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>> Certified AB-731 Questions <<
These Microsoft AB-731 dumps are real, updated, and error-free. It provides you with the essential Microsoft AB-731 exam knowledge that you need to prepare and pass the Microsoft AB-731 certification test with high scores. You can easily use all these three Microsoft AB-731 Exam Questions format. These formats are compatible with all devices, operating systems, and the latest browsers.
NEW QUESTION # 81
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 - Larger datasets can increase the cost of generative AI solution that uses an Azure Machine Learning workspace.
larger datasets can increase the cost of a generative AI solution using an Azure Machine Learning (AML) workspace. While the AML workspace itself has no additional charge, larger datasets increase expenses across several underlying Azure services, particularly through higher storage, compute, and data transfer requirements.
Box 2: Yes
Yes - The cost of consuming Azure OpenAI models is primarily identified by the number of input and output tokens processed.
The cost of consuming Azure OpenAI models is primarily determined by the number of input and output tokens processed.
This consumption-based, pay-as-you-go model calculates costs based on the total volume of text (or image/audio data) sent to the model (input) and the text generated by the model (output), usually billed per 1,000 or 1 million tokens, depending on the model and pricing page updates.
Box 3: No
No - The cost of custom generative AI solutions always remains the same regardless of the model version or capability used he cost of custom generative AI solutions varies significantly based on the model's version, complexity, and capability. While a basic chatbot might range from $40,000 to $150,000, advanced enterprise-grade platforms with high-risk reasoning can exceed $400,000 to
$500,000+.
Reference:
https://www.doit.com/blog/optimizing-ml-costs-with-azure-machine-learning
https://www.finout.io/blog/azure-openai-pricing
https://medium.com/@dejanmarkovic_53716/custom-ai-solutions-cost-guide-2025-pricing- insights-revealed-cf19442261ec
NEW QUESTION # 82
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 - For a user to access organizational data from a mobile device, the user needs a Microsoft
365 Copilot license.
To access, summarize, and query organizational data (such as emails, chats, documents in SharePoint/OneDrive, and calendar items) via Microsoft 365 Copilot on a mobile device, a user must have a Microsoft 365 Copilot license assigned to them.
This license is an add-on to a qualifying base subscription (such as Microsoft 365 E3, E5, Business Standard, or Business Premium).
Box 2: Yes
Yes - To reason over your organizational data by using Microsoft Graph, you need a Microsoft
365 Copilot license.
To use the advanced AI reasoning capabilities of Microsoft 365 Copilot-specifically to analyze, summarize, and query your organizational data (emails, chats, documents, meetings) via Microsoft Graph-you need a Microsoft 365 Copilot license.
Box 3: Yes
Yes - To use the Analyst agent, you need a Microsoft 365 Copilot license To use the Analyst agent, you generally need a Microsoft 365 Copilot add-on license.
While a basic version of Copilot Chat is available for many Microsoft 365 and Office 365 subscribers at no extra cost, advanced "Frontier" agents like Analyst and Researcher are specifically built for deep reasoning and multi-step tasks, which are reserved for licensed Copilot users.
Reference:
https://learn.microsoft.com/en-us/copilot/microsoft-365/microsoft-365-copilot-minimum- requirements
https://learn.microsoft.com/en-us/copilot/faq
https://it.osu.edu/news/2025/07/22/new-microsoft-365-copilot-agents-available-research-and- analysis
NEW QUESTION # 83
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:
* A text-to-image generator can be used to translate content into other languages. Answer: No
* A predictive analytics model can generate new marketing content for a company's online ads. Answer:
No
* A generative AI chatbot can engage customers in personalized conversations and recommend products.
The answer: Yes
* No - A text-to-image generator's primary function is to create images from text prompts , not translate text between languages. Translation is a natural language processing task typically handled by language models or dedicated translation services. A text-to-image model could illustrate translated content (e.g., generate an image based on a translated prompt), but it is not the tool used to perform the translation itself.
* No - Predictive analytics models are designed to predict outcomes (forecasts, probabilities, classifications) from historical patterns, such as predicting click-through rate, churn, or next-quarter demand. They are not designed to create new ad copy or marketing creatives. Generating new marketing content is a generative AI capability (text generation), not predictive analytics.
* Yes - A generative AI chatbot is well-suited to interactive, natural-language conversations . With access to product catalogs and business rules, it can ask clarifying questions, tailor responses to customer needs, and recommend products (for example, suggesting tents based on group size, season, and budget). This combines conversational generation with retrieval/recommendation logic behind the scenes, enabling personalized customer engagement at scale.
NEW QUESTION # 84
- What should you use for each task? To answer, select the appropriate options in the answer area. NOTE:
Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Answer Area
* Extracting structured data from forms and invoices: Answer: Azure Document Intelligence in Foundry Tools
* Summarizing written content from business reports: Answer: Azure Language in Foundry Tools
* Generating descriptive text for uploaded images: Answer: Azure Vision in Foundry Tools These three tasks align to three different Azure AI capability families: document processing, language understanding/generation, and computer vision.
* Forms and invoices are semi-structured documents where the business need is to extract specific fields (IDs, names, totals, dates) reliably into structured output. Azure Document Intelligence is designed for intelligent document processing and includes prebuilt models (such as invoices) as well as custom extraction options, making it the correct choice for structured data extraction from documents.
* Summarizing written business reports is an NLP task focused on compressing long text into key points, themes, and action items. Azure Language provides language processing capabilities (including summarization features within language service capabilities), so it is the best fit for summarization scenarios.
* Generating descriptive text for images (image captioning/description) is a computer vision task.
Azure Vision can analyze uploaded images and return descriptions/captions and other visual insights, which directly matches the requirement to produce descriptive text from images.
NEW QUESTION # 85
DRAG DROP - Match the Microsoft responsible AI principles to the appropriate descriptions. NOTE: Each correct match is worth one point.
Answer:
Explanation:
Explanation:
Answer Area
* Protect personal information and apply strong safeguards to keep data secure. Answer: Privacy and security
* Make AI solutions understandable by explaining how and why decisions are made. Answer:
Transparency
* Design AI solutions that are accessible to people of all abilities and experiences. Answer: Inclusiveness
* Involve human oversight in the control of AI solutions. Answer: Accountability Microsoft's Responsible AI principles provide a practical framework for building and deploying trustworthy AI systems.
Privacy and security is the principle focused on protecting personal information and ensuring strong safeguards-such as least privilege access, encryption, DLP controls, secure storage, and auditing-so data remains protected throughout collection, processing, and usage.
Transparency is about making AI systems understandable. This includes communicating when AI is being used, explaining system capabilities and limitations, and providing clarity on how outputs are produced (for example, what data sources are used, what assumptions apply, and why certain recommendations are generated). Transparency reduces confusion and helps users calibrate trust appropriately.
Inclusiveness ensures AI solutions are designed to benefit people of diverse abilities, backgrounds, and experiences. Practically, this includes accessibility (assistive technologies, readable outputs), inclusive UX design, and testing across diverse user groups so the solution does not exclude or disadvantage certain populations.
Accountability emphasizes that humans and organizations remain responsible for AI outcomes. Involving human oversight is a direct expression of accountability: setting governance, defining who owns decisions, establishing escalation paths, and ensuring there are controls for review, intervention, and correction when AI outputs are unsafe or incorrect.
NEW QUESTION # 86
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