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Explain Conversational Artificial Intelligence Workloads Features on Azure (15-20%)
In the last section, you will face with the following subtopics:
- Identify the basic use cases for Conversational Artificial Intelligence (AI) – The students should be able to identify the features and usage for a range of elements. These include personal digital assistants, webchat bots, and telephone voice menus. It also covers the skills in identifying the basic features of conversational Artificial Intelligence solutions.
- Establish Azure services for Conversational Artificial Intelligence – The potential candidates for the Microsoft AI-900 exam should have the capacity to identify the capabilities of Azure Bot Service and QnA Maker service.
Reference: https://docs.microsoft.com/en-us/learn/certifications/exams/ai-900
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Microsoft AI-900 中文 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Features of generative AI workloads on Azure | 20–25% | - Describe generative AI concepts - Describe use cases for generative AI - Describe responsible AI practices for generative AI - Describe capabilities of Azure OpenAI Service |
| Features of computer vision workloads on Azure | 15–20% | - Describe capabilities of Azure Custom Vision - Describe capabilities of Azure Form Recognizer - Describe capabilities of Azure Computer Vision - Identify types of computer vision solutions - Describe capabilities of Azure Face |
| Features of Natural Language Processing (NLP) workloads on Azure | 15–20% | - Identify types of NLP solutions - Describe capabilities of Azure Language - Describe capabilities of Azure Speech - Describe capabilities of Azure Translator |
| Artificial Intelligence workloads and considerations | 15–20% | - Describe responsible AI principles - Identify types of AI workloads - Describe considerations for developing AI solutions |
| Fundamental principles of machine learning on Azure | 15–20% | - Describe core concepts of machine learning - Describe automated machine learning - Describe machine learning pipelines - Describe capabilities of Azure Machine Learning |
Microsoft Azure AI Fundamentals (AI-900中文版) Sample Questions:
對於以下每個陳述，如果該陳述為真，請選擇「是」。否則，選擇“否”。 注意：每個正確的選擇都值得一分。
Correct Answer:
Explanation:
The Translator service, part of Microsoft Azure Cognitive Services, is designed specifically for text translation between multiple languages. It is a cloud-based neural machine translation service that supports more than 100 languages. According to Microsoft Learn's module "Translate text with the Translator service", this service provides two main capabilities: text translation and automatic language detection.
* "You can use the Translator service to translate text between languages." # YesThis statement is true.
The primary purpose of the Translator service is to translate text accurately and efficiently between supported languages, such as English to Spanish or French to Japanese. It maintains contextual meaning using neural machine translation models.
* "You can use the Translator service to detect the language of a given text." # YesThis statement is also true. The Translator service includes automatic language detection, which determines the source language before translation. For instance, if a user submits text in an unknown language, the service can identify it automatically before performing translation.
* "You can use the Translator service to transcribe audible speech into text." # NoThis statement is false.
Transcribing speech (audio) into text is a function of the Azure Speech service, specifically the Speech- to-Text API, not the Translator service.
Therefore, the Translator service is used for text translation and language detection, while speech transcription belongs to the Speech service.
選出正確完成句子的答案。
Correct Answer:
Explanation:
"When evaluating the performance of a model, the confusion matrix displays the predicted and actual positives and negatives by using a grid of 0 and 1 values." According to the Microsoft Azure AI Fundamentals (AI-900) official study materials and Microsoft Learn module "Identify features of common machine learning types", a confusion matrix is a tool used to evaluate the performance of classification models. It visually summarizes how many predictions were correctly or incorrectly classified by comparing the predicted labels to the actual (true) labels.
A confusion matrix is a table, typically 2×2 for binary classification, with the following components:
* True Positives (TP): The model correctly predicted the positive class.
* True Negatives (TN): The model correctly predicted the negative class.
* False Positives (FP): The model incorrectly predicted the positive class.
* False Negatives (FN): The model incorrectly predicted the negative class.
The confusion matrix allows data scientists and analysts to derive important performance metrics such as accuracy, precision, recall, and F1-score, which together provide a more complete understanding of how well a model performs beyond a single number.
In Microsoft Learn's AI-900 curriculum, the confusion matrix is highlighted as a key visualization tool that
"compares actual values to predicted values to evaluate classification performance." The grid format (using 0s and 1s for predicted classes) helps identify where misclassifications occur.
By contrast:
* AUC metric (Area Under Curve) and ROC curve evaluate model discrimination ability.
* Threshold defines decision cutoffs but doesn't display classifications.
Therefore, based on the official Microsoft AI-900 study guide and Microsoft Learn resources, the correct answer is Confusion Matrix, as it provides a grid view comparing actual versus predicted values in classification models.
您有一個自然語言處理 (NIP) 模型，該模型是使用未經許可獲得的資料建立的。
這違反了微軟的哪一項負責任人工智慧原則？
- A. 隱私權和安全
- B. 包容性
- C. 可靠性與安全性
- D. 透明度
Correct Answer: A 🗳️
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向 Azure OpenAI 的 REST API 端點傳送請求時，應該使用哪種格式？
- A. XML
- B. JSON
- C. CSV
- D. YAML
Correct Answer: B 🗳️
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選出正確完成句子的答案。
Correct Answer:
Explanation:
Privacy and security.
According to Microsoft's Responsible AI Principles, implementing filters to block harmful or inappropriate content in a Generative AI chat solution demonstrates a commitment to the Privacy and Security principle.
This principle ensures that AI systems are designed and operated in a way that protects users, their data, and society from harm.
When a chat system uses Generative AI models (like Azure OpenAI's GPT-based services), there is a risk that the model might produce unsafe, offensive, or sensitive content. Microsoft addresses this through content filters and safety systems, which automatically detect and block violent, hate-based, or sexually explicit outputs. This is part of responsible deployment practices to ensure that user interactions remain safe, private, and compliant with ethical standards.
Implementing these filters aligns with the Privacy and Security principle because it:
* Protects users from exposure to harmful or abusive content.
* Ensures that conversations are safeguarded against malicious or unsafe use.
* Upholds user trust by maintaining a safe digital environment for all participants.
Let's briefly clarify why the other options are incorrect:
* Fairness deals with ensuring unbiased treatment and equitable outcomes in AI decisions.
* Transparency focuses on explaining how AI systems make decisions.
* Accountability refers to human oversight and responsibility for AI actions.
Thus, content filtering mechanisms are explicitly an example of Privacy and Security, as they protect users and data from harm or misuse while maintaining ethical AI behavior.
Therefore, the verified correct answer is Privacy and security.
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