Guide-linked quiz · Medium Level
Unit Testing Machine Learning Code Quiz
Choose appropriate tests for transforms, losses, tensor contracts and tiny overfit behavior without confusing code checks with generalization.
Question 1 of 8
Which assertions are appropriate for a preprocessing transformer?
Preview every question8 questions
Answers and explanations appear as you take the quiz.
- Which assertions are appropriate for a preprocessing transformer?
- Expected output shape, column order and known transformed values
- Held-out accuracy on a deployment-like population
- Recall across rare production subgroups
- End-to-end latency under peak service load
- Why compare a loss implementation with a hand calculation?
- It proves the model will generalize
- It can catch formula or reduction errors on a known example
- It replaces training data quality checks
- It guarantees the optimizer converges
- What does a tiny-overfit test help detect?
- Fairness across all subgroups
- Reliable performance on unseen data
- Potential training-path or gradient bugs
- Production latency under load
- How should tests compare floating-point outputs?
- Skip checks for all model outputs
- Require exact bitwise equality on every device
- Round every value to an integer
- Use explicit tolerances appropriate to numerical precision
- Which check belongs in a model wrapper interface test?
- A statistical calibration curve on a held-out set
- One prediction per input row and clear rejection of malformed features
- Subgroup performance estimates with confidence intervals
- Training loss after a full retraining job
- Why separate unit tests from model-quality evaluation?
- Quality metrics test only syntax
- Unit tests cannot execute Python functions
- Code correctness does not establish statistical usefulness
- Model validation needs no data
- What can a finite-gradient assertion establish?
- The model is calibrated
- The gradients are mathematically optimal
- The model has no bias
- A basic gradient path exists for expected parameters
- Where should slow full-dataset training usually be placed?
- In a separate integration or training validation stage
- In every tiny unit test
- Inside a feature-name assertion
- In a package lockfile
Keep testing yourself
More quizzes picked for your level.