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質問 # 102
- Select the answer that correctly completes the sentence.
Using high-quality grounding data in a generative AI solution __________.
正解：
解説：
Explanation:
improves the accuracy and reliability of the predictions and outputs of AI.
High-quality grounding data improves a generative AI solution by anchoring responses to trusted, relevant, and up-to-date information , which increases the likelihood that outputs are accurate, consistent, and aligned with the organization's expectations. This is why the best completion is " improves the accuracy and reliability of the predictions and outputs of AI ." When the model is given authoritative context (for example, approved policy text, product specifications, knowledge base articles, or controlled enterprise content), it has less need to "guess" based on general patterns in its training data. That reduces hallucinations and improves response relevance to the user's question and the business domain.
It does not "ensure that all responses are factually accurate" because grounding reduces errors but cannot eliminate them completely-retrieval can return incomplete or irrelevant passages, user prompts can be ambiguous, and the model can still misinterpret context. It also does not inherently "increase performance of an AI model" in the sense of speed/throughput or model capability; grounding is an architecture and data strategy that improves output quality, not compute efficiency. Finally, grounding is not about "increasing storage required to host an AI model." While you may store documents in an index or repository, the core benefit is improved response quality through better context, not larger model hosting requirements.
質問 # 103
Match the business scenario to the appropriate AI solution design approach. Each solution may be used once, more than once, or not at all.
正解：
解説：
Explanation:
* The marketing department at your company wants AI to summarize emails and create presentations.
The answer: Use Microsoft 365 Copilot
* The HR department at your company wants a conversational agent for policy questions and leave requests. Answer: Build with Microsoft Copilot Studio
* The manufacturing department at your company wants AI to predict maintenance schedules. Answer:
Build with Azure Machine Learning
* The finance department at your company wants AI-powered access to enterprise resource planning ERP data by using familiar productivity tools. Answer: Extend with Microsoft 365 Copilot connectors These scenarios map to four distinct solution patterns: out-of-the-box productivity assistance, low-code conversational agents, predictive ML, and enterprise data integration.
Marketing's need to summarize emails and create presentations is a core "productivity copilot" use case.
Microsoft 365 Copilot is embedded in Outlook, Word, PowerPoint, and Teams, so it directly supports summarization, drafting, and presentation generation without building a custom solution-making Use Microsoft 365 Copilot the best fit.
HR's requirement is a conversational agent tailored to internal policies and workflows such as leave requests.
That typically needs custom dialog, grounded knowledge sources, and possibly actions/workflows. Microsoft Copilot Studio is designed to build and manage such agents with organizational knowledge and business process integration, so Build with Microsoft Copilot Studio fits best.
Manufacturing's predictive maintenance scheduling is classic predictive analytics: learning patterns from historical telemetry/maintenance data to forecast failures or optimal service windows. This is best addressed with Azure Machine Learning , which supports training, evaluating, and deploying custom predictive models.
Finance wants AI-powered access to ERP data "using familiar productivity tools," which implies bringing external line-of-business data into the Microsoft 365 Copilot experience. That is precisely where Microsoft
365 Copilot connectors help-indexing and exposing enterprise data sources so Copilot can reference them in a governed way-so Extend with Microsoft 365 Copilot connectors is the best approach.
質問 # 104
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: Yes
Yes - Microsoft Foundry helps organizations securely build and manage generative AI solutions governed environment.
Microsoft Foundry is a unified, interoperable platform designed to help organizations build, optimize, and manage generative AI applications and autonomous agents within a secure, governed environment. It acts as a central "AI app and agent factory" that brings together models, data, and tools, allowing businesses to move from prototyping to production while maintaining safety and compliance.
Box 2: Yes
Yes - Microsoft Foundry provided built-in scalability to enable organizations to expand AI workloads as usage increases.
Microsoft Foundry acts as an enterprise-grade, unified platform for AI app and agent development, designed to enable organizations to build, deploy, and scale AI workloads efficiently. It provides built-in, automated scalability through several key mechanisms that allow organizations to expand their AI usage without manual infrastructure management.
Box 3: Yes
Yes - Microsoft Foundry can be used for image recognition and computer vision tasks.
Microsoft Foundry (part of Azure AI Services/Tools) offers Azure Vision, a comprehensive suite for image recognition and computer vision tasks. It provides prebuilt APIs and tools for analyzing images, detecting objects, OCR, and facial recognition, allowing developers to build intelligent, agentic applications without deep machine learning expertise.
Reference:
https://learn.microsoft.com/en-us/azure/ai-foundry/what-is-foundry
https://azure.microsoft.com/en-us/products/ai-foundry
質問 # 105
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 # 106
HOTSPOT - 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
* You can use Azure Language in Foundry Tools to analyze the sentiment of customer reviews. Answer: Yes
* You can use Azure Language in Foundry Tools to translate internal reports into multiple languages. Answer: No
* You can use Azure Language in Foundry Tools to extract text from scanned documents. Answer: No Azure Language is designed for natural language processing (NLP) over text that is already machine- readable. That includes capabilities like sentiment analysis , key phrase extraction, entity recognition, summarization, and classification. Therefore, statement 1 is Yes : sentiment analysis of customer reviews is a standard NLP workload where the service scores text as positive/negative/neutral (and often provides confidence scores), helping organizations quantify customer satisfaction and detect recurring issues.
Statement 2 is No because translation is typically handled by a dedicated translation capability (commonly delivered as a separate translator service) rather than the core "Language" NLP features. While translation is an AI language workload, it's not what the Azure Language service is primarily used for in this context; the expected Microsoft service choice for multi-language translation is the translator capability, not Azure Language.
Statement 3 is No because extracting text from scanned documents is OCR (optical character recognition), which is a computer vision/document processing function. OCR is delivered through Azure Vision and/or Azure Document Intelligence , which can read printed/handwritten text from images and PDFs and return structured output. Azure Language can analyze extracted text after OCR, but it does not perform the image-to- text extraction step itself.
質問 # 107
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