SQL interview answers often go wrong not because a query uses an obscure feature, but because it changes the wrong rows, groups at the wrong stage, mishandles ties or NULLs, or hides its assumptions. There is no verified statistic here showing what percentage of candidates fail these concepts. The practical preparation priority is to explain the intended rows and each query step clearly.
What SQL topics are most commonly tested?
Two published collections point to joins, aggregation and window functions as frequent SQL interview subjects, but they count different samples and are not universal hiring statistics.
| Publisher and date | Reported SQL question distribution | What the figures represent |
|---|---|---|
| DataDriven, updated July 27, 2026 | GROUP BY and aggregation: 24.5%; JOINs: 19.6%; window functions: 15.1%; combined: 60% | Questions tracked on DataDriven’s platform, as described in its SQL interview cheat sheet. |
| DataScienceHired, as of August 29, 2026 | Joins: 30; window functions: 15; subqueries: 12; GROUP BY: 11, among 100 SQL questions | Its own published and tagged question bank; the report says the full dataset contains 389 questions across 49 companies and 32 topics. Company-question associations draw on public interview reports and candidate write-ups, not necessarily official company materials. See the report. |
The collections use different sources and category labels, so their counts should not be combined. Neither establishes the share of all employers’ interviews that cover a topic, predicts what a particular employer will ask, or measures candidate failure rates. Treat “fail” as a useful warning about common reasoning mistakes, not a measured outcome.
How should I think about joins and row counts?
A join is a rule for matching rows, not simply a way to combine two tables. Before writing one, identify the intended output grain—one row per customer, order, or customer-month, for example—and the key or keys that connect the tables. Then ask whether either key can repeat.
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Predict which rows survive
- INNER JOIN: returns rows with a match on both sides.
- LEFT JOIN: retains every left-side row; when there is no right-side match, right-side columns are NULL.
These are row-preservation rules, not guarantees about the result’s size. If one left-side key matches three right-side rows, that left row appears three times. If a key occurs twice on each side, the join can produce four combinations for that key. Duplicate keys can therefore multiply counts and sums. PostgreSQL 18 documents these join behaviors in Joins Between Tables.
In an interview, state which entities must remain in the result, whether the join key is unique on either side, and what multiplicity you expect. If the prompt asks for one result per customer but the joined data has several orders per customer, decide whether to aggregate or otherwise reduce orders before joining. Do not assume the output has one row per key merely because the join condition uses that key.
When do WHERE, GROUP BY and HAVING apply?
These clauses answer different questions: WHERE filters input rows, GROUP BY forms groups from the remaining rows, and HAVING filters those groups after aggregation.
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Example: customers with more than two orders
SELECT customer_id, COUNT(*) AS order_count
FROM orders
WHERE status = 'complete'
GROUP BY customer_id
HAVING COUNT(*) > 2;Here, only complete orders enter the groups; the query then counts them per customer and returns groups whose count exceeds two. Moving the count condition to WHERE would be wrong because that stage operates on individual input rows, not the completed group counts.
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COUNT(*) counts rows. COUNT(column) counts only rows where that expression is not NULL, so the two can differ when the column has missing values. PostgreSQL 18’s aggregate documentation describes grouping and aggregate behavior. In a spoken solution, name the stage each filter belongs to before deciding where to put it.
How do window functions differ from grouped aggregation?
A grouped aggregate generally produces one output row per group. A window function calculates across related rows while retaining each input row in the output, which is useful for ranks, running totals and comparisons against other rows in the same group.
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SELECT customer_id, order_id, order_date,
ROW_NUMBER() OVER (
PARTITION BY customer_id
ORDER BY order_date, order_id
) AS purchase_number
FROM orders;PARTITION BY restarts the numbering for each customer; ORDER BY defines the sequence within that customer. The additional order_id makes the ordering more specific if two orders share a date, assuming it distinguishes those rows.
Choose a ranking function based on ties
ROW_NUMBER()assigns a unique number to each row. Use it to select one row only when the ordering resolves ties sufficiently for the desired result.RANK()gives tied values the same rank and leaves gaps after ties.DENSE_RANK()gives tied values the same rank without gaps.
