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NEW QUESTION # 22
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 - Azure Vision in Foundry Tools can extract and analyze key phrases from PDF files.
Azure Document Intelligence (formerly part of Azure AI Services, now integrated into Azure AI Foundry Tools) can extract text, tables, and structures from PDF files.
While Azure Vision specifically handles Optical Character Recognition (OCR) for scanning text in images and documents, the combined capabilities within the Foundry ecosystem-particularly using Document Intelligence-allow for the extraction of structured data and, when combined with Azure Language services, the identification of key phrases and semantic information.
Box 2: No
No - Azure Vision in Foundry Tools can generate images based on natural language descriptions.
Azure Vision in Foundry Tools is designed for analyzing existing visual content rather than generating new images from scratch.
While the "Foundry" platform does offer image generation, it is typically handled by a separate image generation tool (currently in preview) that uses models like DALL-E Box 3: Yes Yes - Azure Document Intelligence in Foundry Tools can be used to automate the processing of invoices and credit notes.
Azure Document Intelligence in Foundry Tools (formerly part of Azure AI Services) is designed to automate the processing of invoices and credit notes, transforming unstructured documents into structured, actionable data within workflows.
It uses machine learning and Optical Character Recognition (OCR) to extract key fields (such as vendor name, invoice date, amounts, and tax information) and line items.
Reference:
https://azure.microsoft.com/en-in/products/ai-foundry/tools/document-intelligence
https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/concept-describing-images
https://azure.microsoft.com/en-in/products/ai-foundry/tools/document-intelligence
NEW QUESTION # 23
Your company creates a custom Azure Machine Learning model that uses a generative AI assistant.
The model initially delivers strong results. However, six months later, the model predictions become noticeably less accurate.
What is a possible cause of the issue?
Answer: B
Explanation:
This phenomenon is known as data drift.
It is one of the top reasons model accuracy declines after deployment. In the context of a generative AI assistant, data drift occurs when the statistical properties or distributions of the input data (the prompts or context provided by users) change significantly from what the model was originally trained on.
Key Causes of Data Drift
Changing User Behavior: Users may start interacting with the assistant differently, using new slang, jargon, or evolving topics of interest.
Real-World Changes: External events (like a pandemic or economic shift) can suddenly make the model's training data outdated and irrelevant.
Data Pipeline Issues: Changes in how data is collected, such as updated sensors or modified web forms, can alter the format or scale of inputs.
Seasonality: Patterns may fluctuate based on the time of year, such as different holiday shopping behaviors.
Impact on Generative AI
For a generative assistant, data drift often manifests as:
Reduced Relevance: Outputs feel outdated or fail to address contemporary topics.
Increased Hallucinations: When faced with unfamiliar input patterns, the model may produce factually incorrect or nonsensical responses.
Loss of Quality: Outputs may become less creative, more repetitive, or exhibit biased behavior.
Reference:
https://nexla.com/ai-infrastructure/data-drift
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:
Explanation:
Box 1: No
A text-to-image generator (like DALL-E or Midjourney) takes textual descriptions and synthesizes them into visual imagery. It does not perform language translation workflows; that task requires natural language processing (NLP) or large language models (LLMs).
Box 2: No
Predictive analytics models rely on historical data and machine learning algorithms to forecast future trends, behaviors, or numerical outcomes (such as click-through rates or future sales).
They do not create or synthesize brand-new creative artifacts like marketing text or ad copies; that is the role of Generative AI.
Box 3: Yes
Generative AI chatbots (like those built using custom LLMs or Copilot Studio) excel at maintaining dynamic, human-like context. They can handle highly personalized conversations based on user profile inputs and dynamically synthesize relevant product recommendations.
NEW QUESTION # 25
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
Microsoft 365 Copilot respects the existing data access permissions configured in your tenant.
However, if an organization suffers from "over-sharing" (e.g., files or folders accidentally shared with "Everyone except external users" or large global groups), Copilot will surfaces this data effortlessly via user prompts. This radically exposes and amplifies pre-existing data governance flaws, dark data, and stale permissions that went unnoticed when users had to look for files manually.
Box 2: No
Implementing Copilot typically increases or maintains data management costs, rather than reducing them. To deploy Copilot safely, organizations often need to purchase additional security and governance licensing (such as Microsoft Purview Information Protection, Data Loss Prevention, or eDiscovery) and allocate significant IT resources/consulting hours to clean up over-shared data, classify sensitive materials, and establish robust lifecycle policies.
Box 3: Yes
While it introduces challenges, Copilot integrated alongside Microsoft Purview works dynamically to manage and surface risks. Features like automated content labeling, data classification insights, and auditing logs generated by Copilot interactions help IT teams track where sensitive data is being processed, how it's being synthesized, and identify compliance or security gaps across the tenant.
NEW QUESTION # 26
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? More than one answer choice may achieve the goal. Select the BEST answer.
Answer: B
Explanation:
Microsoft's Responsible AI principles serve as a strategic framework that aligns artificial intelligence development with a company's long-term business goals. By adhering to these principles, organizations can transform ethical guidelines into practical governance that supports sustainable growth.
Enhancing Stakeholder Trust
Responsible AI principles directly bolster stakeholder trust--a critical asset for long-term stability-- by ensuring transparency and accountability.
Transparency: Making AI operations and decisions understandable to users and stakeholders ensures people can see the "how" and "why" behind AI outputs.
Fairness and Inclusiveness: Treating all users equally and mitigating bias prevents discriminatory outcomes, protecting the organization's reputation.
Privacy and Security: Protecting user data through platform-level controls builds a foundation of safety that is essential for customer retention and regulatory compliance.
Reference:
https://www.preciofishbone.com/knowledge-hub/responsible-ai-in-the-ai-era-why-it-has-become-a-business-imperative
NEW QUESTION # 27
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