Concept:
If \( \lambda \) is an eigenvalue of a matrix \(A\), then the eigenvalue of \(A^k\) is \( \lambda^k \).
This follows from the eigenvalue definition:
\[
A\mathbf{x} = \lambda \mathbf{x}
\]
Multiplying both sides by \(A\):
\[
A^2 \mathbf{x} = A(\lambda \mathbf{x}) = \lambda (A\mathbf{x}) = \lambda(\lambda \mathbf{x}) = \lambda^2 \mathbf{x}
\]
Thus, the eigenvalues of \(A^2\) are the squares of the eigenvalues of \(A\).
Step 1: Identify the eigenvalues of \(A\).
Given:
\[
\lambda_1 = 2, \qquad \lambda_2 = 3
\]
Step 2: Find eigenvalues of \(A^2\).
Square each eigenvalue:
\[
\lambda_1^2 = 2^2 = 4
\]
\[
\lambda_2^2 = 3^2 = 9
\]
Step 3: Write the eigenvalues of \(A^2\).
\[
\text{Eigenvalues of } A^2 = 4, 9
\]
\[
\therefore \text{The correct answer is } 4, 9.
\]