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| Certification Vendor: | Microsoft |
|---|---|
| Exam Name: | Exam AB-731: AI Transformation Leader |
| Exam Number: | AB-731 |
| Real Exam Qty: | 40–60 |
| Exam Duration: | 45–65 |
| Exam Format: | Drag and drop, Yes/No, Multiple choice, Multiple response, Case studies |
| Passing Score: | 700 |
| Certificate Validity Period: | 12 months |
| Exam Price: | $99 USD |
| Available Languages: | German, Spanish, Chinese (Simplified), French, Japanese, English |
| Recommended Training: | AB-731T00: AI Transformation Leader |
| Exam Registration: | Microsoft Certification Registration |
| Sample Questions: | Microsoft AB-731 Sample Questions |
| Exam Way: | Online proctored or onsite at authorized test centers |
| Pre Condition: | No mandatory prerequisites; recommended experience in business transformation, change management, and familiarity with Microsoft 365 and Azure AI services |
| Official Syllabus URL: | https://learn.microsoft.com/en-us/credentials/certifications/resources/study-guides/ab-731 |
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NEW QUESTION # 15
Hotspot Question
Select the answer that correctly completes the sentence.
Answer:
Explanation:
Explanation:
Box: Microsoft Graph
When you use Microsoft 365 Copilot connectors to connect to external content to __________, your users can find, summarize, and learn from line-of-business (LOB) data by using natural language prompts.
By using Microsoft Graph connectors, you can index your third-party data sources-such as file shares, ServiceNow, or SQL databases-directly into the Microsoft Graph.
Once indexed, this LOB data becomes part of the "knowledge base" that Microsoft 365 Copilot can access. This allows users to ask questions like, "What is the status of the tickets in ServiceNow?" or "Summarize the project specs from our internal wiki," and receive grounded, context-aware responses.
Reference:
https://learn.microsoft.com/en-us/graph/connecting-external-content-experiences
NEW QUESTION # 16
You are exploring how Microsoft 365 Copilot uses Microsoft Graph to deliver AI-powered experiences.
Which information in Microsoft Graph can Copilot use by default?
Answer: B
Explanation:
Microsoft 365 Copilot is designed to work within the Microsoft 365 ecosystem and use organizational context that is already governed by Microsoft Entra ID, Microsoft 365 permissions, and compliance controls. By default, Copilot can use Microsoft Graph signals and content that exist in Microsoft 365 workloads the user already has access to-most commonly emails, files, meetings, and chats . That corresponds to A .
The key concept is permission-trimming: Copilot doesn't magically gain access to everything; it can only surface or use data that the signed-in user is permitted to access in Microsoft 365. This is what makes Copilot valuable for productivity scenarios-summarizing email threads, drafting replies, generating meeting recaps, creating documents from your files, or pulling context from Teams chats-because those artifacts are already part of daily work and already subject to tenant policies.
The other options are not "by default" Microsoft Graph content for Copilot: B (file shares) typically requires additional integration or migration into Microsoft 365 repositories or indexing via connectors; it's not inherently available. C is unrelated to Microsoft 365 tenant work data. D (public web) is not "information in Microsoft Graph"; web grounding is a separate capability and not the default Graph workload data source referenced here.
NEW QUESTION # 17
Hotspot Question
Select the answer that correctly completes the sentence.
Answer:
Explanation:
Explanation:
Box: crafting clear instructions to guide generative AI solutions in generating context-appropriate content.
Prompt engineering is the process of ___________________.
Prompt engineering is the process of crafting, evaluating, and improving prompts to gain more accurate outputs from an AI model. Factors that improve prompts include the LLM's preferred format, specificity of language, appropriately identifying the audience's expectations, and making function calls for external data.
At its core, prompt engineering is about reducing ambiguity so the model doesn't have to "guess" what you want. It's the bridge between a vague idea and a high-quality output.
Beyond just clarity, modern prompting often involves specific frameworks like Chain-of-Thought (asking the AI to think step-by-step) or Few-Shot Prompting (providing examples) to significantly improve reasoning and accuracy.
Reference:
https://www.linkedin.com/pulse/using-prompt-engineering-optimize-genai-models-iabac-nfa9c
NEW QUESTION # 18
Select the answer that correctly completes the sentence.
Answer:
Explanation:
Explanation:
Generative AI is designed to create new outputs, such as text, images, code, summaries, emails, product descriptions, and conversational responses, based on patterns learned from training data and the user's prompt. Predictive AI is different because it focuses on forecasting outcomes, classifying data, or estimating probabilities from historical patterns, such as predicting customer churn, sales demand, or equipment failure.
Generative AI does not necessarily use a smaller dataset, and it does not automatically produce more accurate results than predictive AI. Accuracy depends on data quality, grounding, model design, and validation. The defining distinction is content creation: generative AI produces new material, while predictive AI predicts likely outcomes.
NEW QUESTION # 19
Your company uses a generative AI solution. You need to improve the quality of responses by using grounding. Which statement accurately describes how grounding improves accuracy and relevancy?
Answer: A
Explanation:
Grounding is an AI solution pattern used to improve response quality by ensuring the model's output is based on trusted, relevant information provided at inference time , rather than relying only on what the model "remembers" from training. Therefore, C is correct : grounding anchors responses in specific data sources .
In practical deployments, grounding commonly uses retrieval (often called Retrieval Augmented Generation, or RAG) where the system first finds relevant content from approved sources-such as internal policy documents, product documentation, knowledge bases, or databases-and then includes that content in the prompt context sent to the model. Microsoft's guidance describes grounding data as information supplied at inference time to help responses become more accurate and relevant because the model is guided by authoritative, up-to-date content that may not have been part of original training.
The other options do not define grounding. A relates to inclusion practices and diversity considerations, which are important for responsible AI but are not what grounding means. B describes transparency/explainability concepts. D relates to model evaluation/communication of limitations. Grounding is specifically about tying outputs to known sources , which reduces hallucinations and improves business trust in the generated responses.
NEW QUESTION # 20
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