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| Section | Weight | Objectives |
|---|---|---|
| Identify an implementation and adoption strategy for Microsoft's AI apps and services | 20-25% | - Align an AI strategy with Microsoft responsible AI policies
|
| Identify the business value of generative AI solutions | 35-40% | - Identify the foundational concepts of generative AI
|
| Identify benefits, capabilities, and opportunities for Microsoft's AI apps and services | 35-40% | - Identify benefits and capabilities of Azure AI services
|
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NEW QUESTION # 71
Select the answer that correctly completes the sentence.
Answer:
Explanation:
Explanation:
In Microsoft Copilot Studio, knowledge sources allow an agent to generate responses from configured content such as websites, files, SharePoint content, Dataverse, or other approved knowledge repositories. This reduces the need to manually create a separate scripted topic for every possible user question. Instead, the agent can use generative answers grounded in the configured knowledge source and respond dynamically based on the user's request. Changing only the response format is too narrow and does not describe knowledge sources.
Summarizing Teams meetings is more aligned with Microsoft 365 Copilot scenarios. Creating one scripted topic for every question is exactly what knowledge sources help avoid.
NEW QUESTION # 72
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:
The answer Area
* A text-to-image generator can be used to translate content into other languages. Answer: No
* A predictive analytics model can generate new marketing content for a company's online ads. Answer:
No
* A generative AI chatbot can engage customers in personalized conversations and recommend products.
The answer : Yes
* No - A text-to-image generator's primary function is to create images from text prompts , not translate text between languages. Translation is a natural language processing task typically handled by language models or dedicated translation services. A text-to-image model could illustrate translated content (e.g., generate an image based on a translated prompt), but it is not the tool used to perform the translation itself.
* No - Predictive analytics models are designed to predict outcomes (forecasts, probabilities, classifications) from historical patterns, such as predicting click-through rate, churn, or next-quarter demand. They are not designed to create new ad copy or marketing creatives. Generating new marketing content is a generative AI capability (text generation), not predictive analytics.
* Yes - A generative AI chatbot is well-suited to interactive, natural-language conversations . With access to product catalogs and business rules, it can ask clarifying questions, tailor responses to customer needs, and recommend products (for example, suggesting tents based on group size, season, and budget). This combines conversational generation with retrieval/recommendation logic behind the scenes, enabling personalized customer engagement at scale.
NEW QUESTION # 73
Your company receives thousands of scanned invoices each month.
You need to recommend an AI solution that can automatically extract key details, such as invoice numbers, vendor names, and total amounts.
What is the best solution to recommend? More than one answer choice may achieve the goal.
Select the BEST answer.
Answer: C
Explanation:
Azure Document Intelligence in Foundry Tools is the preferred solution for automatically extracting structured data-such as vendor names, invoice IDs, and total amounts-from invoices, PDFs, and images. It utilizes advanced AI and OCR to convert unstructured documents into structured JSON data.
Reference:
https://learn.microsoft.com/en-us/azure/ai-services/document-intelligence/prebuilt/invoice
NEW QUESTION # 74
Your company sells hiking and camping gear online.
You need a generative AI solution that can interact with customers and ask questions about their needs.
What should you include in the solution?
Answer: A
Explanation:
In the evolving landscape of online retail, a generative AI solution typically takes the form of an AI Shopping Assistant or Conversational Agent. Unlike traditional, rule-based chatbots that follow rigid "yes/no" decision trees, these advanced solutions use Natural Language Processing (NLP) and Large Language Models (LLMs) to hold human-like, two-way dialogues.
Core Capabilities for Customer Interaction
To effectively assess customer needs, a generative AI solution should include:
Proactive Discovery Questions: Instead of waiting for a search query, the assistant can initiate the conversation with open-ended questions like, "What are you looking for today?" or "Is this gift for you or someone else?" to narrow down options.
Contextual Probing: If a customer's response is vague (e.g., "comfortable shoes"), the AI can ask clarifying follow-up questions to understand specific requirements for fit, material, or use case.
Personalized Recommendations: By analyzing real-time behavior, past purchases, and current session data, the AI generates tailored suggestions that act as a digital sales associate.
24/7 Multi-Channel Support: These agents provide instant assistance across websites, mobile apps, and social platforms like WhatsApp or Facebook Messenger, regardless of business hours.
Reference:
https://www.cognigy.com/blog/ai-chatbots-for-e-commerce
NEW QUESTION # 75
Your company is building a portfolio of AI-powered business solutions. Company executives want to understand how Microsoft responsible AI principles can support the company ' s long-term goals. Which benefit best demonstrates the importance of responsible AI? Select the BEST answer.
Answer: C
Explanation:
Responsible AI is fundamentally about earning and maintaining trust while scaling AI across the enterprise. Option C is the best answer because responsible AI practices (fairness, reliability and safety, privacy and security, transparency, accountability, and inclusiveness) reduce reputational, legal, and operational risk and make adoption sustainable over time. When stakeholders trust that AI is governed, tested, and monitored, the organization can expand AI usage confidently across business units.
The other options are incorrect because they make absolute or counterproductive claims. A is false:
responsible AI does not "guarantee" accuracy; it reduces risk and improves assurance, but no model can be guaranteed correct in all contexts. B is the opposite of reality: responsible AI increases the importance of data protection and governance; it does not reduce the need for them. D is also incorrect: responsible AI requires clear ownership and oversight, especially from leadership, because accountability is a core principle. In short, responsible AI matters because it builds stakeholder confidence and provides guardrails that support long- term, scalable, and compliant AI adoption-exactly what executives care about when investing in an AI portfolio.
NEW QUESTION # 76
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