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問題 #72
Hotspot Question
Select the answer that correctly completes the sentence.
答案：
解題說明：
Explanation:
Box: guide AI strategy, ensure responsible AI oversight, and promote alignment across business units.
To ensure that your organization follows trustworthy AI principles, the organization should establish an AI governance council to _____________________.
An AI governance council should be a cross-functional, multidisciplinary body that oversees the development, deployment, and evaluation of AI solutions to ensure they are ethical, secure, and aligned with organizational values. Key functions include establishing accountability, managing risks, ensuring regulatory compliance (e.g., privacy), and promoting transparency and fairness.
Core Responsibilities
The council's mandate typically covers the entire AI lifecycle:
*-> Strategy & Policy: Developing internal AI usage policies and identifying high-risk use cases.
*-> Risk Oversight: Conducting impact assessments for bias, privacy, and security before deployment.
Continuous Monitoring: Tracking live models for "drift" (performance degradation over time) or emerging ethical issues.
Culture & Literacy: Driving organization-wide training to build AI fluency and a responsible AI culture.
Audit & Reporting: Maintaining transparent records and "model cards" to demonstrate accountability to stakeholders and regulators.
Reference:
https://athena-solutions.com/ai-governance-framework-2025/
問題 #73
Your company has an AI solution that uses a prebuilt Azure OpenAI model to generate content.
You need to reduce the cost of solution while minimizing the impact on the quality of the generated output.
Which two actions should you perform? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
答案：A,C
解題說明：
To reduce costs for a prebuilt Azure OpenAI model while maintaining high output quality, you should implement a combination of prompt optimization, caching, and model selection strategies.
[C]
1. Optimize Prompt Engineering
Reducing the number of tokens processed is the most direct way to lower costs.
Prompt Compression: Use tools like LLMLingua to remove redundant text from prompts without losing semantic meaning.
Task Decomposition: Break complex tasks into smaller, focused prompts to improve clarity and reduce expensive retries.
Shorten Context: Summarize long conversation histories or external RAG (Retrieval-Augmented Generation) documents before sending them to the model.
2. Implement Caching Mechanisms
[A]
3. Use Model Routing and Tiering
Matching request complexity to model capability can reduce costs by 30-60%.
*-> Model Cascading: Use a small, cheap model (e.g., GPT-4o-mini) for simple tasks like classification or basic formatting. Route only complex reasoning tasks to high-end models like GPT-4o.
Reasoning Effort Control: For newer models like GPT-5, adjust the reasoning_effort parameter to
"minimal" for straightforward tasks to save on compute.
Reference:
https://ekaivakriti.com/blog/how-to-reduce-openai-costs-by-60-percent/
問題 #74
- Select the answer that correctly completes the sentence.
A __________ AI solution recognizes patterns in large and complex datasets to create new and original content.
答案：
解題說明：
Explanation:
generative
The sentence describes an AI system that creates new, original content (for example, text, images, audio, code) by learning patterns from large datasets. That is the defining characteristic of generative AI , so
"generative" is the correct completion.
To clarify the contrasts: predictive AI primarily uses historical data to forecast outcomes or estimate probabilities (for example, predicting next-quarter sales, predicting equipment failure, or classifying whether a transaction is fraudulent). It focuses on what is likely to happen or which category something belongs to , rather than producing novel content. Prescriptive AI goes a step further by recommending actions or decisions (for example, "increase inventory by X," "route tickets to team Y," "schedule maintenance next week") often using optimization and business constraints.
Generative AI, by comparison, is focused on content synthesis . It identifies statistical patterns and relationships in training data and then generates new sequences that resemble the learned distribution while being responsive to prompts. In business use cases, that translates to drafting emails and documents, summarizing reports, generating meeting notes, creating product descriptions, producing marketing copy, and building chat-based assistants. While all three categories can "recognize patterns," only generative AI is explicitly characterized by the ability to produce new and original content , which is what the question is testing.
問題 #75
For each of the following statements, select Yes if the statement is true. Otherwise, select No. NOTE: Each correct selection is worth one point.
答案：
解題說明：
Explanation:
Answer Area
* A manufacturer can use Azure Vision in Foundry Tools to identify product defects on an assembly line. Answer: Yes
* A logistics company can use Azure Vision in Foundry Tools to recognize package shipping labels. Answer: Yes
* The HR department at your company can only use Azure Vision in Foundry Tools to extract written content from Microsoft Word files. Answer: No Azure Vision in Foundry Tools provides computer vision capabilities to analyze images, including identifying visual features and reading text with OCR. Because it is designed to "analyze images" and support vision scenarios, it can be applied to manufacturing quality inspection use cases where the goal is to detect anomalies/defects from images captured on a production line. This aligns with statement 1 being Yes .
Statement 2 is also Yes because recognizing shipping labels is fundamentally text extraction from images (often plus some layout/field parsing). Azure Vision supports optical character recognition (OCR) to read printed text from images, and Microsoft documentation explicitly notes OCR can extract text from images such as product labels and similar real-world text surfaces-making shipping labels a direct fit.
Statement 3 is No because it is incorrectly restrictive. Azure Vision is not limited to extracting written content from Word documents, nor is OCR restricted to Word files. Vision capabilities apply broadly to images (and, depending on the capability, various document/image inputs) for tasks like image analysis and text recognition. HR could use it for many scenarios such as extracting text from scanned images, photos, or other visual inputs-not "only" Word files.
問題 #76
Your company manages a website that publishes daily news articles. You need to recommend an AI solution that can analyze text and identify the main people, locations, and companies mentioned in the articles. What should you include in the recommendation?
答案：C
解題說明：
The requirement is to analyze text and identify "people, locations, and companies" mentioned in news articles. This is a classic Named Entity Recognition (NER) / entity extraction scenario, which falls under natural language processing. Azure Language in Foundry Tools is the correct choice because it provides text analytics capabilities that detect and categorize entities in unstructured text-commonly including Person, Location, and Organization. This enables downstream experiences such as topic tagging, search filters (e.g.,
"all articles mentioning Company X"), trend dashboards (top people/places mentioned this week), and improved content discovery.
The other options do not match the requirement. Content Safety focuses on moderating harmful or policy- violating content (for example, hate, violence, self-harm, sexual content) and is not the primary tool for extracting named entities. Azure Vision is for analyzing images and performing OCR; it would only be relevant if the articles were images or scans, but the task here is entity extraction from text articles. Azure Speech is for speech-to-text, text-to-speech, and audio analysis; it would be used if your input were audio recordings rather than written articles.
Therefore, to identify key entities (people, locations, companies) from daily news article text, the best recommendation is Azure Language in Foundry Tools.
問題 #77
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