멋진 IT전문가로 거듭나는 것이 꿈이라구요? 국제적으로 승인받는 IT인증시험에 도전하여 자격증을 취득해보세요. IT전문가로 되는 꿈에 더 가까이 갈수 있습니다. Microsoft인증 AB-731시험이 어렵다고 알려져있는건 사실입니다. 하지만DumpTOP의Microsoft인증 AB-731덤프로 시험준비공부를 하시면 어려운 시험도 간단하게 패스할수 있는것도 부정할수 없는 사실입니다. DumpTOP의Microsoft인증 AB-731덤프는 실제시험문제의 출제방형을 철저하게 연구해낸 말 그대로 시험대비공부자료입니다. 덤프에 있는 내용만 마스터하시면 시험패스는 물론 멋진 IT전문가로 거듭날수 있습니다.
| Certification Vendor: | Microsoft |
|---|---|
| Exam Name: | Exam AB-731: AI Transformation Leader |
| Exam Number: | AB-731 |
| Exam Price: | $99 USD |
| Available Languages: | French, German, English, Spanish, Japanese, Chinese (Simplified) |
| Certificate Validity Period: | 12 months |
| Exam Format: | Multiple choice, Drag and drop, Multiple response, Case studies, Yes/No |
| Exam Duration: | 45–65 |
| Real Exam Qty: | 40–60 |
| Passing Score: | 700 |
| 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 |
빨리 DumpTOP 덤프를 장바구니에 넣으시죠. 그러면 100프로 자신감으로 응시하셔서 한번에 안전하게 패스하실 수 있습니다. 단 한번으로Microsoft AB-731인증시험을 패스한다…… 여러분은 절대 후회할 일 없습니다.
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질문 # 77
Your company sells hiking and camping gear online. You need a generative AI solution that can interact with customers and ask questions about their needs. What should you include in the solution?
정답：C
질문 # 78
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.
정답：
설명：
질문 # 79
- For each of the following statements, select Yes if the statement is true. Otherwise, select No.
정답：
설명：
Explanation:
The answer Area
* A generative AI model guarantees factually accurate responses if the model is trained on a large dataset.
The answer: No
* Content filtering and responsible AI safeguards help a generative AI model generate safe and inoffensive content. Answer: Yes
* A generative AI model always produces fair and unbiased results when the training data has been properly prepared and reviewed for fairness. Answer: No
* No - A larger training dataset can improve coverage and fluency, but it does not guarantee factual accuracy. Generative models can still hallucinate, mix concepts, or produce plausible-but-incorrect statements because they generate likely text rather than verifying truth. This is why solution designs commonly add grounding/retrieval, validation, and human review for high-stakes outputs.
* Yes - Content filtering and Responsible AI controls are specifically used to reduce harmful, unsafe, or policy-violating outputs . In practice, safeguards include input/output filters, safety classifiers, and governance controls that help enforce safety policies and minimize offensive content. These controls don't make outputs "perfect," but they materially reduce risk and are a standard part of production AI deployments.
* No - Even with careful data preparation and fairness reviews, models can still produce biased outcomes due to residual bias in data, label/measurement issues, deployment context, and shifting real- world distributions. "Always fair and unbiased" is an absolute claim that is not achievable in real systems; fairness is managed through continuous evaluation, monitoring, and mitigations-not assumed as guaranteed.
질문 # 80
For each of the following statements, select Yes if the statement is true. Otherwise, select No . NOTE: Each correct selection is worth one point.
정답：
설명：
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.
질문 # 81
Drag and Drop Question
Match the business scenario to the appropriate AI solution design approach.
To answer, drag the AI solution from the column on the left to its business scenario on the right.
Each solution may be used once, more than once, or not at all.
NOTE: Each correct match is worth one point.
정답：
설명：
Explanation:
Box 1: Use Microsoft 365 Copilot
Summarizing emails (Outlook) and creating presentations (PowerPoint) are native, out-of-the-box productivity capabilities built directly into Microsoft 365 Copilot. There is no need to develop a custom solution or build novel connectors here; the standard user license fully covers these daily workplace workflows.
Box 2: Build with Microsoft Copilot Studio
Creating a customized, conversational agent (formerly Power Virtual Agents) to handle specific internal business logic-like answering HR policy questions and triggering a workflow to submit leave requests-falls squarely under Microsoft Copilot Studio. This allows you to build a tailored chatbot with specific conversation topics, triggers, and actions.
Box 3: Build with Azure Machine Learning
Predictive maintenance schedules require deep data science workflows, including custom data ingestion from manufacturing sensors (IoT), training custom regression or classification models, and processing time-series data. This requires the robust, full-lifecycle data science capabilities of Azure Machine Learning rather than an LLM-based productivity tool.
Box 4: Extend with Microsoft 365 Copilot connectors
The finance department wants to use their familiar productivity tools (like Excel or Teams via Microsoft 365 Copilot) but needs those tools to access outside, third-party data silos (ERP data).
To allow the native Copilot to securely read and interact with non-Microsoft 365 data repositories, you must Extend with Microsoft 365 Copilot connectors (via Graph connectors or Power Platform connectors).
질문 # 82
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AB-731시험대비: https://www.dumptop.com/Microsoft/AB-731-dump.html