Option C explicitly selects every required leaf field by using dot notation for nested structs. The resulting DataFrame retains event_id and event_ts while projecting device.id, device.model, device.location.latitude, and device.location.longitude as output columns. Option B expands only the immediate fields of device; the location value remains a nested struct, so the data is not fully flattened. Option A creates separate columns for the device identifier and model but retains the complete location struct instead of extracting latitude and longitude. Option D places bracket-like expressions inside column-name strings, which do not represent the required nested struct paths. Spark DataFrames support selecting nested fields through qualified column paths, making option C the direct transformation for creating analytics-friendly root-level fields.

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