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, 1.Which business requirement most closely relates to grounding a generative AI model?
A. supporting multiple languages
B. measuring the number of user interactions per day
C. enabling users to interact by using natural language queries
D. ensuring that verified company data sources are used for response generation
Answer: D
Explanation:
Grounding in generative AI means ensuring model outputs are based on trusted, relevant information
sources rather than only on the model’s general training data. In a business context, grounding is about
aligning responses with verified enterprise knowledge (policies, product documentation, internal
procedures, approved FAQs, etc.) so the system is more accurate, consistent, and defensible. That is
exactly what option D describes: “ensuring that verified company data sources are used for response
generation.”
In Microsoft AI solution patterns, grounding is commonly achieved using retrieval-augmented generation
(RAG). With RAG, the system retrieves relevant passages from approved company repositories (for
example, indexed documents or knowledge bases) and supplies them as context to the model during
response generation. This reduces hallucinations, improves factual correctness, and makes answers
more relevant to the organization’s reality—critical when AI is used for customer support, employee
helpdesks, compliance guidance, or executive reporting.
The other options do not directly address grounding. A relates to localization/multilingual capability, B is a
usage/telemetry metric, and C is an interaction method (natural language interface). They can all be
important requirements, but none of them ensure outputs are anchored to verified company data—the
core purpose of grounding.
2.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:
For a user to access organizational data from a mobile device, the user needs a Microsoft 365 Copilot
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