Fresh SQL interview question threads
Social posts circulated recent SQL interview questions covering fundamentals like SELECT vs WHERE, top‑salary queries, GROUP BY/HAVING, and JOIN types—material aimed squarely at data‑analyst interview prep. A separate social post linked a broader data‑science interview guide that lists essential skills (Python/SQL/ML), key topic areas, and recommended platforms for practice. ( )
A pair of social posts pushed fresh Structured Query Language interview prep back into feeds, with question lists built around the basics most data-analyst screens still test first. (x.com, geeksforgeeks.org) The questions highlighted familiar filters and table logic: how `SELECT` retrieves columns, how `WHERE` filters rows, how `GROUP BY` bundles records before aggregation, and how `HAVING` filters grouped results after totals are computed. GeeksforGeeks’ December 5, 2025 SQL interview roundup uses the same building blocks in sample prompts and answers. (geeksforgeeks.org) Another recurring prompt asks candidates to find a highest salary, a pattern that usually tests sorting, ranking, subqueries, or window functions rather than memorization. Join questions do the same for table relationships, especially inner join, left join, right join, and full join. (geeksforgeeks.org, docs.snowflake.com) Structured Query Language is the standard language for relational databases, the table-based systems used by products, finance teams, and analytics groups to store and retrieve records. PostgreSQL, one of the best-known open-source relational databases, said on February 26, 2026 that version 18.3 was part of its latest supported releases. (geeksforgeeks.org, postgresql.org) That matters for hiring because analyst and product-data interviews often start with SQL before they move to statistics, experiments, or machine learning. Coursera’s data-science interview guide, updated December 19, 2025, says technical rounds commonly test Python or R, SQL, statistics, machine learning judgment, and communication. (coursera.org) The second post in circulation pointed to that broader style of preparation: not one language in isolation, but a stack that combines coding, data handling, modeling, and business explanation. Exponent’s 2026 guide splits data-science roles into machine-learning, product-analytics, full-stack, and engineering tracks, with SQL emphasized most heavily in product and analytics interviews. (x.com, tryexponent.com) Practice platforms are also packaging that mix more explicitly. StrataScratch says its site has more than 813 questions, 81 free questions, and practice across SQL, Python, and statistics, while SQLit offers timed mock SQL interviews filtered by role, including data analyst and data engineer. (stratascratch.com, sqlit.space) The underlying interview logic has not changed much: employers want to know whether a candidate can pull the right rows, combine the right tables, and explain the result without breaking the data. That is why the oldest-looking prompts in these threads — filters, joins, salary rankings, and grouped counts — keep resurfacing in new prep posts. (coursera.org, tryexponent.com)
