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 service provides pre-built ML models via REST APIs without requiring any model training?
Answer: Azure Cognitive Services
Azure Cognitive Services offers pre-trained AI models for vision, speech, language, and decision-making, accessible via simple REST API calls without custom training.
In the context of Azure ML, what is a 'model registry'?
Answer: A centralized repository for storing, versioning, and managing trained ML models
The Azure ML model registry is a centralized store that tracks all trained models with versioning, metadata, and lineage information for governance and deployment.
Which machine learning technique combines predictions from multiple models to improve overall accuracy?
Answer: Ensemble learning
Ensemble learning combines outputs from multiple models (e.g., bagging, boosting, stacking) to produce a stronger, more accurate prediction than any single model.
What is the primary purpose of normalization or standardization in machine learning data preparation?
Answer: Scaling features to a similar range so no single feature dominates training
Normalization and standardization scale numeric features to comparable ranges, preventing features with large values from disproportionately influencing model training.
In Azure AutoML for NLP tasks, which scenario type would you choose to assign a single category to each text document?
Answer: Multi-class text classification
Multi-class text classification assigns exactly one class label per document, whereas multi-label allows multiple labels; NER extracts entities rather than classifying documents.
Which metric best evaluates a classification model when the classes in the dataset are highly imbalanced?
Answer: F1 Score
F1 Score balances precision and recall, making it a better metric than accuracy when one class is much rarer, since accuracy can be misleadingly high by always predicting the majority class.
What does 'hyperparameter tuning' mean in Azure Machine Learning?
Answer: Searching for the optimal configuration settings that control the training process
Hyperparameter tuning (e.g., using Azure ML's HyperDrive) searches over settings like learning rate and tree depth that are set before training to find the combination that yields the best model.