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NEW QUESTION # 10
Which business requirement most closely relates to grounding a generative AI model?
Answer: B
NEW QUESTION # 11
Hotspot Question
Select the answer that correctly completes the sentence.
Answer:
Explanation:
NEW QUESTION # 12
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 # 13
Your company is deploying Microsoft 365 Copilot. The deployment must provide users with access to the Researcher agent to search across data in Microsoft SharePoint.
You need to recommend a licensing plan for the solution.
What should you recommend?
Answer: C
Explanation:
To use the Researcher agent for searching across Microsoft SharePoint data, you need the Microsoft 365 Copilot add-on license.
The Researcher agent is a first-party reasoning agent included in the core experience for users with a paid Copilot license. It is designed for multi-step, in-depth research grounded in your Microsoft Graph data, including SharePoint, OneDrive, emails, and Teams.
Required Licensing Components
To deploy this capability, your organization must have:
1. A Qualifying Base License:
Enterprise: Microsoft 365 E3 or E5, or Office 365 E3 or E5.
Business: Microsoft 365 Business Standard or Business Premium.
*-> 2. Microsoft 365 Copilot Add-on:
This paid license (typically ~$30/user/month) unlocks the Researcher agent and the ability to search internal tenant data like SharePoint.
Reference:
https://learn.microsoft.com/en-us/copilot/microsoft-365/faq-researcher
NEW QUESTION # 14
You have a historical dataset that contains 1,000 records. You need an AI solution that can analyze the data to identify patterns and predict future outcomes. What should you include in the solution?
Answer: B
Explanation:
The requirement describes a predictive analytics / machine learning scenario: using historical data to learn patterns and then predict future outcomes . The Microsoft service that directly supports the end-to-end machine learning lifecycle-data preparation, model training, evaluation, deployment, and MLOps-is Azure Machine Learning , which is why C is the best choice. Azure Machine Learning is explicitly designed to help data scientists and engineers train and deploy models and manage the ML project lifecycle, making it the right fit for building a predictive model from your dataset.
The other options focus on different problem classes: Azure Document Intelligence is for extracting structured data from documents (OCR, key-value pairs, tables), not for general predictive modeling. Azure Content Understanding is for deriving structured insights from multimodal content (documents, images, audio, video) into a user-defined schema; it's not the primary service for training predictive models from a tabular historical dataset. Microsoft Foundry is a broader platform for building AI apps/agents and orchestrating models/tools, but the specific need here is classical ML training and prediction-handled most directly by Azure Machine Learning.
NEW QUESTION # 15
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