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AIF-C01 Practice Question: Which TWO statements about the bias-variance…
Which TWO statements about the bias-variance tradeoff are correct? (Choose TWO.)
⚠ Common exam trap
The trap is mixing up the symptoms: candidates often think high bias causes overfitting (it's the opposite) or that more complexity reduces variance (it increases it).
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
High bias typically leads to underfitting
Option C is correct because high bias means the model makes overly simplistic assumptions about the data, so it fails to capture the underlying pattern and systematically underfits both training and test data. Option E is correct because high variance means the model is overly sensitive to the particular training samples, fitting noise as well as signal, which produces excellent training performance but poor generalization—the hallmark of overfitting. Option A is wrong because high bias leads to underfitting, not overfitting; overfitting is associated with high variance. Option B is wrong because high variance models are highly sensitive to changes in training data, not insensitive. Option D is wrong because increasing model complexity typically increases variance (and decreases bias), not decreases variance.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
High bias typically leads to overfitting
Why it's wrong here
High bias means the model is too rigid to capture the underlying pattern, producing underfitting with poor accuracy on both training and test data. Overfitting arises from high variance, where the model memorises training noise. High bias is the correct diagnosis when a linear model is fitted to clearly non-linear data.
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High variance models are insensitive to changes in training data
Why it's wrong here
High variance means the model fits training noise, so small changes in the training set alter the learned parameters and predictions substantially. Insensitivity to training data changes is characteristic of high bias, where the model ignores the data's structure, as with a linear fit to curved data.
- ✓
High bias typically leads to underfitting
Why this is correct
High bias means the model's assumptions are too rigid for the underlying data, so it cannot capture the true relationship. Consequently it performs poorly on both training and test data — the defining symptom of underfitting.
- ✗
Increasing model complexity typically decreases variance
Why it's wrong here
Raising model complexity increases variance, because the model has more parameters to fit training noise, so predictions shift sharply with different samples. Variance falls when complexity is reduced, for example constraining a deep tree with pruning or limiting polynomial degree.
- ✓
High variance typically leads to overfitting
Why this is correct
High variance means the model is excessively sensitive to the particular training samples, fitting noise rather than signal. It therefore performs well on training data but poorly on unseen test data — the defining symptom of overfitting.
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Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Amazon Web Services exam blueprint
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