Greater Than All Accessories — Solution & ExplanationProblems / Greater Than All Accessories / EditorialApproach

price > ALL (SELECT price FROM products WHERE category = 'Accessories') keeps a row only if its price beats every accessory. For a non-empty, NULL-free set, > ALL is equivalent to > MAX, so price > (SELECT MAX(price) ...) is the same query.

Add category <> 'Accessories'. The premium Leather Laptop Sleeve (95.00) beats several non-accessory products, but accessories are excluded from the output by definition — not by price.

Order by product_id.

Key InsightThe most expensive accessory costs 95.00 — more than the Budget Earbuds (60.00) or the Desk Lamp (85.00) — so the bar is a global one, not per-category, and cheap non-accessories genuinely fail it. The Smart Display at exactly 95.00 documents the strictness: > ALL means strictly greater, so a tie with the maximum is out. In PySpark, the scalar "max accessory price" becomes a one-row DataFrame crossJoined onto every product row — the DataFrame idiom for a scalar subquery.Hints

price > ALL (subquery) is true only when the price beats every row the subquery returns — the same as beating the subquery's MAX.

The two filters are independent: beat every accessory price AND not be an accessory. The premium accessory at 95.00 out-prices several other products, but it still must not appear in the output.

One product ties the maximum accessory price exactly — strictly greater excludes it.

PySpark has no scalar subqueries: aggregate the max accessory price into a one-row DataFrame and crossJoin it onto products, then filter.

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