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| Section | Weight | Objectives |
|---|---|---|
| Identify the business value of generative AI solutions | 35–40% | - AI value identification
|
| Identify benefits, capabilities, and opportunities for Microsoft AI apps and services | 35–40% | - Microsoft AI ecosystem
|
| Identify an implementation and adoption strategy for Microsoft AI apps and services | 20–25% | - AI adoption strategy
|
人生は勝ち負けじゃない、負けたって言わない人が勝ちなのよ。近年Microsoft AB-731認定試験の難度で大方の受験生は試験に合格しなかったのに面して、勇者のようにこのチャレンジをやってますか。それで、我々社のMicrosoft AB-731無料の試験問題集サンプルを参考します。自分の相応しい復習問題集バージョン（PDF版、ソフト版を、オンライン版）を選んで、ただ学習教材を勉強し、正確の答えを覚えるだけ、Microsoft AB-731資格認定試験に一度で合格できます。
質問 # 66
HOTSPOT - Select the answer that correctly completes the sentence.
You use __________ to train a model that will forecast product demand based on historical sales data.
正解：
解説：
Explanation:
Azure Machine Learning
Forecasting product demand from historical sales data is a predictive analytics / machine learning use case.
It typically requires selecting an appropriate forecasting approach (for example, regression, tree-based methods, or time-series models), preparing and splitting historical data, training and validating the model, tuning hyperparameters, and then deploying the model for ongoing inference. The Microsoft service designed to support that end-to-end ML lifecycle is Azure Machine Learning , which is why it correctly completes the sentence.
Azure Machine Learning provides the tooling and infrastructure to: manage datasets, run training jobs on scalable compute, track experiments, compare model performance, register models, and operationalize them through managed endpoints and pipelines. This makes it well-suited for iterative forecasting work, where you may retrain on new data regularly, monitor drift, and update models as product lines, promotions, or seasonality patterns change.
The other options do not directly fit "train a model" for forecasting. Azure AI Search is an indexing/retrieval service used to search and ground generative AI responses, not for training predictive models. Azure OpenAI provides access to large language and multimodal models for generative tasks (drafting, summarizing, Q & A) and is not the primary platform for building classical forecasting models. Microsoft Foundry is a broader platform experience for building and governing AI apps and agents, but the specific service for training a forecasting model on historical sales data is Azure Machine Learning.
質問 # 67
Hotspot Question
You need to recommend an AI solution for each task. The solutions must use prebuilt AI capabilities to reduce development time.
What should you recommend for each task? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
正解：
解説：
質問 # 68
Your company is developing an AI-powered customer support agent.
You need to ensure that the solution follows Microsoft responsible AI principles.
Which two actions should you perform? Select the two BEST answers. Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
正解：A、B
解説：
[B]
Under Microsoft's Responsible AI framework, this testing specifically addresses the principles of Fairness and Inclusiveness. To operationalize these, you should focus on the following testing and development areas:
Key Testing Focus Areas
*-> Cultural Sensitivity: Ensure the agent respects different values, perspectives, and international contexts to avoid causing offense or misunderstanding.
*-> Fairness and Bias Mitigation: Test the agent to ensure it treats all users equitably and does not reinforce societal stereotypes or discriminate based on protected characteristics like race, gender, or religion.
Accessibility: Validate that the agent is usable by people of all abilities, providing equal power and engagement regardless of their background.
Global Community Engagement: Involve diverse users and underserved communities in the pre- deployment validation and feedback process to identify representation gaps.
[E]
To align with Microsoft's Responsible AI principles, providing a clear disclaimer that users are interacting with an AI solution is a core requirement of the Transparency principle.
Under this principle and the Microsoft Responsible AI Standard, developers must ensure that AI systems are understandable and that users are not deceived into believing they are interacting with a human.
Reference:
https://learn.microsoft.com/en-us/legal/ai-code-of-conduct
https://www.microsoft.com/en-us/ai/principles-and-approach
質問 # 69
For each of the following statements, select Yes if the statement is true. Otherwise, select No. NOTE: Each correct selection is worth one point.
正解：
解説：
Explanation:
Answer Area
* Retrieval Augmented Generation RAG requires model fine-tuning. Answer: No
* Retrieval Augmented Generation RAG is helpful when you need a generative AI solution that can access current, verifiable information. Answer: Yes
* Retrieval Augmented Generation RAG enables you to get more relevant responses based on your organization's documents without retraining the base model. Answer: Yes RAG is an architecture pattern that improves generative AI responses by retrieving relevant information from external knowledge sources (for example, a document index, database, or knowledge base) and injecting that information into the model's prompt/context at runtime.
* No - RAG does not inherently require fine-tuning. Fine-tuning changes the model weights. RAG, instead, keeps the base model as-is and augments it with retrieved context. Fine-tuning can be complementary (for style, domain tone, or specialized tasks), but it is not required for RAG to work.
* Yes - RAG is especially valuable when you need current and verifiable information because the retrieval layer can pull the latest approved content (updated policies, product specs, incident runbooks) and provide it to the model. This reduces hallucinations and makes answers traceable to known sources.
* Yes - A major benefit of RAG is improved relevance to organizational documents without retraining. Instead of rebuilding the model whenever documents change, you update the underlying content store/index; the model then generates responses grounded in the retrieved passages, producing answers that align with your organization's latest information and terminology.
質問 # 70
Your company is reviewing a new AI solution before deploying it. The company wants to ensure that the solution follows Microsoft responsible AI principles. What is the best approach to achieve the goal? More than one answer choice may achieve the goal. Select the BEST answer.
正解：B
解説：
Following Microsoft's responsible AI principles--fairness, reliability and safety, privacy and security, inclusiveness, transparency, and accountability--requires a structured, end-to-end testing approach to identify and mitigate bias. Testing to ensure fair and consistent outcomes involves auditing data, validating model performance across subgroups, and using specific technical tools.
Reference:
https://www.microsoft.com/en-us/research/blog/responsible-ai-the-research-collaboration-behind-new-open-source-tools-offered-by-microsoft
質問 # 71
......
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