無料でクラウドストレージから最新のJapancert AB-731 PDFダンプをダウンロードする：https://drive.google.com/open?id=1QfwZX32USkougDYtVJDN9an9REkJsDOl
AB-731試験の準備は精巧にまとめられており、非常に効率的です。時間と労力を節約できます。合格率とヒット率も非常に高く、数千人の受験者が当社のAB-731ガイドトレントを信頼し、試験に合格しています。候補者には非常に多くの保証を提供しており、AB-731学習教材を心配なく購入できます。そのため、当社Microsoftが提供するAB-731試験トレントを十分に理解し、最初の試行でAB-731試験に合格できることを願っています。
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質問 # 22
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:
* 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.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.
質問 # 23
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
A text-to-image generator (like DALL-E or Midjourney) takes textual descriptions and synthesizes them into visual imagery. It does not perform language translation workflows; that task requires natural language processing (NLP) or large language models (LLMs).
Box 2: No
Predictive analytics models rely on historical data and machine learning algorithms to forecast future trends, behaviors, or numerical outcomes (such as click-through rates or future sales).
They do not create or synthesize brand-new creative artifacts like marketing text or ad copies; that is the role of Generative AI.
Box 3: Yes
Generative AI chatbots (like those built using custom LLMs or Copilot Studio) excel at maintaining dynamic, human-like context. They can handle highly personalized conversations based on user profile inputs and dynamically synthesize relevant product recommendations.
質問 # 24
Hotspot Question
Select the answer that correctly completes the sentence.
正解：
解説：
Explanation:
Box: model inaccuracy
When a generative AI model produces output that seems realistic but contains incorrect information, the behavior is known as _______________.
That specific behavior-where the AI generates plausible-sounding but factually incorrect information-is known as hallucination.
While "model inaccuracy" is a broad way to describe it, "hallucination" specifically refers to when a generative AI model-like a large language model (LLM)-produces incorrect, misleading, or entirely fabricated information while presenting it as fact with a confident and plausible tone.
Reference:
https://www.techtimes.com/articles/314230/20260122/ai-hallucinations-explained-why-generative- ai-often-produces-inaccurate-results.htm
質問 # 25
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 # 26
Your company purchases Microsoft 365 Copilot for its sales department.
The sales department needs to find and summarize information across internal documents quickly.
From which two data sources can the sales department obtain results by default? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
正解：B、C
解説：
With Microsoft 365 Copilot, the two primary data sources used to ground data with internal documents are:
SharePoint
OneDrive
These sources allow Copilot to access, analyze, and summarize files (such as Word documents, PDFs, Excel files, and PowerPoint presentations) stored within your organization's Microsoft 365 tenant. Other sources mentioned in the context of grounding include Microsoft Teams chat history and emails.
Reference:
https://learn.microsoft.com/en-us/microsoft-copilot-studio/knowledge-copilot-studio
質問 # 27
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無料でクラウドストレージから最新のJapancert AB-731 PDFダンプをダウンロードする：https://drive.google.com/open?id=1QfwZX32USkougDYtVJDN9an9REkJsDOl