Module 5 practice · combine without overstating

Use the lesson, then compare the solutions.

A. Geometry with declared inputs. Use query vector (1,1) and document vectors A=(1,0), B=(0,1), C=(0,0). Calculate cosine similarity to each. Predict the order from fixed_vector_ranking when IDs are a,b,c. Why is this not evidence that a text encoder understands a question?

B. Rank fusion. For lists [a,b] and [c,a], use constant 10 and compute each RRF score. Which document wins? What happens to a relevant document d that appears in neither list? Verify using reciprocal_rank_fusion.

C. Constraint boundaries. A passage contains Books may be borrowed. Predict what the explicit required term filters books, book and laptop do. Explain two ways a manual exact-term filter can reject a useful passage. Is this implementation a BERT reranker?

D. Write an honest comparison proposal. Describe a future lexical-versus-dense experiment in six sentences or fewer. Name the frozen sources, labels, model/preprocessing information, common cutoff, per-case review and what counts as a held-out question. State that no neural comparison has run in this course package.

Self-review: correct cosine/RRF arithmetic, no invented candidates, explicit exact-token limitations and honest experiment status.