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As you remember, the Microsoft Certified: Azure AI Fundamentals validation is the fundamental certification designed for beginners with both technical and non-technical backgrounds. It is considered a good path to follow if you're interested in starting a career in the field of AI and ML. Below are some of the jobs that you can get your hand on through this certification. These jobs come with decent annual salaries as covered by Payscale.com:
- Developer: $50k;
- Data Scientist: $67k.
- AI Engineer: $76k;
Exam Topics
The potential candidates for this certification test must develop their skills in its topics before attempting it. It is recommended to go through the official webpage to review the detailed objectives. Microsoft updates the topics of its exams regularly, so studying the latest domains is very important to your success in the test. Thus, Microsoft AI-900 covers the following sections:
Describe AI Considerations and Workloads (15-20%)
The first topic evaluates the following skills:
- Identify the guiding principles associated with responsible Artificial Intelligence – This one will evaluate the candidates’ ability to explain some concepts. These include considerations of safety and reliability in AI solutions and considerations for fairness in AI solutions. It also covers considerations for security and privacy, transparency, inclusiveness, and accountability within an AI solution.
- Identify the common AI Workloads features – You should be able to identify a variety of workloads, such as forecasting/prediction, computer vision, and conversational AI. The subsection also covers the characteristics of anomaly detection workloads, knowledge mining, and natural language processing workloads.
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Prove that you can describe the following: AI workloads and considerations; fundamental principles of machine learning on Azure; features of computer vision workloads on Azure; features of Natural Language Processing (NLP) workloads on Azure; and features of conversational AI workloads on Azure.
Reference: https://docs.microsoft.com/en-us/learn/certifications/exams/ai-900
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.
Microsoft AI-900 中文 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Artificial Intelligence workloads and considerations | 15–20% | - Describe considerations for developing AI solutions - Identify types of AI workloads - Describe responsible AI principles |
| Topic 2: Features of computer vision workloads on Azure | 15–20% | - Identify types of computer vision solutions - Describe capabilities of Azure Custom Vision - Describe capabilities of Azure Form Recognizer - Describe capabilities of Azure Computer Vision - Describe capabilities of Azure Face |
| Topic 3: Fundamental principles of machine learning on Azure | 15–20% | - Describe capabilities of Azure Machine Learning - Describe machine learning pipelines - Describe core concepts of machine learning - Describe automated machine learning |
| Topic 4: Features of Natural Language Processing (NLP) workloads on Azure | 15–20% | - Describe capabilities of Azure Speech - Identify types of NLP solutions - Describe capabilities of Azure Language - Describe capabilities of Azure Translator |
| Topic 5: Features of generative AI workloads on Azure | 20–25% | - Describe capabilities of Azure OpenAI Service - Describe generative AI concepts - Describe use cases for generative AI - Describe responsible AI practices for generative AI |
Microsoft Azure AI Fundamentals (AI-900中文版) Sample Questions:
將 Azure Al 服務與適當的操作相符。
若要回答，請將對應的服務從左側的列拖曳到右側的操作。
注意：每場正確的比賽都值得一分。
Correct Answer:
Explanation:
The correct mapping is based on how each Azure Cognitive Service functions within the Microsoft AI ecosystem, as detailed in the Microsoft Azure AI Fundamentals (AI-900) study guide and Microsoft Learn Cognitive Services documentation.
* Convert spoken requests into text # Azure AI SpeechThe Azure AI Speech service provides speech-to- text (STT) capabilities, which enable an application to recognize spoken language and convert it into written text. This functionality is foundational in voice-enabled applications like digital assistants or transcription services. When a user speaks, this service captures the audio signal and produces an accurate textual representation that can then be processed by other AI services.
* Identify the intent of a user's requests # Azure AI LanguageThe Azure AI Language service (which includes Conversational Language Understanding, formerly LUIS) is designed to extract meaning from text. It identifies intents-the goals or actions a user wants to perform-and entities, which are key details within that request. For example, in the command "Book a flight to Paris," the intent is "book a flight," and the entity is "Paris."
