoft AI ecosystem
|
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17. Frage
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.
Antwort:
Begründung:
18. Frage
Hotspot Question
Select the answer that correctly completes the sentence.
Antwort:
Begründung:
Explanation:
Box: to create new content, such as text, images, or code.
The primary goal of generative AI is ______________________.
Generative AI (GenAI) is a type of artificial intelligence designed to create new, original content- including text, images, videos, audio, and code-by learning patterns from large, existing datasets. Unlike traditional AI that analyzes or classifies data, GenAI produces unique, human- like outputs, such as written stories, realistic images, or computer code.
Reference:
https://www.ai21.com/glossary/foundational-llm/generative-ai/
19. Frage
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.
Antwort:
Begründung:
Explanation:
Box 1: Yes
Prompt engineering is the practice of designing, structuring, and refining text inputs given to a generative AI model. By providing explicit constraints, formatting parameters, and guidelines, it directly steers the model to yield highly accurate and relevant output.
Box 2: No
The part of the prompt that includes examples to demonstrate the desired output structure or style is called Examples (often referred to in context as few-shot examples or learning data). The Instruction is the specific task, command, or action you want the model to perform (e.g.,
"Summarize this document" or "Translate the text").
Box 3: Yes
The Context provides background data, domain-specific details, situational boundaries, or source documents (such as grounding data) that the model must reference or operate within to formulate a targeted and contextualized response.
20. Frage
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.
Antwort:
Begründung:
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."
21. Frage
Your company discovers that several employees use personal ChatGPT accounts to assist with work tasks.
You are concerned about proprietary data being shared externally. You need to evaluate the business value of rolling out Microsoft 365 Copilot. Which capability is a key benefit of using Copilot instead of a personal ChatGPT account?
Antwort: A
Begründung:
The core business concern in the scenario is data leakage -employees using consumer tools where corporate data could be pasted, stored, or processed outside the organization's governance boundary. The key differentiator of Microsoft 365 Copilot is that it's designed to work inside your Microsoft 365 tenant and to respect the organization's existing security, compliance, identity, and data access controls. Therefore, D is the best answer: Copilot accesses internal work data (Microsoft Graph-connected content such as mail, files, chats, meetings) in accordance with existing Microsoft 365 policies and permissions -meaning it can only surface content the user is already allowed to access, and it operates under enterprise-grade controls (authentication, auditing, compliance boundaries, and admin governance).
Options B and C describe general generative AI capabilities that personal ChatGPT can also provide (brainstorming, drafting, rewriting). A can be done in multiple tools as well, and it is not the primary
"enterprise value" difference tied to the stated risk. The scenario's driver is governance: reducing the likelihood of proprietary data leaving controlled systems while still enabling productivity. Rolling out Copilot addresses that by providing "work-safe" AI anchored to organizational content and managed through the same tenant controls your company already uses.
22. Frage
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