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Microsoft AI Transformation Leader Sample Questions (Q29-Q34):

NEW QUESTION # 29
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.

Answer:

Explanation:

Explanation:
Box 1: No
No - Retrieval Augmented Generation (RAG) requires model fine-tuning.
Retrieval Augmented Generation (RAG) does not require model fine-tuning; it is designed to enhance Large Language Models (LLMs) with external data without modifying their internal parameters. RAG enables fast knowledge updates and reduces hallucinations by fetching relevant information. While fine-tuning adjusts weights for domain-specific behavior, RAG is for dynamic, up-to-date knowledge.
Box 2: Yes
Yes - Retrieval Augmented Generation (RAG) is helpful when you need a generative AI solution that can access current, verifiable information.
Think of Retrieval Augmented Generation (RAG) as giving an AI an "open-book exam" instead of forcing it to rely solely on its internal memory.
By connecting the model to external, authoritative data sources-like a company's private knowledge base or real-time news-it becomes significantly more reliable in several ways:
Reduces Hallucinations: Because the AI must ground its answers in the retrieved documents, it's less likely to "make things up".
Transparency: You can see the exact source used for the answer, making it easy to verify facts.
Cost-Efficiency: It is often much cheaper and faster to update a RAG database than it is to retrain or fine-tune a massive model on new information Box 3: Yes Yes - Retrieval Augmented Generation (RAG) enables you to get more relevant responses based on your organization's documents without retraining the base model.
Retrieval-Augmented Generation (RAG) is an AI framework that improves the accuracy and relevance of Large Language Model (LLM) outputs by incorporating, in real-time, external data that was not part of the model's original training, all without the need to retrain or fine-tune the base model. This method is particularly effective for allowing AI systems to access and utilize an organization's proprietary, private, or constantly updating data to generate more contextually accurate and authoritative responses.
Reference:
https://www.redhat.com/en/topics/ai/rag-vs-fine-tuning
https://pub.towardsai.net/how-rag-powers-smart-ai-applications-8d005696baa3


NEW QUESTION # 30
Your company uses a generative AI solution.
You need to improve the quality of responses by using grounding.
Which statement accurately describes how grounding improves accuracy and relevancy?

Answer: A

Explanation:
Grounding is a critical technique for improving the accuracy and relevance of generative AI solutions by linking or "anchoring" the large language model's (LLM) outputs to specific, verified, and up-to-date data sources. Without grounding, LLMs rely on their pre-trained, static, and often outdated knowledge, leading to "hallucinations"-confidently generated but incorrect, irrelevant, or fabricated information.
How Grounding Improves Accuracy and Relevance
Grounding transforms a general-purpose AI into a specialized, trustworthy, and actionable tool by providing the following benefits:
Reduces Hallucinations: By forcing the model to anchor its responses in provided data-such as internal documents, databases, or live web searches-grounding significantly reduces the likelihood of the model creating false information.
Enhances Contextual Relevance: Grounded models can access domain-specific, private data (e.g., CRM records, internal wikis, proprietary PDFs) rather than just public, general knowledge.
Ensures Data Freshness: Instead of relying on a static, old training cut-off date, grounding (often via Retrieval-Augmented Generation or RAG) enables the model to access the latest, real-time information, such as current inventory, updated policies, or recent news.
Provides Auditability and Trust: Grounded systems frequently provide citations or links to the exact source material used to generate the answer, allowing users to verify the information and increasing trust in the system.
Reference:
https://portkey.ai/blog/llm-grounding-for-accurate-outputs/


NEW QUESTION # 31
Select the answer that correctly completes the sentence.
When a generative AI model produces output that seems realistic but contains incorrect information, the behavior is known as __________.

Answer:

Explanation:

Explanation:
model inaccuracy
The scenario describes a model producing plausible-sounding content that is factually wrong -a common generative AI failure mode often referred to as a "hallucination." Since "hallucination" is not offered in the dropdown, the best matching choice is model inaccuracy because the core problem is that the model's output is incorrect even though it appears confident and coherent.
The other options do not fit the definition of the behavior: data leakage is about sensitive information being exposed (for example, proprietary prompts, secrets, or personal data). Prompt injection is an attack technique where a user tries to override system instructions or cause unsafe actions. Overreliance describes a human
/organizational risk -trusting the model too much-rather than the model's intrinsic behavior of generating incorrect facts. Overreliance can be a consequence of this behavior, but it is not what the behavior itself is called.
In practice, you mitigate this kind of inaccuracy by grounding responses in trusted sources (for example, RAG), constraining prompts with explicit requirements, using verification steps (citations, cross-checking, tool-based validation), and adding human review for high-impact use cases.


NEW QUESTION # 32
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 ensure descriptions can be posted immediately after creation, enable selection/inclusion of product details, and be fast and simple for non- technical staff.
What is the best type of solution to use? Select the BEST answer.

Answer: A


NEW QUESTION # 33
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 # 34
......

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