Explanation:

For the vector portion, the correct choice is VECTOR_DISTANCE and order by distance ascending . The requirement is to build a combined weighted formula using the actual vector distance. Microsoft documents that VECTOR_DISTANCE returns the exact distance between two vectors. Since lower distance means greater similarity, ascending distance is the right direction for ranking. VECTOR_SEARCH is for ANN retrieval, but this hotspot specifically asks for a weighted formula based on distance , so VECTOR_DISTANCE is the appropriate operator.
For the keyword portion, the correct choice is CONTAINSTABLE on description and return ranked matches . Microsoft documents that CONTAINSTABLE returns a RANK column from 0 through 1000 , which is exactly what is needed for weighted scoring in a hybrid formula.
For the final ranking expression, the best choice is order by (distance * 0.6) + ((1.0 - RANK/1000.0) * 0.4) .
This works because vector distance is a lower-is-better metric, while full-text RANK is a higher-is-better metric. Dividing RANK by 1000 normalizes it to the documented range, and subtracting from 1.0 converts it into a lower-is-better term so both components can be combined consistently in one ascending score. This final step is a sound inference based on Microsoft's documented distance semantics and full-text rank range.