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NEW QUESTION # 21
Your company manages a website that publishes daily news articles. You need to recommend an AI solution that can analyze text and identify the main people, locations, and companies mentioned in the articles. What should you include in the recommendation?
Answer: D
Explanation:
The requirement is to analyze text and identify "people, locations, and companies" mentioned in news articles. This is a classic Named Entity Recognition (NER) / entity extraction scenario, which falls under natural language processing. Azure Language in Foundry Tools is the correct choice because it provides text analytics capabilities that detect and categorize entities in unstructured text-commonly including Person
, Location , and Organization . This enables downstream experiences such as topic tagging, search filters (e.
g., "all articles mentioning Company X"), trend dashboards (top people/places mentioned this week), and improved content discovery.
The other options do not match the requirement. Content Safety focuses on moderating harmful or policy- violating content (for example, hate, violence, self-harm, sexual content) and is not the primary tool for extracting named entities. Azure Vision is for analyzing images and performing OCR; it would only be relevant if the articles were images or scans, but the task here is entity extraction from text articles. Azure Speech is for speech-to-text, text-to-speech, and audio analysis; it would be used if your input were audio recordings rather than written articles.
Therefore, to identify key entities (people, locations, companies) from daily news article text, the best recommendation is Azure Language in Foundry Tools .
NEW QUESTION # 22
Your company plans to use generative AI to help summarize and analyze internal business documents. You need to recommend a solution to prevent generative AI from accessing confidential or classified information.
What should you include in the recommendation?
Answer: B
Explanation:
Preventing generative AI from accessing confidential or classified information is primarily a data access and classification control problem. The most effective broad solution is data governance (B) -the framework of policies and controls that ensures sensitive content is identified, classified, protected, and access is restricted using least privilege. Data governance includes information classification/sensitivity labels, access control reviews, secure sharing practices, data loss prevention (DLP), and auditing-controls that directly limit what data is available to the AI through permission trimming and policy enforcement.
An information barrier (A) policy is more specific: it's intended to prevent communication and collaboration between defined user groups (often for regulatory/ethical walls). It does not comprehensively address document classification or restrict AI access to sensitive files across the tenant. A data retention policy (C) governs how long content is kept and when it's deleted; it's not a primary access-prevention mechanism. Communication monitoring (D) is a detection/oversight control; it can help identify risky activity, but it does not itself prevent access to confidential content by the AI.
Therefore, to prevent AI from surfacing confidential/classified data during summarization and analysis, you should recommend data governance as the primary control layer.
NEW QUESTION # 23
Your company uses Microsoft 365 Copilot.
You identify several business processes that require custom workflows and specialized automation.
You need to recommend a solution that extends Copilot capabilities while minimizing development effort and costs.
What should you recommend?
Answer: D
Explanation:
To extend Microsoft 365 Copilot while minimizing development effort and costs for business processes requiring specialized automation, you should build a declarative agent by using the
"lite" experience of Microsoft Copilot Studio (often referred to as the Agent Builder).
Declarative Agent (Lite Experience)
Best for: Minimizing effort and cost while staying within the Microsoft 365 ecosystem.
Effort: Low; utilizes a natural language interface where you describe tasks and workflows in plain language.
Capabilities: Adds custom instructions, knowledge (like SharePoint files), and specific actions (API plugins) to the existing Copilot orchestrator.
Cost: Often included with Microsoft 365 Copilot licenses, reducing additional procurement friction.
Incorrect:
[Not A]
Agent with Microsoft Foundry
Best for: Pro-code development, specialized model tuning, and high-scale, multi-agent orchestration.
Effort: High; requires professional developers, data scientists, and specialized skills in languages like Python or C#.
Cost: Higher due to specialized developer resources and Azure consumption-based pricing for models and infrastructure.
[Not C]
Custom Agent (Full Copilot Studio)
Best for: More complex, branched workflows and departmental-scale automation.
Effort: Moderate; involves a low-code, drag-and-drop interface and may require understanding
"Topics" and manual conversation logic.
Capabilities: Offers greater reach, bespoke integrations, and can be published as a standalone bot beyond Microsoft 365 apps.
Reference:
https://peafowlit.com/blog/microsoft-copilot-studio-vs-foundry-ai-agents
NEW QUESTION # 24
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:
NEW QUESTION # 25
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 # 26
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