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| Certification Vendor: | Microsoft |
|---|---|
| Exam Name: | AI Transformation Leader |
| Exam Number: | AB-731 |
| Exam Format: | Multiple-choice / scenario-based questions, Interactive components (may be included), Proctored online exam |
| Available Languages: | English |
| Related Certifications: | Microsoft AI Business Professional (AB-730) |
| Exam Price: | $99 USD |
| Exam Duration: | 45 minutes |
| Passing Score: | 700 |
| Recommended Training: | AB-731T00: Drive AI transformation in your organization |
| Exam Registration: | Exam scheduling (Pearson VUE via Microsoft portal) Official Microsoft certification page |
| Sample Questions: | Microsoft AB-731 Sample Questions |
| Exam Way: | Online proctored exam (may include interactive components) |
| Pre Condition: | No formal prerequisites required; designed for business leaders and decision-makers. |
| Official Syllabus URL: | https://learn.microsoft.com/en-us/credentials/certifications/ai-transformation-leader/ |
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NEW QUESTION # 75
- 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:
Answer Area
* Prompt engineering changes how a generative AI model was trained. Answer: No
* Prompt engineering focuses on designing clear, concise, and context-rich instructions. Answer: Yes
* Effective prompt engineering involves maximizing the number of tokens used in each request. Answer:
No
* No - Prompt engineering does not modify model weights or retrain the model. It is an inference-time technique : you steer outputs by improving the instructions and context you send to the model.
Changing how a model was trained would involve pretraining, fine-tuning, or other training methods- separate from prompt engineering.
* Yes - The primary goal of prompt engineering is to reduce ambiguity and variability by providing clear instructions , the right context , and explicit output constraints . This often includes specifying role and task, providing necessary facts or grounding text, defining format (bullets, JSON, headings), and adding examples (few-shot) when helpful. Well-constructed prompts improve consistency, relevance, and usefulness of outputs.
* No - Good prompt engineering does not mean "use as many tokens as possible." In fact, using unnecessary tokens can increase cost and may degrade quality by adding noise. Effective prompts are typically as short as possible but as long as necessary : enough context to achieve accuracy and alignment, but not bloated. Token discipline matters because most pricing is token-based and long contexts can dilute attention over what's most important.
NEW QUESTION # 76
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
To use Microsoft 365 Copilot chat, you must have a Microsoft Copilot license.
You do not necessarily need a separate "Microsoft 365 Copilot" add-on license to use the Copilot Chat feature.
Microsoft now includes a baseline version of Copilot Chat at no additional cost for users with eligible Microsoft 365 and Office 365 subscriptions.
Here is how the licensing impacts your experience:
*-> Without a Copilot Add-on License: You can still use Copilot Chat if you have a qualifying base subscription (such as Business Standard, E3, or E5). This version is web-grounded, meaning it can answer questions using public internet data and context from your currently open file, but it cannot search through your entire organization's emails, meetings, or files.
With a Microsoft 365 Copilot License: Adding this paid license transforms Copilot into a work- grounded assistant. It gains the ability to "reason" across your entire Microsoft Graph-searching your private inbox, calendar, and SharePoint documents to provide context-aware answers.
Box 2: Yes
Yes - Microsoft 365 Copilot chat provides context-aware assistance in Microsoft 365 apps.
Microsoft 365 Copilot chat is an AI-powered, context-aware assistant embedded directly into Microsoft 365 apps (Word, Excel, PowerPoint, Outlook, OneNote) to streamline workflow, summarize content, and create documents. It operates within a secure environment, using organizational data-including emails, chats, and files-to provide tailored assistance, with or without a separate paid add-on license.
Key Features and Capabilities:
*-> Context-Aware Assistance: Interacts with the user's active files (e.g., summarizing an open Word document) and Microsoft Graph data to provide relevant, in-the-moment help.
Integrated Apps: Available in a side pane in Word, Excel, PowerPoint, Outlook, and OneNote.
Content Generation & Summarization: Helps draft content, revise tones, summarize long email threads, and analyze data.
Secure Data Usage: Built-in with enterprise-grade data protection, ensuring that user data remains secure and is not used to train public models.
Functionality: Capabilities include uploading images, expanding input boxes, and providing quick access to agents and page-creation tools.
Box 3: No
No - Microsoft 365 Copilot chat can only access information in open files and read emails.
