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| Section | Weight | Objectives |
|---|---|---|
| Identify benefits, capabilities, and opportunities for Microsoft's AI apps and services | 35-40% | - Identify benefits and capabilities of Microsoft 365 Copilot and Microsoft Copilot
|
| Identify the business value of generative AI solutions | 35-40% | - Identify benefits and capabilities of generative AI solutions
|
| Identify an implementation and adoption strategy for Microsoft's AI apps and services | 20-25% | - Align an AI strategy with Microsoft responsible AI policies
|
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NEW QUESTION # 85
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,D
NEW QUESTION # 86
You need to help analysts investigate incidents across endpoint, identity, cloud, and security operations data.
The analysts must summarize alerts, identify affected resources, and review recommended next steps by using natural language.
What should you use?
Answer: A
Explanation:
The correct answer is A. Microsoft Security Copilot. The requirement is clearly a security operations scenario: analysts must investigate incidents across endpoint, identity, cloud, and security operations data.
Microsoft Security Copilot is designed for security teams and supports natural-language investigation, alert summarization, incident context, affected-resource identification, and recommended response actions.
Microsoft 365 Copilot is focused on productivity tasks such as drafting, summarizing, and analyzing content in Microsoft 365 apps. Azure Document Intelligence extracts structured information from documents such as forms and invoices. Microsoft Copilot Studio is used to build and customize agents for business workflows.
Because the scenario centers on security incident investigation and response guidance, Microsoft Security Copilot is the correct solution.
NEW QUESTION # 87
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: Yes
Yes - Microsoft Foundry helps organizations securely build and manage generative AI solutions governed environment.
Microsoft Foundry is a unified, interoperable platform designed to help organizations build, optimize, and manage generative AI applications and autonomous agents within a secure, governed environment. It acts as a central "AI app and agent factory" that brings together models, data, and tools, allowing businesses to move from prototyping to production while maintaining safety and compliance.
Box 2: Yes
Yes - Microsoft Foundry provided built-in scalability to enable organizations to expand AI workloads as usage increases.
Microsoft Foundry acts as an enterprise-grade, unified platform for AI app and agent development, designed to enable organizations to build, deploy, and scale AI workloads efficiently. It provides built-in, automated scalability through several key mechanisms that allow organizations to expand their AI usage without manual infrastructure management.
Box 3: Yes
Yes - Microsoft Foundry can be used for image recognition and computer vision tasks.
Microsoft Foundry (part of Azure AI Services/Tools) offers Azure Vision, a comprehensive suite for image recognition and computer vision tasks. It provides prebuilt APIs and tools for analyzing images, detecting objects, OCR, and facial recognition, allowing developers to build intelligent, agentic applications without deep machine learning expertise.
Reference:
https://learn.microsoft.com/en-us/azure/ai-foundry/what-is-foundry
https://azure.microsoft.com/en-us/products/ai-foundry
NEW QUESTION # 88
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 an AI solution pattern used to improve response quality by ensuring the model's output is based on trusted, relevant information provided at inference time, rather than relying only on what the model
"remembers" from training. Therefore, C is correct: grounding anchors responses in specific data sources .
In practical deployments, grounding commonly uses retrieval (often called Retrieval Augmented Generation, or RAG) where the system first finds relevant content from approved sources-such as internal policy documents, product documentation, knowledge bases, or databases-and then includes that content in the prompt context sent to the model. Microsoft's guidance describes grounding data as information supplied at inference time to help responses become more accurate and relevant because the model is guided by authoritative, up-to-date content that may not have been part of original training.
The other options do not define grounding. A relates to inclusion practices and diversity considerations, which are important for responsible AI but are not what grounding means. B describes transparency/explainability concepts. D relates to model evaluation/communication of limitations. Grounding is specifically about tying outputs to known sources, which reduces hallucinations and improves business trust in the generated responses.
NEW QUESTION # 89
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:
Answer Area
* A manufacturer can use Azure Vision in Foundry Tools to identify product defects on an assembly line. Answer: Yes
* A logistics company can use Azure Vision in Foundry Tools to recognize package shipping labels. Answer: Yes
* The HR department at your company can only use Azure Vision in Foundry Tools to extract written content from Microsoft Word files. Answer: No Azure Vision in Foundry Tools provides computer vision capabilities to analyze images, including identifying visual features and reading text with OCR. Because it is designed to "analyze images" and support vision scenarios, it can be applied to manufacturing quality inspection use cases where the goal is to detect anomalies/defects from images captured on a production line. This aligns with statement 1 being Yes .
Statement 2 is also Yes because recognizing shipping labels is fundamentally text extraction from images (often plus some layout/field parsing). Azure Vision supports optical character recognition (OCR) to read printed text from images, and Microsoft documentation explicitly notes OCR can extract text from images such as product labels and similar real-world text surfaces-making shipping labels a direct fit.
Statement 3 is No because it is incorrectly restrictive. Azure Vision is not limited to extracting written content from Word documents, nor is OCR restricted to Word files. Vision capabilities apply broadly to images (and, depending on the capability, various document/image inputs) for tasks like image analysis and text recognition. HR could use it for many scenarios such as extracting text from scanned images, photos, or other visual inputs-not "only" Word files.
NEW QUESTION # 90
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