Microsoft AI-900 中文 Q&A - in .pdf

  • AI-900 中文 pdf
  • Exam Code: AI-900 中文
  • Exam Name: Microsoft Azure AI Fundamentals (AI-900中文版)
  • Updated: Nov 08, 2026
  • Q & A: 336 Questions and Answers
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  • Exam Code: AI-900 中文
  • Exam Name: Microsoft Azure AI Fundamentals (AI-900中文版)
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  • Q & A: 336 Questions and Answers
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  • AI-900 中文 Testing Engine
  • Exam Code: AI-900 中文
  • Exam Name: Microsoft Azure AI Fundamentals (AI-900中文版)
  • Updated: Nov 08, 2026
  • Q & A: 336 Questions and Answers
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Exam Details

The Microsoft AI-900 exam is the only qualifying test that leads to the Microsoft Certified: Azure AI Fundamentals certification. It measures one’s knowledge of AI considerations & workloads, features of NLP workloads on Azure, and basic principles of ML on Azure. The candidates can choose to take this exam in English, French, Simplified Chinese, Japanese, German, Spanish, or Korean. It contains 40-60 questions covering different types, including case studies, drag and drop, active screen, build list, and multiple choice. You can schedule your test with Certiport or Pearson VUE. However, it is required to pay the fee of $99 before scheduling it.

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

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About Azure AI Fundamentals Certification and Who It Is Intended for

The Microsoft Certified: Azure AI Fundamentals qualification authenticates the learner's understanding and skills in AI & ML concepts along with associated Azure services. In particular, the individual with this certification is competent in demonstrating their understanding of various workloads related to those terms along with their implementation in Azure cloud computing services. In addition, this certification is an excellent way to get a step ahead in the preparation for other role-based Azure certificates as well as showcasing one's skill in the fundamentals of ML as well as AI. All in all, the Azure AI Fundamentals validation form Microsoft is for beginners and thus, doesn't require any technical knowledge and expertise like, for example, engineering for software. However, some understanding of programming will help you a lot to easily nail test AI-900.

Career Opportunities

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:

  • AI Engineer: $76k;
  • Developer: $50k;
  • Data Scientist: $67k.

Microsoft AI-900 中文 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Features of computer vision workloads on Azure15-20%- Computer vision solutions
  • 1. OCR and image analysis
    • 2. Image classification
      • 3. Object detection
        Topic 2: Features of natural language processing (NLP) workloads on Azure30-35%- Text analytics and language understanding
        • 1. Key phrase extraction
          • 2. Sentiment analysis
            • 3. Language modeling and translation
              Topic 3: Fundamentals of machine learning on Azure25-30%- Core machine learning concepts
              • 1. Training and validation concepts
                • 2. Supervised vs unsupervised learning
                  Topic 4: Describe AI workloads and considerations20-25%- Fundamentals of artificial intelligence concepts
                  • 1. Responsible AI principles
                    • 2. Common AI workloads

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

                      Question #1

                      您需要為客戶提供使用電話、社群媒體或數位助理查詢訂單狀態的能力。
                      你應該使用什麼?

                      • A. Azure 機器學習模型
                      • B. Azure Al Bot 服務
                      • C. Azure Al 翻譯服務
                      • D. Azure Al 文件智慧模型
                      Reveal Solution  Discussion  0

                      Correct Answer: B  🗳️

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

                      Question #2

                      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。

                      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", there are three main types of machine learning: supervised learning, unsupervised learning, and reinforcement learning. Within supervised learning, two common approaches are regression and classification, while clustering is a primary example of unsupervised learning.
                      * "You train a regression model by using unlabeled data." - No.Regression models are trained with labeled data, meaning the input data includes both features (independent variables) and target labels (dependent variables) representing continuous numerical values. Examples include predicting house prices or sales forecasts. Unlabeled data (data without target output values) cannot be used to train regression models; such data is used in unsupervised learning tasks like clustering.
                      * "The classification technique is used to predict sequential numerical data over time." - No.
                      Classification is used for categorical predictions, where outputs belong to discrete classes, such as spam
                      /not spam or disease present/absent. Predicting sequential numerical data over time refers to time series forecasting, which is typically a regression or forecasting problem, not classification. The AI-900 syllabus clearly separates classification (categorical prediction) from regression (continuous value prediction) and time series (temporal pattern analysis).
                      * "Grouping items by their common characteristics is an example of clustering." - Yes.This statement is correct. Clustering is an unsupervised learning technique used to group similar data points based on their features. The AI-900 study materials describe clustering as the process of "discovering natural groupings in data without predefined labels." Common examples include customer segmentation or document grouping.
                      Therefore, based on Microsoft's AI-900 training objectives and definitions:
                      * Regression # supervised learning using labeled continuous data (No)
                      * Classification # categorical prediction, not sequential numeric forecasting (No)
                      * Clustering # grouping by similarity (Yes)

                      Question #3

                      您應該使用什麼來識別一組圖像中的相似面孔?

                      • A. Azure Al 自訂視覺
                      • B. Azure 人工智慧視覺
                      • C. Azure OpenAI 服務
                      • D. Azure Al 語言
                      Reveal Solution  Discussion  0

                      Correct Answer: B  🗳️

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

                      Question #4

                      您需要追蹤使用 Azure 機器學習訓練的模型的多個版本。你該怎麼辦?

                      • A. 提供推理除塵器。
                      • B. 解釋模型。
                      • C. 註冊模型。
                      • D. 註冊訓練資料。
                      Reveal Solution  Discussion  0

                      Correct Answer: C  🗳️

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

                      Question #5

                      您需要根據照片中的街道標誌來識別街道名稱。
                      您應該使用哪種類型的電腦視覺?

                      • A. 臉部辨識
                      • B. 物體偵測
                      • C. 影像分類
                      • D. 光學字元辨識(OCR)
                      Reveal Solution  Discussion  0

                      Correct Answer: D  🗳️

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

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