For a “top three” request, clarify whether the interviewer wants exactly three rows or every row tied within the top three ranks. That distinction can change the function and result size. For running or moving calculations, inspect the window frame rather than relying on an unstated default. PostgreSQL 18 explains window syntax and behavior in its window-function tutorial.
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How should SQL interview answers handle NULL?
NULL represents missing or unknown data; it is not an ordinary value that can be tested with equality. Use IS NULL or IS NOT NULL, not = NULL or <> NULL. Comparisons involving NULL can evaluate to unknown rather than true or false, which affects filtering.
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Be cautious with NOT IN
If a NOT IN subquery can return NULL, its three-valued comparison behavior can prevent rows from passing the predicate in ways that surprise candidates. Consider NOT EXISTS or an anti-join only after deciding how NULLs in both the outer and inner data should be treated; these patterns are not automatically interchangeable under every null-handling requirement.
Keep LEFT JOIN conditions in the right stage
A condition on the right-side table placed in WHERE can discard rows where the LEFT JOIN found no match, because those right-side columns are NULL. If the condition defines which right-side rows count as matches while preserving all left-side rows, put it in the join’s ON condition. If the goal is to discard left-side rows without a qualifying match, a WHERE filter may be appropriate. Work through a matched and unmatched example rather than relying on the clause name. PostgreSQL documents NULL-aware comparisons in Comparison Functions and Operators and join conditions in Table Expressions.
How can you decompose a multi-step SQL problem?
For a prompt such as “find each customer’s first purchase and compare it with the prior month,” make the transformations explicit. First define the relevant rows and date range; then calculate each customer’s first purchase; then derive the monthly comparison; finally select the requested output. The exact sequence depends on the prompt’s definitions of “first” and “prior month.”
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A common table expression (CTE) can name those intermediate results so you can explain and inspect each stage:
WITH relevant_orders AS (
SELECT customer_id, order_id, order_date, amount
FROM orders
WHERE status = 'complete'
), ranked_orders AS (
SELECT customer_id, order_id, order_date, amount,
ROW_NUMBER() OVER (
PARTITION BY customer_id
ORDER BY order_date, order_id
) AS purchase_number
FROM relevant_orders
)
SELECT customer_id, order_id, order_date, amount
FROM ranked_orders
WHERE purchase_number = 1;This example selects one earliest complete order per customer, using order_id to resolve same-date ordering if it is a suitable tie-breaker. It does not perform a prior-month comparison; that would require defining the relevant month and comparison rule first. A CTE improves visibility but does not itself fix duplicate joins, ambiguous ties or misplaced filters. PostgreSQL 18 documents WITH queries in its CTE reference.
What should you check before presenting a query?
Review the query against the prompt and table relationships, not just whether it runs. These are practical self-checks, not a universal interviewer scoring rubric:
- Grain and cardinality: What does one output row represent? Can duplicate keys or many-to-many matches multiply rows?
- Row preservation: Should unmatched entities remain? Does the join type and placement of conditions preserve them?
- Filter stage: Does a condition belong on source rows in WHERE, or on aggregate groups in HAVING?
- Missing data: What should happen when relevant values are NULL, and can NULL enter a NOT IN set?
- Ties and ordering: Should tied values share a rank? Does the ordering fully resolve a request for one row?
- Empty or missing groups: Should entities with no qualifying rows appear, and if so, how should their aggregate be represented?
- Dates and dialect: Are time boundaries, time zones and date functions explicit? Does the SQL match the employer’s database engine?
What SQL interview questions should I prepare for?
Practice problems that expose assumptions rather than memorizing query templates. Start with a small example containing repeated keys, unmatched rows, NULLs and tied values. Write the query before looking at a solution, and narrate the intended grain of each intermediate result.
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- Before each filter, say whether it applies to source rows or groups.
- For every window function, state its partition, ordering and—when relevant—frame.
- Test ties, missing values, empty groups and date boundaries with hand-checkable rows.
- Practice under a time limit you choose, but prioritize explaining assumptions over racing through syntax.
The SQL examples here use PostgreSQL 18 documentation for behavior and syntax. Other engines may differ in functions, date handling and supported syntax, so confirm the interview’s dialect when possible and adapt accordingly.
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