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NEW QUESTION # 95
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: D
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 # 96
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: C
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Real-World Changes: External events (like a pandemic or economic shift) can suddenly make the model's training data outdated and irrelevant.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Reduced Relevance: Outputs feel outdated or fail to address contemporary topics.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 # 97
A mid-sized company wants to give all 500 employees access to AI assistance. The company uses Microsoft 365 E3 but has not purchased any additional AI licences. The CEO wants to understand the difference between the free Microsoft Copilot available to all employees and the paid Microsoft 365 Copilot add-on before approving the budget.
What is the key difference between these two products?
Answer: C
Explanation:
The fundamental distinction between the free Microsoft Copilot and the paid Microsoft 365 Copilot is data access. The free Microsoft Copilot is grounded in internet data only and does not access any organisational information. Microsoft 365 Copilot integrates with Microsoft Graph to access the company's emails, documents, calendar events, Teams conversations, and contacts, enabling it to provide contextually relevant assistance within the organisation's own data. This is the primary value proposition for the paid licence.
NEW QUESTION # 98
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:
Data governance is essential to prevent generative AI (GenAI) from accessing confidential or classified information, as it provides the necessary framework, policies, and controls to manage data security throughout the AI lifecycle. Effective governance involves a combination of data classification, strict access controls, and active monitoring to ensure only authorized, non- sensitive data is used.
Reference:
https://dacodes.com/blog/data-governance-best-practices-for-generative-ai
NEW QUESTION # 99
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 # 100
......
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