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Azure AI Data and AI Models Flashcards
Microsoft Azure AI Fundamentals AI-900 Flashcards
| Front | Back |
| What are common evaluation metrics for Classification models | Accuracy precision recall and F1 score |
| What are common evaluation metrics for Regression models | Mean Absolute Error Mean Squared Error and R squared |
| What are the main steps in Data Preprocessing | Cleaning transformation splitting into training and test sets |
| What is a Pipeline in Azure Machine Learning | A series of steps to automate workflows for data preparation training and deployment |
| What is Batch Deployment | Deploying an AI model to process data in batches rather than real time |
| What is Data Cleaning | Removing or correcting inaccurate incomplete or irrelevant data |
| What is Data Preprocessing | The process of cleaning transforming and preparing raw data for use in AI models |
| What is Feature Engineering | The process of selecting creating or transforming variables to improve model performance |
| What is Feature Transformation | Altering data to improve its suitability for machine learning often including scaling or encoding |
| What is Hyperparameter Tuning | The process of optimizing settings for an AI model to improve its performance |
| What is Inference in AI | Using a trained model to make predictions or decisions based on new data |
| What is Model Overfitting | When a model learns the training data too well and performs poorly on unseen data |
| What is Model Underfitting | When a model is too simple and fails to capture patterns in the training data |
| What is Real-time Deployment | Deploying an AI model to provide immediate predictions or decisions as data is received |
| What is Responsible AI in Azure | Ensuring AI models are fair explainable and ethically deployed |
| What is the difference between Training and Inference | Training builds the model while inference uses the model to make predictions |
| What is the purpose of Cross-validation | To ensure the model generalizes well by testing it on different subsets of the training data |
| What is Training in AI | The process of feeding data into a machine learning algorithm to create a model |
| Which Azure service supports model training and deployment | Azure Machine Learning |
| Why is Splitting Data important | To separate data into training and testing sets ensuring model evaluation is unbiased |
Learn key concepts of Azure AI with flashcards focused on data preprocessing, model training, deployment, and evaluation. Understand terms like feature transformation, hyperparameter tuning, pipelines, and Responsible AI. Covers essential AI practices such as cleaning data, feature engineering, splitting datasets, and avoiding overfitting. Perfect for mastering Azure Machine Learning concepts and building robust AI solutions.