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NEW QUESTION # 45
- For each of the following statements, select Yes if the statement is true. Otherwise, select No.
Answer:
Explanation:
Explanation:
The answer Area
* A generative AI model guarantees factually accurate responses if the model is trained on a large dataset.
The answer: No
* Content filtering and responsible AI safeguards help a generative AI model generate safe and inoffensive content. Answer: Yes
* A generative AI model always produces fair and unbiased results when the training data has been properly prepared and reviewed for fairness. Answer: No
* No - A larger training dataset can improve coverage and fluency, but it does not guarantee factual accuracy. Generative models can still hallucinate, mix concepts, or produce plausible-but-incorrect statements because they generate likely text rather than verifying truth. This is why solution designs commonly add grounding/retrieval, validation, and human review for high-stakes outputs.
* Yes - Content filtering and Responsible AI controls are specifically used to reduce harmful, unsafe, or policy-violating outputs . In practice, safeguards include input/output filters, safety classifiers, and governance controls that help enforce safety policies and minimize offensive content. These controls don't make outputs "perfect," but they materially reduce risk and are a standard part of production AI deployments.
* No - Even with careful data preparation and fairness reviews, models can still produce biased outcomes due to residual bias in data, label/measurement issues, deployment context, and shifting real- world distributions. "Always fair and unbiased" is an absolute claim that is not achievable in real systems; fairness is managed through continuous evaluation, monitoring, and mitigations-not assumed as guaranteed.
NEW QUESTION # 46
Your company purchases Microsoft 365 Copilot for its sales department. The sales department needs to find and summarize information across internal documents quickly. From which two data sources can the sales department obtain results by default? (Select TWO.)
Answer: C,E
Explanation:
By default, Microsoft 365 Copilot is grounded in your organization's Microsoft 365 data through Microsoft Graph , and it respects the user's existing permissions. For "find and summarize information across internal documents," the most direct default document repositories in Microsoft 365 are SharePoint (team sites and shared libraries) and OneDrive (a user's work files). That is why C (Microsoft SharePoint) and D (Microsoft OneDrive) are the correct selections. Microsoft explicitly describes Copilot as accessing organizational content via Microsoft Graph, including user documents and related work content.
The other options are not "by default" sources. A (on-premises file share) is not automatically part of Microsoft Graph unless you integrate/migrate content or use connectors to make it discoverable in Microsoft
365 experiences. B (custom CRM) similarly requires an integration approach (for example, Microsoft 365 Copilot connectors / Graph connectors) to index and expose that data for Copilot to use. E (Microsoft Sway) is not a primary default content source for Copilot's document grounding and is not typically referenced as a core internal document repository compared to SharePoint/OneDrive.
So, for immediate, out-of-the-box internal document discovery and summarization within Microsoft 365, SharePoint + OneDrive are the default answer.
NEW QUESTION # 47
Your company uses a non-reasoning generative AI model to create textual content.
You discover that the model's responses are inconsistent and do NOT meet expectations.
You need to improve the prompts.
What should you do? More than one answer choice may achieve the goal. Select the BEST answer.
Answer: B
Explanation:
When a non-reasoning model produces inconsistent results, you can ground its output by transforming a vague request into a highly structured framework. Since these models rely on pattern prediction rather than true logical deduction, providing "missing" data directly in the prompt acts as a roadmap for the desired completion.
To move from inconsistent to reliable content, focus on these specific additions:
*-> Contextual Guardrails: Provide situational details, such as the intended audience (e.g., "tech- savvy software developers" vs. "elementary students") and domain-specific constraints. This narrows the model's focus to relevant training data patterns.
*-> External Sources & Grounding: Include specific facts, background documents, or source material within the prompt to prevent the model from guessing or "hallucinating" facts.
*-> Explicit Expectations: Clearly define the format (e.g., JSON, Markdown, bulleted list) and tone (e.g., professional, witty). Stating what "success" looks like-such as word count limits or mandatory sections-reduces ambiguity.
Few-Shot Prompting: Add 1-3 examples of the exact style and structure you want the model to mimic. This is often the most effective way to align a non-reasoning model's output with your expectations.
Persona Assignment: Instruct the model to "act as" a specific professional (e.g., "Senior Copy Editor" or "Skeptical Venture Capitalist") to influence the vocabulary and perspective of the generated text.
Reference:
https://cloud.google.com/discover/what-is-prompt-engineering
NEW QUESTION # 48
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.
Answer: D
Explanation:
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
NEW QUESTION # 49
Your company uses generative AI to assist with content creation and customer interactions.
You need to evaluate whether Azure Machine Learning can add value to the current customer management.
For which use case should you use Machine Learning?
Answer: A
Explanation:
The correct answer is D. predicting customer retention. Azure Machine Learning is best suited for predictive analytics scenarios where historical data is used to train a model that predicts future outcomes. Customer retention prediction typically uses customer behavior, purchase history, engagement data, service interactions, churn signals, and account attributes to estimate whether a customer is likely to stay or leave. That is a classic machine learning use case. Generating marketing campaigns and summarizing customer service transcripts are generative AI or natural language processing tasks. Creating product descriptions from images is a multimodal or generative AI scenario. The wording "predicting" is the key indicator: when the business requirement is to forecast outcomes from patterns in data, Azure Machine Learning is the correct fit.
NEW QUESTION # 50
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