Step 1: Recall the condition for an orthogonal matrix.
A square matrix \(A\) is orthogonal if
\[
AA^T=I
\]
where \(I\) is the identity matrix.
For an orthogonal matrix, we have
\[
A^{-1}=A^T
\]
Step 2: Write the matrix without the scalar factor.
Let
\[
B=
\begin{bmatrix}
3 & -2 & 6 \\
-6 & -3 & 2 \\
-2 & 6 & 3
\end{bmatrix}
\]
Then,
\[
A=\frac{1}{7}B
\]
Step 3: Verify orthogonality by checking row vectors.
The rows of \(B\) are
\[
R_1=(3,-2,6)
\]
\[
R_2=(-6,-3,2)
\]
\[
R_3=(-2,6,3)
\]
Now compute their dot products.
\[
R_1\cdot R_2
=
3(-6)+(-2)(-3)+6(2)
\]
\[
=-18+6+12=0
\]
\[
R_1\cdot R_3
=
3(-2)+(-2)(6)+6(3)
\]
\[
=-6-12+18=0
\]
\[
R_2\cdot R_3
=
(-6)(-2)+(-3)(6)+2(3)
\]
\[
=12-18+6=0
\]
Thus, all rows are mutually orthogonal.
Now find the length of each row:
\[
|R_1|^2=3^2+(-2)^2+6^2
\]
\[
=9+4+36=49
\]
Similarly,
\[
|R_2|^2=49
\]
and
\[
|R_3|^2=49
\]
Hence, after multiplying by \(\frac{1}{7}\), each row becomes a unit vector.
Therefore, the rows of \(A\) are orthonormal.
Step 4: Conclude the nature of the matrix.
Since the rows are orthonormal,
\[
AA^T=I
\]
Hence, \(A\) is an orthogonal matrix.
Therefore,
\[
A^{-1}=A^T
\]
Step 5: Final conclusion.
Hence,
\[
\boxed{A^{-1}=A^T}
\]