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The individuals who passed the Microsoft AI-900 exam possess the foundational knowledge of artificial intelligence (AI) and machine learning (ML). The test is designed to evaluate the candidates’ understanding of basic AI and ML workloads as well as how to execute them on Microsoft Azure. The students can earn the Microsoft Certified: Azure AI Fundamentals certification at the end of their journey with this path.
Reference: https://docs.microsoft.com/en-us/learn/certifications/exams/ai-900
The Azure AI Fundamentals certification might not be a prerequisite for any other certification levels, however, it can still be used to prepare for multiple Azure-related qualifications including the Microsoft Certified: Azure Data Scientist Associate and the Microsoft Certified: Azure AI Engineer Associate.
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| Section | Weight | Objectives |
|---|---|---|
| Describe AI workloads and considerations | 20-25% | - Fundamentals of artificial intelligence concepts
|
| Features of computer vision workloads on Azure | 15-20% | - Computer vision solutions
|
| Features of natural language processing (NLP) workloads on Azure | 30-35% | - Text analytics and language understanding
|
| Fundamentals of machine learning on Azure | 25-30% | - Core machine learning concepts
|
選出正確完成句子的答案。
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", a regression model is a type of supervised machine learning model that is used to predict continuous numeric values based on one or more input variables (features).
In supervised learning, models are trained using labeled data, where each input record has a known target value (label). For regression specifically, the label represents a numeric quantity - such as price, age, temperature, or sales figures. The model learns to approximate a function that maps input variables to numeric outputs.
For example:
* Predicting house prices based on size, location, and number of rooms.
* Predicting monthly sales revenue from marketing spend and seasonality.
* Forecasting temperature based on historical weather data.
In Azure Machine Learning Designer and AutoML, when building a regression model, the label column must therefore be of numeric data type (integer or float). If the label were categorical (for example, "yes/no" or
"approved/denied"), the appropriate model type would be classification, not regression.
To contrast with other options:
* Boolean - used in binary classification problems (true/false outcomes).
* Datetime - used for time series forecasting, not standard regression labels.
* Text - used as input features for NLP models, not as regression targets.
Hence, when configuring a regression task in Azure, ensuring the target variable (label) is numeric is a fundamental requirement. The model's performance metrics-such as Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and R²-also rely on numeric computations.
說明用於訓練模型的資料來源是哪個負責任的 AI 原則的一個例子?
Correct Answer: C 🗳️
Explanation: Only visible for TroytecDumps members. You can sign-up / login (it's free).
哪個術語用於描述上傳自己的資料以自訂 Azure OpenAI 模型?
Correct Answer: B 🗳️
Explanation: Only visible for TroytecDumps members. You can sign-up / login (it's free).
將該工具與 Azure 機器學習任務相符。
要回答,請將適當的工具從左側的列拖曳到右側的任務中。每個工具可以使用一次、多次或完全不使用注意:每個正確的配對都值得一分。
Correct Answer:

Explanation:
The correct matching aligns directly with the Microsoft Azure AI Fundamentals (AI-900) official study guide and Microsoft Learn modules under "Identify features of Azure Machine Learning". Azure Machine Learning provides a suite of tools that serve different functions within the model development lifecycle - from creating workspaces, to training models, to automating experimentation.
* The Azure portal # Create a Machine Learning workspace.The Azure portal is a web-based graphical interface for managing all Azure resources. According to Microsoft Learn, you use the portal to create and configure the Azure Machine Learning workspace, which acts as the central environment where datasets, experiments, models, and compute resources are organized. Creating a workspace through the portal involves specifying a subscription, resource group, and region - tasks that are part of the setup stage rather than model development.
* Machine Learning designer # Use a drag-and-drop interface used to train and deploy models.The Machine Learning designer (formerly "Azure ML Studio (classic)") provides a visual, no-code/low- code interface for building, training, and deploying machine learning pipelines. The designer uses a drag-and-drop workflow where users connect modules representing data transformations, model training, and evaluation. This tool is ideal for beginners and those who want to quickly experiment with machine learning concepts without writing code.
* Automated machine learning (Automated ML) # Use a wizard to select configurations for a machine learning run.Automated ML simplifies model creation by automatically selecting algorithms, hyperparameters, and data preprocessing options. Users interact through a guided wizard (within the Azure Machine Learning studio) that walks them through configuration steps such as selecting datasets, target columns, and performance metrics. The system then iteratively trains and evaluates multiple models to recommend the best-performing one.
Together, these tools streamline the machine learning workflow:
* Azure portal for setup and resource management,
* Machine Learning designer for visual model creation, and
* Automated ML for guided, automated model selection and tuning.
您應該使用哪種類型的機器學習來預測下個月將售出的禮品卡數量?
Correct Answer: B 🗳️
Explanation: Only visible for TroytecDumps members. You can sign-up / login (it's free).
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