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| Certification Vendor: | Microsoft |
|---|---|
| Exam Name: | Exam AB-731: AI Transformation Leader |
| Exam Number: | AB-731 |
| Exam Duration: | 45–65 |
| Certificate Validity Period: | 12 months |
| Available Languages: | English, Chinese (Simplified), Spanish, Japanese, German, French |
| Real Exam Qty: | 40–60 |
| Passing Score: | 700 |
| Exam Format: | Yes/No, Multiple choice, Drag and drop, Multiple response, Case studies |
| Exam Price: | $99 USD |
| 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 |
>> Valid AB-731 Test Syllabus <<
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NEW QUESTION # 35
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: C
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 # 36
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
Answer:
Explanation:
Explanation:
The correct selections are Yes, No, Yes. Prompt engineering is the practice of designing clear and effective inputs that guide a generative AI model toward relevant, accurate, and useful output. Guidelines, constraints, role instructions, tone requirements, and output-format rules are all common prompt-engineering techniques, so statement A is true. Statement B is false because examples are normally considered examples or few-shot demonstrations, not the instruction itself. The instruction tells the model what task to perform, while examples demonstrate the preferred pattern or output style. Statement C is true because context supplies the background information, source material, business scenario, audience, or constraints the model should use when generating the answer. Strong prompts usually combine instruction, context, constraints, and examples.
NEW QUESTION # 37
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: D
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 # 38
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: D
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 # 39
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:
The answer Area
* A manufacturer can use Azure Vision in Foundry Tools to identify product defects on an assembly line.
The answer : Yes
* A logistics company can use Azure Vision in Foundry Tools to recognize package shipping labels.
The answer : Yes
* The HR department at your company can only use Azure Vision in Foundry Tools to extract written content from Microsoft Word files. Answer: No Azure Vision in Foundry Tools provides computer vision capabilities to analyze images, including identifying visual features and reading text with OCR. Because it is designed to "analyze images" and support vision scenarios, it can be applied to manufacturing quality inspection use cases where the goal is to detect anomalies
/defects from images captured on a production line. This aligns with statement 1 being Yes.
Statement 2 is also Yes because recognizing shipping labels is fundamentally text extraction from images (often plus some layout/field parsing). Azure Vision supports optical character recognition (OCR) to read printed text from images, and Microsoft documentation explicitly notes OCR can extract text from images such as product labels and similar real-world text surfaces-making shipping labels a direct fit.
Statement 3 is No because it is incorrectly restrictive. Azure Vision is not limited to extracting written content from Word documents, nor is OCR restricted to Word files. Vision capabilities apply broadly to images (and, depending on the capability, various document/image inputs) for tasks like image analysis and text recognition. HR could use it for many scenarios such as extracting text from scanned images, photos, or other visual inputs-not "only" Word files.
NEW QUESTION # 40
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