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質問 # 90
- 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
* Prompt engineering changes how a generative AI model was trained. Answer: No
* Prompt engineering focuses on designing clear, concise, and context-rich instructions. Answer: Yes
* Effective prompt engineering involves maximizing the number of tokens used in each request. Answer:
No
* No - Prompt engineering does not modify model weights or retrain the model. It is an inference-time technique : you steer outputs by improving the instructions and context you send to the model.
Changing how a model was trained would involve pretraining, fine-tuning, or other training methods- separate from prompt engineering.
* Yes - The primary goal of prompt engineering is to reduce ambiguity and variability by providing clear instructions , the right context , and explicit output constraints . This often includes specifying role and task, providing necessary facts or grounding text, defining format (bullets, JSON, headings), and adding examples (few-shot) when helpful. Well-constructed prompts improve consistency, relevance, and usefulness of outputs.
* No - Good prompt engineering does not mean "use as many tokens as possible." In fact, using unnecessary tokens can increase cost and may degrade quality by adding noise. Effective prompts are typically as short as possible but as long as necessary : enough context to achieve accuracy and alignment, but not bloated. Token discipline matters because most pricing is token-based and long contexts can dilute attention over what's most important.
質問 # 91
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 - A manufacturer can use Azure Vision in Foundry Tools to identify product defects on an assembly line.
A manufacturer can use Azure Vision in Foundry Tools (formerly part of Azure AI Services, now integrated within the AI Foundry toolkit) to identify product defects on an assembly line. This solution automates visual inspections to detect anomalies such as surface scratches, cracks, misalignments, or missing components in real time.
Box 2: Yes
Yes - A logistics company can use Azure Vision in Foundry Tools to recognize package shipping labels.
A logistics company can use Azure Vision in Foundry Tools (part of the broader Azure AI services suite) to recognize, interpret, and digitize package shipping labels. By integrating Azure's advanced AI with Palantir Foundry, firms can automate manual data entry, track shipments, and improve operational efficiency.
Box 3: No
No - The HR department at your company can only use Azure Vision in Foundry Tools to extract written content from Microsoft Word files.
Azure Vision in Foundry Tools is primarily designed for images, while its sibling tool, Document Intelligence, handles Microsoft Word files.
Reference:
https://datalabs.io/azure-ai-for-smart-manufacturing-defect-detection-with-computer-vision
https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/overview
https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/concept-ocr
質問 # 92
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
To use Microsoft 365 Copilot chat, you must have a Microsoft Copilot license.
You do not necessarily need a separate "Microsoft 365 Copilot" add-on license to use the Copilot Chat feature.
Microsoft now includes a baseline version of Copilot Chat at no additional cost for users with eligible Microsoft 365 and Office 365 subscriptions.
Here is how the licensing impacts your experience:
*-> Without a Copilot Add-on License: You can still use Copilot Chat if you have a qualifying base subscription (such as Business Standard, E3, or E5). This version is web-grounded, meaning it can answer questions using public internet data and context from your currently open file, but it cannot search through your entire organization's emails, meetings, or files.
With a Microsoft 365 Copilot License: Adding this paid license transforms Copilot into a work- grounded assistant. It gains the ability to "reason" across your entire Microsoft Graph-searching your private inbox, calendar, and SharePoint documents to provide context-aware answers.
Box 2: Yes
Yes - Microsoft 365 Copilot chat provides context-aware assistance in Microsoft 365 apps.
Microsoft 365 Copilot chat is an AI-powered, context-aware assistant embedded directly into Microsoft 365 apps (Word, Excel, PowerPoint, Outlook, OneNote) to streamline workflow, summarize content, and create documents. It operates within a secure environment, using organizational data-including emails, chats, and files-to provide tailored assistance, with or without a separate paid add-on license.
Key Features and Capabilities:
*-> Context-Aware Assistance: Interacts with the user's active files (e.g., summarizing an open Word document) and Microsoft Graph data to provide relevant, in-the-moment help.
Integrated Apps: Available in a side pane in Word, Excel, PowerPoint, Outlook, and OneNote.
Content Generation & Summarization: Helps draft content, revise tones, summarize long email threads, and analyze data.
Secure Data Usage: Built-in with enterprise-grade data protection, ensuring that user data remains secure and is not used to train public models.
Functionality: Capabilities include uploading images, expanding input boxes, and providing quick access to agents and page-creation tools.
Box 3: No
No - Microsoft 365 Copilot chat can only access information in open files and read emails.
While Copilot does work with active, open content, it is designed to ground its responses in a much broader range of organizational data. It uses the Microsoft Graph to access, search, and summarize data across your entire Microsoft 365 tenant, provided you have the necessary permissions.
Reference:
https://www.microsoft.com/en-us/microsoft-365-copilot/pricing/enterprise
https://support.microsoft.com/en-gb/topic/how-copilot-chat-works-with-and-without-a-microsoft-
365-copilot-license-5810b659-fbe0-48ee-9fe6-d731fe86cdeb
https://learn.microsoft.com/en-us/copilot/microsoft-365/microsoft-365-copilot-privacy
質問 # 93
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 # 94
Your company deploys an AI-powered loan approval solution that enables applicants to request an explanation as to why their loan application was denied.
Which Microsoft responsible AI principle is this an example of?
正解：B
解説：
According to Microsoft's guidelines, transparency means that AI systems should be understandable, and users should be able to understand the system's decisions or recommendations. Providing an explanation for a loan denial allows applicants to understand how the AI arrived at its decision.
Reference:
https://www.linkedin.com/pulse/deep-dive-responsible-ai-digitalbricksai-typie
質問 # 95
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