Behavioral Testing of ML Models Quiz

Answers and explanations appear as you take the quiz.

1. A punctuation change should preserve sentence meaning. Which test type checks output stability?
   • Invariance testing
   • Directional expectation testing
   • Minimum functionality testing
   • A random train-test split
2. A model should respond in a specified direction when one validated input increases. Which test type fits?
   • Invariance
   • Directional expectation
   • Minimum functionality
   • Serialization testing
3. A test checks whether the model can preserve negation in a straightforward translation example, without comparing transformed input pairs. Which category fits?
   • Performance latency
   • Invariance testing
   • Minimum functionality testing
   • Data drift monitoring
4. Why must a team review the expected behavior before encoding it as a test?
   • Review makes model outputs deterministic
   • Any expected behavior automatically proves fairness
   • The test suite creates ground-truth labels
   • The transformation or expectation may not be valid for every context
5. What does passing a small behavioral suite establish?
   • The model is robust to every distribution shift
   • The model passed those specified cases, not all production conditions
   • The model is calibrated across all groups
   • The test set is representative by definition
6. How does metamorphic testing relate to behavioral tests?
   • It requires a correct reference label for every transformed input
   • It checks only exact equality across all transformations
   • It checks expected relations between outputs when exact answers are unavailable
   • It applies only to image models and not other input types
7. Why can a brittle invariance test be inappropriate?
   • The model must be retrained for each sentence
   • Invariance tests cannot compare outputs
   • All model probabilities are fixed
   • The transformation may change meaning for some examples
8. Which evaluation should behavioral tests complement?
   • Representative held-out metrics and other model checks
   • Only the training-set score
   • No other evaluation is needed
   • A package installation checklist only