```json { "table_name": "polling_on_ai_usage_jul_2026", "primary_key": "question_number,response_verbatim", "candidates": [ { "rank": 0, "partition_key": "question_number", "partition_key_label": "primary_identifier", "partition_key_reasoning": "RPUV=7.00 is ideal for partitioning. This column groups the 105 rows into 15 logical question blocks (each question has multiple response rows). Semantically, it is the primary organizing dimension for this polling data — each question represents a distinct survey item with its own set of responses and demographic breakdowns.", "clustering_key": null, "clustering_key_label": null, "clustering_key_reasoning": "Dimension tables typically do not require clustering. The response_verbatim column (RPUV=1.62, 65 unique values) could theoretically order responses within each question, but it is not a natural sort axis (responses are categorical, not sequential). No clustering key is needed.", "world_line": null, "session_column": null, "confidence": "HIGH", "notes": "This is a dimension table representing polling survey structure: each row is a (question, response, demographic_segment) tuple with percentage values. The composite primary key (question_number, response_verbatim) uniquely identifies each row. Partitioning by question_number organizes the data by survey question, which is the natural access pattern for analysis. The 40+ demographic columns (Gender, Age, Income, etc.) are attributes, not keys. The _ingestion_timestamp and _batch_id are metadata with RPUV=105 (single value) and are not viable keys." } ] } ```