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Microsoft AI-900 中文 exam : Microsoft Azure AI Fundamentals (AI-900中文版)

AI-900 中文 Exam Simulator
  • Exam Code: AI-900-CN
  • Exam Name: Microsoft Azure AI Fundamentals (AI-900中文版)
  • Updated: Oct 23, 2026
  • Q & A: 336 Questions and Answers
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Important facts you need to know about AI-900: Microsoft Azure AI Fundamentals Exam

Preparing for the exam will help you screen out questions that are irrelevant for this certification. Ingestion of the data is the most important role in AI. Ready to use the data as soon as possible. Anomaly detection refers to all situations where something out of the ordinary is happening. Accountability models are used for the accuracy rate. Transparency refers to using the data for this service. Interoperability is used on the platform. Microsoft AI-900 exam dumps in order to get the best scores with the Microsoft Azure AI Fundamentals Exam. Tech terms used in AZ-900:Microsoft Azure AI Fundamentals Exam. Serviceidentify is used in the process of AI. Files are used to store the data. The AI application is the product of the AI. Learning is used for this purpose to provide better accuracy rate.

Concepts of the AI are explained in the Microsoft AI-900 exam. Image classification is used as the labeling. Recommendation engine is used as the indexing. Intelligent chatbots are used as the chatbot. Community engagement is the chatbot. Custom bot is used as the chatbot. Extracts are used for this purpose. Brainpool is the tool used to perform the extraction. Word embedding is used as the vector training. Dimensionality reduction is used for this purpose. Intelligent chatbot are used as the chatbot.

Reference: https://docs.microsoft.com/en-us/learn/certifications/exams/ai-900

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Microsoft AI-900 中文 Exam Syllabus Topics:

SectionWeightObjectives
Describe AI workloads and considerations20-25%- Fundamentals of artificial intelligence concepts
  • 1. Common AI workloads
    • 2. Responsible AI principles
      Features of natural language processing (NLP) workloads on Azure30-35%- Text analytics and language understanding
      • 1. Language modeling and translation
        • 2. Key phrase extraction
          • 3. Sentiment analysis
            Features of computer vision workloads on Azure15-20%- Computer vision solutions
            • 1. Image classification
              • 2. OCR and image analysis
                • 3. Object detection
                  Fundamentals of machine learning on Azure25-30%- Core machine learning concepts
                  • 1. Supervised vs unsupervised learning
                    • 2. Training and validation concepts

                      Microsoft Azure AI Fundamentals (AI-900中文版) Sample Questions:

                      Question #1

                      您有一個常見問題 (FAQ) PDF 檔案。
                      您需要建立一個基於常見問題解答的對話支援系統。
                      您應該使用哪種服務?

                      • A. 電腦視覺
                      • B. 語言理解 (LUIS)
                      • C. 文字分析
                      • D. QnA Maker
                      Reveal Solution  Discussion  0

                      Correct Answer: D  🗳️

                      Explanation: Only visible for ITExamSimulator members. You can sign-up / login (it's free).

                      Question #2

                      選出正確完成句子的答案。

                      Reveal Solution  Discussion  0

                      Correct Answer:


                      Explanation:

                      According to the Microsoft Azure AI Fundamentals (AI-900) official study materials and Microsoft Learn module "Describe features of common AI workloads," an anomaly detection workload is designed to identify data points or patterns that deviate significantly from what is expected or normal. These anomalies often indicate irregularities, faults, or potential issues that require attention.
                      In this scenario, the AI system monitors temperature data from a large machine. Normally, the machine operates within a predictable temperature range. When the AI detects sudden or unexpected temperature spikes or drops - behavior that does not match the historical pattern - it flags these occurrences as anomalies. This type of workload is fundamental in predictive maintenance and industrial monitoring, where it helps detect equipment failures, safety hazards, or energy inefficiencies before they escalate.
                      Microsoft's AI-900 curriculum emphasizes that anomaly detection workloads are often used in:
                      * Industrial IoT systems (detecting abnormal sensor readings or machine behavior)
                      * Finance (fraud detection or unusual transaction monitoring)
                      * Cybersecurity (detecting irregular network traffic or access patterns)
                      * Operations (identifying abnormal variations in production data)
                      The Azure service used for this purpose is Azure Anomaly Detector, part of Azure Cognitive Services, which uses advanced statistical and machine learning models to automatically detect outliers in time-series data such as temperature, pressure, or transaction logs.
                      By comparison:
                      * Computer vision handles image or video analysis.
                      * Knowledge mining extracts insights from large document collections.
                      * Natural Language Processing (NLP) interprets human language.
                      Thus, based on the official Microsoft AI-900 study guide and Microsoft Learn, the correct and verified answer is An anomaly detection workload, since detecting unusual temperature fluctuations precisely fits this AI workload type.

                      Question #3

                      您計劃使用機器學習設計器部署 Azure 機器學習模型 您應該依序執行哪四個動作?要回答,請將適當的操作從操作清單移至答案區域,並按正確的順序排列。

                      Reveal Solution  Discussion  0

                      Correct Answer:


                      Explanation:

                      According to the Microsoft Azure AI Fundamentals (AI-900) official study guide and the Microsoft Learn module "Identify features of common machine learning types", the standard workflow for creating and deploying a machine learning model - especially within Azure Machine Learning Designer - follows a structured sequence of steps to ensure that the model is trained effectively and evaluated correctly.
                      Here's the detailed breakdown of the correct order:
                      * Import and prepare a dataset:This is always the first step in the machine learning lifecycle. The dataset is imported into Azure Machine Learning and cleaned or preprocessed. Preparation might include handling missing values, normalizing data, removing outliers, and encoding categorical variables. This ensures the dataset is ready for modeling.
                      * Split the data randomly into training data and validation data:The dataset is then divided into two parts
                      - the training set and the validation (or testing) set. Typically, around 70-80% of the data is used for training and 20-30% for validation. This step ensures that the model can be evaluated on unseen data later, preventing overfitting.
                      * Train the model:During this stage, the machine learning algorithm learns patterns from the training data. Azure Machine Learning Designer provides multiple algorithms (classification, regression, clustering, etc.) that can be applied using "Train Model" components.
                      * Evaluate the model against the validation dataset:Finally, the trained model's performance is tested using the validation dataset. Evaluation metrics such as accuracy, precision, recall, or RMSE (depending on the model type) are calculated to assess how well the model generalizes to new data.
                      The incorrect option - "Evaluate the model against the original dataset" - is not used in proper ML workflows, because evaluating on the same data used for training would give misleadingly high accuracy due to overfitting.

                      Question #4

                      您可以使用 Azure 機器學習設計器建立模型管道。在運行管道之前您應該創建什麼?

                      • A. 計算資源
                      • B. 註冊模型
                      • C. Jupyter 筆記本
                      Reveal Solution  Discussion  0

                      Correct Answer: A  🗳️

                      Explanation: Only visible for ITExamSimulator members. You can sign-up / login (it's free).

                      Question #5

                      您需要透過實施 Chabot 以預定義答案回答簡單問題來減輕電話接線員的負擔。
                      您應該使用哪兩項人工智慧服務來實現目標?每個正確答案都代表了解決方案的一部分。
                      注意:每個正確的選擇都值得一分。

                      • A. Azure 機器學習
                      • B. Azure 8ol 服務
                      • C. 語言服務
                      • D. 翻譯者
                      Reveal Solution  Discussion  0

                      Correct Answer: B,C  🗳️

                      Explanation: Only visible for ITExamSimulator members. You can sign-up / login (it's free).

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