Step 1: Understanding the Concept:
We want the probability that the student guessed, given that the answer is correct. This is a conditional probability that needs Bayes' theorem.
Step 2: Key Formula or Approach:
Let \(K\) be the event that the student knows the answer and \(G\) the event that the student guesses. Let \(C\) be the event of a correct answer.
\[ P(K) = 0.9,\quad P(G) = 0.1,\quad P(C|K) = 1,\quad P(C|G) = \tfrac14 \]
\[ P(G|C) = \frac{P(G)P(C|G)}{P(K)P(C|K) + P(G)P(C|G)} \]
Step 3: Detailed Explanation:
Numerator: \(0.1\times\tfrac14 = 0.025\).
Denominator: \(0.9\times1 + 0.025 = 0.925\).
\[ P(G|C) = \frac{0.025}{0.925} = \frac{25}{925} = \frac{1}{37} \]
Option (C) \(\tfrac{36}{37}\) is the probability that the student knew the answer given it is correct. Option (A) and (D) do not match the ratio 25/925.
Final Answer:
The probability that the student was guessing is \(\dfrac1{37}\), option (B).
\[ \boxed{\frac{1}{37} \text{ (B)}} \]