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질문 # 11
For each of the following statements, select Yes if the statement is true. Otherwise, select No. NOTE: Each correct selection is worth one point.
정답：
설명：
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 .
질문 # 12
Your company manages an online catalog of office supplies.
You plan to use a generative AI solution to create product descriptions for your company's website. The solution must meet the following requirements:
- Ensure that the descriptions can be posted immediately after they are created.
- Enable the selection and inclusion of product details in each
description.
- Be fast and simple for non-technical staff to use.
What is the best type of solution to use? More than one answer choice may achieve the goal.
Select the BEST answer.
정답：B
설명：
Using the Researcher agent within Microsoft 365 Copilot provides a highly effective solution for creating and immediately posting product descriptions. It allows non-technical staff to generate tailored, brand-aligned content by leveraging both internal product data and web research, allowing for immediate publication.
Reference:
https://learn.microsoft.com/en-us/dynamics365/business-central/ai-overview
질문 # 13
- Select the answer that correctly completes the sentence.
Using high-quality grounding data in a generative AI solution __________.
정답：
설명：
Explanation:
improves the accuracy and reliability of the predictions and outputs of AI.
High-quality grounding data improves a generative AI solution by anchoring responses to trusted, relevant, and up-to-date information , which increases the likelihood that outputs are accurate, consistent, and aligned with the organization's expectations. This is why the best completion is " improves the accuracy and reliability of the predictions and outputs of AI ." When the model is given authoritative context (for example, approved policy text, product specifications, knowledge base articles, or controlled enterprise content), it has less need to "guess" based on general patterns in its training data. That reduces hallucinations and improves response relevance to the user's question and the business domain.
It does not "ensure that all responses are factually accurate" because grounding reduces errors but cannot eliminate them completely-retrieval can return incomplete or irrelevant passages, user prompts can be ambiguous, and the model can still misinterpret context. It also does not inherently "increase performance of an AI model" in the sense of speed/throughput or model capability; grounding is an architecture and data strategy that improves output quality, not compute efficiency. Finally, grounding is not about "increasing storage required to host an AI model." While you may store documents in an index or repository, the core benefit is improved response quality through better context, not larger model hosting requirements.
질문 # 14
Your company plans to use generative AI to help summarize and analyze internal business documents.
You need to recommend a solution to prevent generative AI from accessing confidential or classified information.
What should you include in the recommendation?
정답：B
설명：
Data governance is essential to prevent generative AI (GenAI) from accessing confidential or classified information, as it provides the necessary framework, policies, and controls to manage data security throughout the AI lifecycle. Effective governance involves a combination of data classification, strict access controls, and active monitoring to ensure only authorized, non- sensitive data is used.
Reference:
https://dacodes.com/blog/data-governance-best-practices-for-generative-ai
질문 # 15
Your company stores thousands of reports and documents across multiple systems. You recommend using Azure AI Search as part of a new generative AI solution to improve information discovery. What is a key benefit of using Azure AI Search in this scenario?
정답：A
설명：
Azure AI Search provides an indexing and retrieval layer that makes large, distributed document collections searchable in a consistent way. The key benefit in an information discovery scenario is that it can index content from many sources and then retrieve relevant documents/passages using rich query capabilities, including natural language-style queries and semantic ranking. That directly aligns with B .
This retrieval capability is foundational for RAG architectures: the system uses Azure AI Search to find the best matching content, then supplies those results to a generative model so the answer is grounded in organizational knowledge. That improves relevance and reduces hallucinations because the model is guided by retrieved evidence.
Option A is the opposite of what you want-Search is used precisely to reference existing data. C is more aligned to workflow automation platforms (Logic Apps/Power Automate) and document processing services.
D describes fine-tuning, which is a different approach; Azure AI Search improves discovery and grounding through retrieval, not by changing model weights.
질문 # 16
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