6.3 Practice Questions: Machine Learning
Key Takeaways
- These questions test core ML concepts: features/labels, training data splits, supervised vs. unsupervised learning, and Azure ML services.
- Know the evaluation metrics: R-squared for regression, precision/recall for classification, silhouette score for clustering.
- Understand when to use AutoML vs. Designer vs. Notebooks, and real-time vs. batch endpoints.
- The Transformer architecture and neural networks are tested conceptually — no math or implementation required.
- Classification vs. clustering is one of the most frequently tested distinctions on the AI-900.
Practice Questions: Machine Learning
Test your knowledge of Domain 2 with these practice questions covering regression, classification, clustering, deep learning, and Azure Machine Learning.
A model predicts insurance claim amounts based on age, accident history, and coverage type. The predicted output is a dollar amount. This is an example of:
A data analyst wants to build a machine learning model but has no coding experience and no ML expertise. They want the system to automatically find the best algorithm for their data. Which Azure ML feature should they use?
What is the purpose of validation data in the machine learning process?
A marketing team has customer purchase data but NO predefined customer categories. They want to discover natural groups of similar customers. Which technique should they use?
Which statement about deep learning is correct?
A model has 98% accuracy on training data but only 55% accuracy on test data. What is this called?
In a spam detection model, which metric should be prioritized if the most important goal is to avoid blocking legitimate emails?
An e-commerce company needs to score 2 million customer records overnight for churn prediction. Which Azure ML deployment type should they use?