You can solve this in **SQL or Python**. You are given two datasets with the same feature columns: ### Tables **`target_rows`** (rows you want to match) - `target_id` (STRING, PK) - `f1, f2, ..., fk` (DOUBLE; numeric features; may contain NULLs) **`candidate_rows`** (rows to search) - `candidate_id` (STRING, PK) - `f1, f2, ..., fk` (DOUBLE; numeric features; may contain NULLs) ### Task For **each** row in `target_rows`, find the **top 5** most similar rows in `candidate_rows` using **all features**. 1. Define a similarity/distance metric using all features (e.g., **MSE** across features). 2. Compute the distance between each target row and candidate row. 3. Return the 5 candidates with the smallest distance per target. ### Distance definition (use this unless you clearly state an alternative) Let the feature set be \(\{f_1,\dots,f_k\}\). Define distance as: \[ \text{MSE}(t,c) = \frac{1}{k}\sum_{j=1}^{k} (t.f_j - c.f_j)^2 \] Assumptions you should clarify in your solution: - How you handle **NULL** features (e.g., drop those dimensions for that pair, or impute). - Whether you need **feature scaling/standardization** before computing distances. ### Output Return: - `target_id` - `candidate_id` - `distance` (smaller = more similar) - `rank` (1 to 5 per `target_id`, where 1 is most similar) Order results by `target_id`, then `rank`.