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 |
About the Flashcards
Flashcards for the Microsoft Azure AI Fundamentals exam guide you through the full machine-learning lifecycle, from raw data to deployed model. You'll review essential terminology such as data preprocessing steps-cleaning, transformation, splitting-and important concepts like feature engineering, cross-validation, and hyperparameter tuning. Each card delivers a concise question-and-answer format that makes last-minute revision efficient.
The deck also covers model training versus inference, evaluation metrics for classification and regression, and the pitfalls of overfitting or underfitting. Specific Azure Machine Learning topics-pipelines, deployment options, and responsible AI-help connect theory to real certification tasks, ensuring confident exam readiness.
Topics covered in this flashcard deck:
- Data preprocessing steps
- Model training & inference
- Evaluation metrics
- Hyperparameter tuning
- Azure ML pipelines
- Deployment & responsible AI