Let the parametric equations of the two lines be:
\(\vec{r}_1 = (\lambda \hat{i} + 4 \hat{j} + 3 \hat{k}) + \alpha (2 \hat{i} + 3 \hat{j} + 4 \hat{k})\),
\(\vec{r}_2 = (2 \hat{i} + 6 \hat{j} + 7 \hat{k}) + \beta (2 \hat{i} + 3 \hat{j} + 4 \hat{k})\).
Here, the direction vector for both lines is:
\(\vec{b} = 2 \hat{i} + 3 \hat{j} + 4 \hat{k}\),
and the position vectors of points on the lines are:
\(\vec{a}_1 = \lambda \hat{i} + 4 \hat{j} + 3 \hat{k}\), \(\vec{a}_2 = 2 \hat{i} + 6 \hat{j} + 7 \hat{k}\).
The formula for the shortest distance between two skew lines is:
\(\text{Shortest distance} = \frac{|\vec{b} \times (\vec{a}_2 - \vec{a}_1) \cdot \vec{b}|}{|\vec{b}|} = \frac{13}{\sqrt{29}}\).
Substituting \(\vec{a}_2 - \vec{a}_1\):
\(\vec{a}_2 - \vec{a}_1 = (2 - \lambda) \hat{i} + 2 \hat{j} + 4 \hat{k}\).
The cross product \(\vec{b} \times (\vec{a}_2 - \vec{a}_1)\) simplifies as follows:
\(\vec{b} \times (\vec{a}_2 - \vec{a}_1) = \begin{vmatrix} \hat{i} & \hat{j} & \hat{k} \\ 2 & 3 & 4 \\ 2 - \lambda & 2 & 4 \end{vmatrix}\).
Solving this determinant gives:
\(\vec{b} \times (\vec{a}_2 - \vec{a}_1) = (-8 \hat{j} + 12 \hat{i} + 4 (2 - \lambda) \hat{j})\).
Taking the magnitude and using the formula for shortest distance:
\(\text{Shortest distance} = \frac{|(-8 \hat{j} + 12 \hat{i})|}{13}\).
Finally solving the quadratic for \(\lambda = 1\).