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>> AB-731 Original Questions <<
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NEW QUESTION # 27
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 # 28
Your company's HR department spends a large amount time answering repetitive questions and updating policy documents.
You need a solution that can automate employee interactions, respond to HR-related questions, and connect to internal business data sources.
What solution should you recommend?
Answer: A
Explanation:
Microsoft Copilot Studio is the most adequate solution for automating employee interactions, responding to HR-related questions, and connecting to internal business data sources.
Copilot Studio (formerly Power Virtual Agents) allows organizations to create custom AI agents or
"copilots" that can be embedded in Microsoft Teams, SharePoint, and other apps to provide self- service support.
Key Capabilities for HR & Internal Data
*-> Connecting to Internal Data Sources: Copilot Studio can connect to SharePoint, HRIS systems, SQL Server, and other databases to retrieve policy documents, payroll information, and employee data.
*-> Automating Employee Interactions: It creates conversational AI chatbots (often called
"AskHR" virtual agents) that handle routine queries, such as leave requests, benefits questions, and policy inquiries.
Intelligent Action and Workflow: Beyond just answering questions, Copilot Studio uses Generative AI to take actions, such as submitting time-off requests or updating employee records through Power Automate integrations.
Customization: It enables the creation of tailored agents (e.g., Onboarding Assistant, Leave Assistant, Policy Assistant) specifically for HR needs.
Reference:
https://www.synapx.com/boosting-internal-productivity-with-copilot-studio-support-bots
NEW QUESTION # 29
You have a large language model (LLM) that was pretrained by using public data.
You want the LLM to generate responses that incorporate frequently updated proprietary content, such as internal documents and knowledge bases.
You need to recommend a solution to ensure that the LLM uses the most current information without retraining the model.
What should you include in the recommendation?
Answer: A
Explanation:
Retrieval-Augmented Generation (RAG) is the optimal, industry-standard approach for incorporating frequently updated proprietary data into an LLM's output. It works by connecting a pre-trained LLM to an external, dynamic knowledge base--such as company documents--using vector databases to retrieve relevant context in real-time, reducing hallucinations and eliminating the need for constant, costly retraining.
Reference:
https://shiftasia.com/community/retrieval-augmented-generation-rag-a-comprehensive-guide-to-smarter-more-accurate-ai
NEW QUESTION # 30
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? Select the BEST answer.
Answer: C
Explanation:
Responsible AI is fundamentally about earning and maintaining trust while scaling AI across the enterprise. Option C is the best answer because responsible AI practices (fairness, reliability and safety, privacy and security, transparency, accountability, and inclusiveness) reduce reputational, legal, and operational risk and make adoption sustainable over time. When stakeholders trust that AI is governed, tested, and monitored, the organization can expand AI usage confidently across business units.
The other options are incorrect because they make absolute or counterproductive claims. A is false:
responsible AI does not "guarantee" accuracy; it reduces risk and improves assurance, but no model can be guaranteed correct in all contexts. B is the opposite of reality: responsible AI increases the importance of data protection and governance; it does not reduce the need for them. D is also incorrect: responsible AI requires clear ownership and oversight, especially from leadership, because accountability is a core principle. In short, responsible AI matters because it builds stakeholder confidence and provides guardrails that support long- term, scalable, and compliant AI adoption-exactly what executives care about when investing in an AI portfolio.
NEW QUESTION # 31
A retail company wants to use AI to forecast seasonal demand for its product lines using five years of historical sales data. A separate team wants to use AI to generate personalised marketing emails based on customer purchase history.
Which types of AI are most appropriate for each use case?
Answer: D
Explanation:
Each use case requires a fundamentally different type of AI. Demand forecasting from historical sales data is a classic machine learning use case involving pattern recognition and time-series prediction on structured numerical data. Machine learning models excel at identifying trends, seasonality, and correlations in historical datasets to produce accurate forecasts. Personalised email generation requires creating new, original content tailored to individual customers, which is the defining capability of generative AI.
NEW QUESTION # 32
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