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| Certification Vendor: | Microsoft |
|---|---|
| Exam Name: | Microsoft Certified: AI Transformation Leader |
| Exam Number: | AB-731 |
| Exam Duration: | 45 minutes |
| Certificate Validity Period: | 1 year (renewable) |
| Exam Format: | Multiple choice, Build list, Case studies, Drag and drop |
| Related Certifications: | Microsoft Certified: AI Transformation Leader |
| Available Languages: | English |
| Exam Price: | USD 99 |
| Real Exam Qty: | 40-60 |
| Passing Score: | 700 / 1000 |
| Sample Questions: | Microsoft AB-731 Sample Questions |
| Exam Way: | Online or at a test centre |
| Pre Condition: | No formal prerequisites. Recommended: familiarity with Microsoft 365 services, Azure AI services, and experience with adoption or change management in a business context. This certification is designed for business decision-makers at all levels; no coding is required. |
| Official Syllabus URL: | https://learn.microsoft.com/en-us/credentials/certifications/resources/study-guides/ab-731 |
>> AB-731 Zertifizierungsantworten <<
Wir alle wissen, dass die Microsoft AB-731 Zertifizierungsprüfung in der IT-Branche eine zentrale Position darstellt. Aber die Kernfrage ist, dass es schwer ist, ein Microsoft AB-731 Zertifikat zu erhalten. Wir wissen genau, dass im Internet relevanten Prüfungsmaterialien von guter Qualität fehlen. Die Examsfragen und Antworten von ITZert können allen an den Zertifizierungsprüfungen teilnehmenden Prüflingen irgendwann die notwendigen Informationen liefern. Wir versprechen Ihnen, dass Sie Ihre Microsoft AB-731 Zertifizierungsprüfung einmalig bestehen können.
| Thema | Einzelheiten |
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| Thema 1 |
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| Thema 2 |
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| Thema 3 |
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19. Frage
Your company has a Microsoft 365 subscription and uses Microsoft 365 Copilot Chat. Some users need to build and use declarative agents that can access work data. Which type of license should you recommend for the users?
Antwort: B
Begründung:
The requirement is specific: users must build and use declarative agents that can access work data (tenant data
/ organizational context). Microsoft's licensing guidance for Copilot extensibility ties use of declarative agents to having the appropriate Copilot entitlement that enables tenant grounding and organizational data access. In Microsoft's cost and licensing considerations for declarative agents, Microsoft states that to use a declarative agent, users must have a Microsoft 365 Copilot add-on license (or an equivalent Copilot Chat add- on path tied to eligible licensing). Therefore, among the provided options, the best recommendation is A.
Option B (Copilot Studio user license) is primarily about authoring/building agents in Copilot Studio, but it is not, by itself, the licensing prerequisite that grants end users the right to use those agents with full Microsoft
365 Copilot capabilities and work-data grounding inside the Microsoft 365 Copilot environment. Publishing
/building can be separate from the end-user entitlement to use the agent with organizational context.
Option C (Copilot Chat pay-as-you-go) can enable usage-based access to declarative agents in some configurations, but the question asks for the best license recommendation for users who need work-data access through declarative agents. The Microsoft 365 Copilot add-on is the straightforward, fully supported entitlement for that scenario.
20. Frage
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?
Antwort: C
Begründung:
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.
21. Frage
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?
Antwort: C
Begründung:
Data governance is essential to prevent generative AI (GenAI) from accessing confidential or classified information, as it provides the necessary framework, policies, and controls to manage data security throughout the AI lifecycle. Effective governance involves a combination of data classification, strict access controls, and active monitoring to ensure only authorized, non- sensitive data is used.
Reference:
https://dacodes.com/blog/data-governance-best-practices-for-generative-ai
22. Frage
HOTSPOT - For each of the following statements, select Yes if the statement is true. Otherwise, select No .
NOTE: Each correct selection is worth one point.
Antwort:
Begründung:
Explanation:
The answer Area
* You can use Azure Language in Foundry Tools to analyze the sentiment of customer reviews. Answer:
Yes
* You can use Azure Language in Foundry Tools to translate internal reports into multiple languages.
The answer: No
* You can use Azure Language in Foundry Tools to extract text from scanned documents. Answer: No Azure Language is designed for natural language processing (NLP) over text that is already machine- readable. That includes capabilities like sentiment analysis , key phrase extraction, entity recognition, summarization, and classification. Therefore, statement 1 is Yes : sentiment analysis of customer reviews is a standard NLP workload where the service scores text as positive/negative/neutral (and often provides confidence scores), helping organizations quantify customer satisfaction and detect recurring issues.
Statement 2 is No because translation is typically handled by a dedicated translation capability (commonly delivered as a separate translator service) rather than the core "Language" NLP features. While translation is an AI language workload, it's not what the Azure Language service is primarily used for in this context; the expected Microsoft service choice for multi-language translation is the translator capability, not Azure Language.
Statement 3 is No because extracting text from scanned documents is OCR (optical character recognition), which is a computer vision/document processing function. OCR is delivered through Azure Vision and/or Azure Document Intelligence , which can read printed/handwritten text from images and PDFs and return structured output. Azure Language can analyze extracted text after OCR, but it does not perform the image-to- text extraction step itself.
23. Frage
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?
Antwort: C
Begründung:
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.
24. Frage
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