Databricks Certified Data Engineer Professional Practice Mode

Community Discussion

Written by other learners. Comments can disagree with the marked answer — check the official documentation when in doubt.

• Chose A0

  - Correct answer : A — `GROUP BY ROLLUP (region, product_category)`, which produces the detail-level groups plus subtotals for `region` and a grand total, following the hierarchical order of the listed columns. A . Correct. `ROLLUP (region, product_category)` produces the full detail grouping, then a subtotal per `region` (dropping `product_category`), then a single grand total, matching the hierarchical requirement described without needing a `product_category`-only subtotal. B . Incorrect for this requirement. `CUBE` also produces a `product_category`-only subtotal (with region dropped) that was not requested, so it returns a superset of the needed groupings rather than the specific hierarchical set the report calls for. C . Workable but not the right construct here. Manually unioning three separately grouped queries can reproduce the same rows, but it requires three separate scans and aggregations and more code than the purpose-built rollup syntax designed for exactly this hierarchical subtotal pattern. D . Incorrect. Spark SQL's `GROUP BY` clause has no `WITH TOTALS` syntax; that construct does not exist in Spark SQL and the query would fail to parse.