サンプル問題 1： Which business requirement most closely relates to grounding a generative AI model?
A: supporting multiple languages
B: measuring the number of user interactions per day
C: enabling users to interact by using natural language queries
D: ensuring that verified company data sources are used for response generation
正解：D
Explanation: Grounding in generative AI means ensuring model outputs are based on trusted, relevant information sources rather than only on the model’s general training data. In a business context, grounding is about aligning responses with verified enterprise knowledge (policies, product documentation, internal procedures, approved FAQs, etc.) so the system is more accurate, consistent, and defensible. That is exactly what option D describes: “ensuring that verified company data sources are used for response generation.” In Microsoft AI solution patterns, grounding is commonly achieved using retrieval-augmented generation (RAG). With RAG, the system retrieves relevant passages from approved company repositories (for example, indexed documents or knowledge bases) and supplies them as context to the model during response generation. This reduces hallucinations, improves factual correctness, and makes answers more relevant to the organization’s reality―critical when AI is used for customer support, employee helpdesks, compliance guidance, or executive reporting. The other options do not directly address grounding. A relates to localization/multilingual capability, B is a usage/telemetry metric, and C is an interaction method (natural language interface). They can all be important requirements, but none of them ensure outputs are anchored to verified company data―the core purpose of grounding
サンプル問題 2： Your company uses a non-reasoning generative AI model to create textual content. You discover that the model’s responses are inconsistent and do NOT meet expectations. You need to improve the prompts. What should you do? More than one answer choice may achieve the goal. Select the BEST answer.
A: Provide the prompts with extensive examples of the expected output.
B: Add the context, sources, and expectations to the prompts.
C: Use technical terms in the prompts to enhance AI comprehension.
D: Add only a single concise requirement to the prompts.
正解：A, B
Explanation: When a non-reasoning generative AI model produces inconsistent outputs, the most reliable improvement is to make the prompt more specific, constrained, and demonstrative of what “good” looks like. A is correct because adding high-quality examples is a form of few-shot prompting. Examples act like “training wheels” at inference time: they show the model the desired structure, tone, level of detail, formatting rules, and boundaries. This reduces ambiguity and variance, especially for tasks like marketing copy, summaries, policy text, or customer replies. The more your examples resemble real target outputs (including edge cases), the more consistent the model’s completions become. B is correct because adding context, relevant source material, and explicit expectations narrows the model’s degrees of freedom. Including the intended audience, purpose, constraints (length, voice, banned claims), and trusted reference content (approved facts, product specs, policy excerpts) helps the model stay aligned and reduces hallucinations and off-brand language. This is also where you specify acceptance criteria such as “must include 3 bullet points,” “use UK English,” or “cite only provided text.” C is not best: technical jargon can confuse or bias output if it’s not aligned to the task; clarity beats jargon. D is not best: a single concise requirement is usually under-specified and often increases variability.
サンプル問題 3： You have a historical dataset that contains 1,000 records. You need an AI solution that can analyze the data to identify patterns and predict future outcomes. What should you include in the solution?
A: Microsoft Foundry
B: Azure Document Intelligence in Foundry Tools
C: Azure Machine Learning
D: Azure Content Understanding in Foundry Tools
正解：C
Explanation: The requirement describes a predictive analytics / machine learning scenario: using historical data to learn patterns and then predict future outcomes. The Microsoft service that directly supports the end-to-end machine learning lifecycle―data preparation, model training, evaluation, deployment, and MLOps―is Azure Machine Learning, which is why C is the best choice. Azure Machine Learning is explicitly designed to help data scientists and engineers train and deploy models and manage the ML project lifecycle, making it the right fit for building a predictive model from your dataset. The other options focus on different problem classes: Azure Document Intelligence is for extracting structured data from documents (OCR, key-value pairs, tables), not for general predictive modeling. Azure Content Understanding is for deriving structured insights from multimodal content (documents, images, audio, video) into a user-defined schema; it’s not the primary service for training predictive models from a tabular historical dataset. Microsoft Foundry is a broader platform for building AI apps/agents and orchestrating models/tools, but the specific need here is classical ML training and prediction―handled most directly by Azure Machine Learning.
Microsoft AB-731試験準備をしていますか？それとも自分は全部できそうです。復習する必要がないと思いますか？それとも自分は何もできないような気がします。どうすればいいでしょうか？実は、この問題を解決するのは難しくないです。勉強者がやるべきのは分解分析をマスターすることです。ここではAB-731問題集を使って学習中の課題を確認し、知識を整理してAI Transformation Leader (beta)試験の準備を進めます。
AB-731問題集を利用して学習中の問題を探し出す。
Microsoft AB-731試験のすべての試験資料ポイントを勉強した後、AB-731問題集の中の問題点を練習してみてください。重要な試験点はどこにあるか、つまりMicrosoftが把握してほしいものは何ですか？そして練習中の問題点を整理して分析してください。できない問題に対して、正しい答えの考えをはっきりさせたいです。それに関する知識点を明らかにします。間違った問題に対しては、具体的に自分がどこで間違っているかを見に行きます。このステップで使う試験資料ポイントは何ですか？間違った問題をこのように一歩分解して分析すると、自然に自分の復習する方向を見つけます。
