Type I = false positive (reject true null); Type II = false negative (fail to reject false null).
How to answer it
Type I is a false positive: you reject the null when it is true, and ship a change that does nothing. Its rate is alpha, the significance level you chose. Type II is a false negative: you fail to reject the null when the effect is real, and shelve something that worked. Its rate is beta, and power is 1 minus beta.
The memorable mapping: alpha is how often you cry wolf; beta is how often you miss the wolf.
The point interviewers want next is that the two trade off through sample size. For a fixed n, lowering alpha raises beta. The only way to lower both is more data. So the question "which error is worse here?" is a real design decision:
A/B test on a checkout change: a false positive costs a rollout of nothing; a false negative costs a missed lift. Both are moderate, so alpha 0.05 and power 0.8 is the convention.
A medical screening test: a false negative is a missed disease, so you accept more false positives and confirm them with a second test.
A fraud model that blocks payments: a false positive blocks a real customer, so precision is protected and some fraud is tolerated.
What they are checking: whether you can say which error matters more in a given setting, not just the definitions.
Common mistake: getting them backwards under pressure. Anchor on "Type I = reject a true null = false alarm" and derive the other.