Machine Learning Basics Quiz

Answers and explanations appear as you take the quiz.

1. A store wants to estimate next week’s sales as a dollar amount from past sales and calendar features. Which task is this?
   • Regression
   • Classification
   • Clustering
   • Text generation
2. A support team assigns each incoming message to one of five known issue labels. Which task matches that goal?
   • Clustering
   • Classification
   • Regression
   • Dimensionality reduction
3. An analyst groups unlabeled news articles by similarity without predefined topics. Which task is being used?
   • Classification
   • Regression
   • Clustering
   • Forecasting
4. A model scores 98% on training records but performs poorly on new records. What is the main concern?
   • It has too few input columns by definition
   • It has proved that the labels are correct
   • It is necessarily a clustering model
   • It may fit the training examples well without generalizing to new inputs
5. Why should a team keep a final test set out of repeated model tuning?
   • Repeated tuning can adapt decisions to the test set, weakening it as an independent check
   • The test set is only for fitting model parameters
   • A test set prevents selecting a metric
   • The training set must always be smaller
6. A forecasting team compares a complex model with “use yesterday’s value” before adopting it. What purpose does that simple rule serve?
   • A substitute for monitoring after deployment
   • A baseline showing whether added complexity provides useful information
   • A way to relabel regression as classification
   • A guarantee that the complex model is fair
7. A deployed model suddenly receives temperatures in Fahrenheit after training on Celsius. Which workflow lesson applies?
   • Strong test accuracy makes input units irrelevant
   • Only the model architecture can cause failures
   • Errors can arise from production preparation or changed input conventions
   • Deployment removes the need to check inputs
8. A classifier has strong average accuracy but misses nearly every rare emergency case. What should the team do?
   • Accept it because average accuracy is high
   • Tune on the final test set until the score improves
   • Remove the rare cases from evaluation
   • Inspect failure cases and choose metrics that reflect the consequences of mistakes