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一生懸命にAB-731日本語版トレーリング &合格スムーズAB-731試験問題解説集 |正確的なAB-731認定資格試験問題集

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Microsoft AB-731 認定試験の出題範囲：

トピック
出題範囲

トピック 1

生成型AIソリューションのビジネス価値を特定する：生成型AIの中核概念、コスト要因、ビジネス上の課題に加え、データ品質、セキュリティ、機械学習手法の向上を通じてAIの価値を高めるプロンプトエンジニアリングやRAGなどの技術についても解説します。

トピック 2

MicrosoftのAIアプリとサービスの導入および採用戦略を特定する：責任あるAIの原則、ガバナンス、組織的な採用計画（AI評議会、チャンピオンプログラム、CopilotおよびAzure AIライセンスモデルの理解を含む）について解説します。

トピック 3

マイクロソフトのAIアプリとサービスのメリット、機能、機会を特定する：Microsoft 365 Copilot、Copilot Studio、Azure AI Foundryツールを含むマイクロソフトのAIエコシステムを実際のビジネスユースケースにマッピングすることに重点を置き、組み込みのスケーラビリティ、セキュリティ、安全性のメリットを活用します。

>> AB-731日本語版トレーリング <<

Microsoft AB-731試験問題解説集 & AB-731認定資格試験問題集

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Microsoft AI Transformation Leader 認定 AB-731 試験問題 (Q49-Q54):

質問 # 49
- Select the answer that correctly completes the sentence.
The primary goal of generative AI is __________.

正解：

解説：

Explanation:
to create new content, such as text, images, or code.
Generative AI is defined by its ability to produce new outputs -content that did not previously exist in exactly that form-based on patterns learned from large datasets. That content can be text (emails, summaries, policies), images (design mockups, marketing visuals), code (snippets, scripts), audio, and more. Therefore, the correct completion is "to create new content, such as text, images, or code." The other options describe different AI categories. "Analyze trends and classify data sources" is primarily analytical/classification work, typically associated with traditional machine learning models (for example, clustering, categorization, fraud classification). "Make predictions based on historical data" is predictive AI (forecasting demand, predicting churn, estimating failure probability). While generative AI can assist those workflows by explaining results or drafting narratives, its primary purpose is not classification or forecasting-it is content synthesis.
In practical business value terms, this is why generative AI is commonly deployed for productivity tasks like drafting and rewriting content, summarizing long documents, generating customer communications, creating knowledge assistants, and producing structured outputs (tables, bullet lists, JSON) from unstructured prompts.
The model's differentiator is its ability to transform instructions and context into coherent, human-like content.

質問 # 50
- Select the answer that correctly completes the sentence.
The primary goal of generative AI is __________.

正解：

解説：

質問 # 51
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 # 52
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.

正解：

解説：

Explanation:
Box 1: No
No - Allowing AI models to make autonomous decisions support Microsoft AI principle of accountability.
Microsoft's principle of accountability actually mandates that humans, not AI models, remain the final authority for how a system operates. While AI can perform automated tasks, the accountability principle requires that the people who design and deploy these systems take responsibility for their impact and maintain meaningful control.
Box 2: Yes
Yes - Regularly testing AI models for fairness and inclusiveness helps ensure they align with Microsoft's Responsible AI principles.
Regularly testing AI models for fairness and inclusiveness is a foundational practice within Microsoft's Responsible AI Standard, which acts as a guide for developing and deploying AI systems. This continuous testing ensures that AI applications do not reinforce historical biases and perform equitably across different demographic groups, including race, gender, age, and background.
Box 3: Yes
Yes - Protecting user data and limiting access to personal information supports the Microsoft responsible AI principles of privacy and security.
Protecting user data and limiting access to personal information are, in fact, foundational to Microsoft's Responsible AI principles of Privacy and Security. Microsoft's AI framework mandates that AI systems are developed and deployed in a manner that respects user privacy and maintains strict data security, aiming for AI systems that are "secure by design".
Reference:
https://learn.microsoft.com/en-us/azure/machine-learning/concept-responsible-ai
https://techcommunity.microsoft.com/blog/nonprofittechies/the-importance-of-responsible-ai-a- comprehensive-guide/4404347

質問 # 53
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.

正解：

解説：

Explanation:
Box 1: No
No - A generative AI model guarantees factually accurate responses if the model is trained on a large dataset.
A large training dataset does not guarantee that a generative AI model will provide factually accurate responses. While larger, diverse datasets generally improve performance and reduce certain types of errors, they do not eliminate the fundamental tendency of these models to generate incorrect information, known as "hallucinations".
Box 2: Yes
Yes - Content filtering and responsible AI safeguards help a generative AI model generate safe an inoffensive content.
Content filtering and responsible AI safeguards (e.g., in Azure AI Foundry or Amazon Bedrock ) act as essential, multi-layered, reactive mechanisms-covering both input and output-to detect and block harmful, illegal, or biased content. These systems use automated classifiers to, for example, filter for hate speech, sexual content, violence, and self-harm. They ensure safety by analyzing prompts and generating responses, often allowing for custom thresholds, to prevent models from generating unsafe or inappropriate output.
Box 3: No
No - A generative AI model always produce fair and unbiased results when the training data has been properly prepared and reviewed for fairness.
Even with perfectly prepared and reviewed training data, generative AI models can still produce biased results. While high-quality data is foundational, bias is a persistent challenge that can emerge from multiple sources throughout the AI lifecycle.
Reference:
https://mehmetozkaya.medium.com/limitations-of-large-language-models-llms-1790a14010db
https://monowar-mukul.medium.com/keeping-your-ai-safe-content-filters-in-azure-ai-foundry-
9a87c8447e11
https://www.sap.com/resources/what-is-ai-bias

質問 # 54
......

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