While Copilot does work with active, open content, it is designed to ground its responses in a much broader range of organizational data. It uses the Microsoft Graph to access, search, and summarize data across your entire Microsoft 365 tenant, provided you have the necessary permissions.
Reference:
https://www.microsoft.com/en-us/microsoft-365-copilot/pricing/enterprise
https://support.microsoft.com/en-gb/topic/how-copilot-chat-works-with-and-without-a-microsoft-365-copilot-license-5810b659-fbe0-48ee-9fe6-d731fe86cdeb
https://learn.microsoft.com/en-us/copilot/microsoft-365/microsoft-365-copilot-privacy
NEW QUESTION # 77
Your company uses generative AI to assist with content creation and customer interactions.
You need to evaluate whether Azure Machine Learning can add value to the current customer management.
For which use case should you use Machine Learning?
Answer: C
Explanation:
The correct answer is D. predicting customer retention. Azure Machine Learning is best suited for predictive analytics scenarios where historical data is used to train a model that predicts future outcomes. Customer retention prediction typically uses customer behavior, purchase history, engagement data, service interactions, churn signals, and account attributes to estimate whether a customer is likely to stay or leave. That is a classic machine learning use case. Generating marketing campaigns and summarizing customer service transcripts are generative AI or natural language processing tasks. Creating product descriptions from images is a multimodal or generative AI scenario. The wording "predicting" is the key indicator: when the business requirement is to forecast outcomes from patterns in data, Azure Machine Learning is the correct fit.
NEW QUESTION # 78
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:
* For a user to access organizational data from a mobile device, the user needs a Microsoft 365 Copilot license. No
* To reason over your organizational data by using Microsoft Graph, you need a Microsoft 365 Copilot license. Yes
* To use the Analyst agent, you need a Microsoft 365 Copilot license. Yes Top of Form Bottom of Form The key distinction here is between Copilot Chat capabilities available with a standard Microsoft 365 subscription and the full Microsoft 365 Copilot add-on that enables richer, in-context experiences grounded in organizational data.
Mobile access to organizational data does not inherently require a Microsoft 365 Copilot license. Microsoft's Microsoft 365 Copilot app (and related mobile experiences) can provide Copilot Chat for work/school accounts with a Microsoft 365 license, so simply accessing organizational content on a mobile device is not the same as having the paid Copilot add-on. The statement claims a Copilot license is required just to access org data from mobile, which is too broad-there are mobile Microsoft 365 apps that access org data without the Copilot add-on.
Reasoning over organizational data via Microsoft Graph is a core value proposition of Microsoft 365 Copilot.
Microsoft documents explain that Microsoft 365 Copilot connects LLMs to your organization's content and context through Microsoft Graph and generates responses "anchored" in organizational data. That deeper integration is tied to the Microsoft 365 Copilot experience (an add-on license).
Analyst is a "reasoning agent" within Microsoft 365 Copilot and Microsoft states that users with a Microsoft
365 Copilot license can use Analyst (with defined usage limits). Therefore, the Analyst agent requires the Microsoft 365 Copilot license.
NEW QUESTION # 79
Your company manages a website that publishes daily news articles. You need to recommend an AI solution that can analyze text and identify the main people, locations, and companies mentioned in the articles. What should you include in the recommendation?
Answer: D
Explanation:
The requirement is to analyze text and identify "people, locations, and companies" mentioned in news articles. This is a classic Named Entity Recognition (NER) / entity extraction scenario, which falls under natural language processing. Azure Language in Foundry Tools is the correct choice because it provides text analytics capabilities that detect and categorize entities in unstructured text-commonly including Person, Location, and Organization. This enables downstream experiences such as topic tagging, search filters (e.g.,
"all articles mentioning Company X"), trend dashboards (top people/places mentioned this week), and improved content discovery.
The other options do not match the requirement. Content Safety focuses on moderating harmful or policy- violating content (for example, hate, violence, self-harm, sexual content) and is not the primary tool for extracting named entities. Azure Vision is for analyzing images and performing OCR; it would only be relevant if the articles were images or scans, but the task here is entity extraction from text articles. Azure Speech is for speech-to-text, text-to-speech, and audio analysis; it would be used if your input were audio recordings rather than written articles.
Therefore, to identify key entities (people, locations, companies) from daily news article text, the best recommendation is Azure Language in Foundry Tools.
NEW QUESTION # 80
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