* Apply intent to entities and utterances # Azure AI LanguageAgain, the Language service performs this deeper contextual analysis. It not only identifies what the user wants (intent) but also applies it to utterances (specific user expressions) and entities (data elements extracted from text). This helps conversational AI systems take meaningful actions, such as fulfilling user requests.
In summary, Azure AI Speech handles audio-to-text conversion, while Azure AI Language performs natural language understanding, mapping intents and entities-a workflow essential in intelligent conversational applications.
選出正確完成句子的答案。
Correct Answer:
Explanation:
Safety system.
According to the Microsoft Learn documentation and the AI-900: Microsoft Azure AI Fundamentals official study guide, the safety system layer in generative AI architecture plays a crucial role in monitoring, filtering, and mitigating harmful or unsafe model outputs. This layer works alongside the model and user experience layers to ensure that generative AI systems-such as those powered by Azure OpenAI-produce responses that are safe, aligned, and responsible.
The safety system layer uses various techniques including content filtering, prompt moderation, and policy enforcement to prevent outputs that could be harmful, biased, misleading, or inappropriate. It evaluates both user inputs (prompts) and model-generated outputs to identify and block unsafe or unethical content. The system might use predefined rules, classifiers, or human feedback signals to decide whether to allow, modify, or stop a response.
In contrast, the other layers serve different purposes:
* The model layer contains the core large language or generative model (e.g., GPT or DALL-E) that processes inputs and produces outputs.
* The metaprompt and grounding layer ensures the model's responses are contextually relevant and factually supported, often linking to organizational data sources or system prompts.
* The user experience layer defines how users interact with the AI system, including the interface and conversational flow, but does not manage safety enforcement.
Therefore, the layer that uses system inputs and context to mitigate harmful outputs from a generative AI model is the Safety system layer.
This aligns with Microsoft's responsible AI principles-Fairness, Reliability and Safety, Privacy and Security, Inclusiveness, Transparency, and Accountability-ensuring generative AI operates ethically and safely.
您應該使用哪種類型的機器學習來識別具有相似購買習慣的人群？
- A. 迴歸
- B. 分類
- C. 聚類
Correct Answer: C 🗳️
Explanation: Only visible for Prep4cram members. You can sign-up / login (it's free).
將臉部辨識任務與適當的問題相匹配。
要回答，請將相應的任務從左側的列拖曳到右側的問題。每個任務可以使用一次、多次或完全不使用。
注意：每個正確的選擇都值得一分。
Correct Answer:
Explanation:
The correct matches are based on the Microsoft Azure AI Fundamentals (AI-900) Official Study Guide and the Microsoft Learn module "Explore computer vision in Microsoft Azure." These materials explain that facial recognition tasks can be categorized into four major operations: verification, identification, similarity, and grouping. Each task serves a distinct purpose in facial recognition scenarios.
* Verification - "Do two images of a face belong to the same person?"The verification task determines whether two facial images represent the same individual. Azure Face API compares the facial features and returns a confidence score indicating the likelihood that the two faces belong to the same person.
* Similarity - "Does this person look like other people?"The similarity task compares a face against a collection of faces to find visually similar individuals. It does not confirm identity but measures how closely two or more faces resemble each other.
* Grouping - "Do all the faces belong together?"Grouping organizes a set of unknown faces into clusters based on similar facial features. This is used when identities are not known beforehand, helping discover potential duplicates or visually similar clusters within an image dataset.
* Identification - "Who is this person in this group of people?"The identification task is used when the system tries to determine who a specific person is by comparing their face against a known collection (face database or gallery). It returns the identity that best matches the input face.
According to Microsoft's AI-900 training, these tasks form the basis of Azure Face API's capabilities. Each helps solve a different type of facial recognition problem-from matching pairs to discovering unknown identities-making them essential components of responsible AI-based vision systems.
生成式人工智慧解決方案的兩個常見用例是什麼？每個正確答案都提供了一個完整的解決方案。
注意：每個正確選擇值一分。
- A. 根據文字描述創作原創藝術品
- B. 根據歷史資料預測銷售收入
- C. 為客戶服務代理程式產生草稿回复
- D. 將電子郵件分類為垃圾郵件或非垃圾郵件
Correct Answer: A,C 🗳️
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