Answer:
A Type I error could occur if the test shows that the proportion is above 90%, while in reality, the actual proportion is 90%.
Step-by-step explanation:
Machines in a factory produce circular washers that meet a specific diameter.
The quality control manager routinely assesses samples of washers to ensure that more than 90% conform to the specified diameter.
Let p represent the proportion of washers that meet the specified diameter
Thus, Null hypothesis: p = 90% 
Alternative Hypothesis: p > 90%
Here, the null hypothesis posits that the proportion is exactly 90%. In contrast, the alternative hypothesis suggests that the proportion exceeds 90%.
Now, a Type I error indicates the probability of rejecting the null hypothesis while it is actually true or, in simple terms, the likelihood of incorrectly rejecting a valid hypothesis.
Thus, based on our question, a Type I error would declare that the test convincingly indicates the proportion is over 90%, while in truth, it remains 90%.