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.
Test your knowledge of Domain 2 with these practice questions covering regression, classification, clustering, deep learning, and Azure Machine Learning.
How to Use This Practice Set
Treat these questions as an active-recall checkpoint for Practice Questions: Machine Learning, not as a reading assignment. Answer the full set before looking at explanations, then mark each miss by skill area, rule, or service name. For every wrong answer, write the reason the correct option wins and why one tempting distractor fails. That habit matters because real exam questions often test the same concept with different wording. If you miss several questions from the same domain, pause and reread that chapter before continuing.
A strong final review loop is: timed attempt, explanation review, targeted reread, then a second attempt after a short break.
Review Routine
After you finish this set, make a three-column log: topic, missed rule, and the clue you should have noticed. Retake only missed questions the next day, then mix them with new questions so you do not memorize order. For calculations or scenario rules, say the first step aloud before choosing an option; that prevents rushing into a familiar but wrong answer.
Exam Tip: For each machine learning question, first identify whether the answer is a number, a category, or a discovered group; that usually narrows the model type immediately.
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?