Machine Learning Flashcards
7 cards from real Microsoft Azure AI Fundamentals practice questions. Tap to flip, then mark Knew It or Still Learning — missed cards come back until you master them.
Read the first 7 Machine Learning flashcards as text
Which Azure ML feature automatically selects the best algorithm and hyperparameters for a given dataset?
Answer: Automated ML (AutoML)
Automated ML (AutoML) in Azure Machine Learning iterates over multiple algorithms and hyperparameter settings to automatically find the best model for your data.
In supervised learning, what is the term for the variable the model is trained to predict?
Answer: Label
In supervised learning, the label (also called the target) is the known value the model learns to predict from input features.
Which type of machine learning is used when training data has no predefined correct answers?
Answer: Unsupervised learning
Unsupervised learning finds patterns in data without labeled examples, such as grouping customers by purchasing behavior using clustering.
What metric would you primarily use to evaluate a binary classification model's ability to avoid false positives?
Answer: Precision
Precision measures the proportion of positive predictions that are actually correct, directly reflecting the model's ability to minimize false positives.
In Azure Machine Learning, what is an 'endpoint' used for after model training?
Answer: Deploying the model for real-time or batch inference
An endpoint in Azure ML exposes a trained model as a web service so applications can send data and receive predictions in real time or via batch.
Which Azure service is purpose-built for large-scale machine learning experimentation and MLOps?
Answer: Azure Machine Learning
Azure Machine Learning is Microsoft's end-to-end platform for building, training, deploying, and managing ML models at scale with MLOps capabilities.
What is 'overfitting' in a machine learning model?
Answer: The model learns the training data too well and performs poorly on new data
Overfitting occurs when a model memorizes training data noise and specifics, causing high accuracy on training data but poor generalization to unseen data.