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The placement assessment is below — 13 questions, about 10 minutes, anonymous. It tells you whether to start with Foundations or jump straight into Database Development, and explains the reasoning behind every answer.

A short, low-stakes diagnostic — 13 questions, about 10 minutes. It is not pass/fail; it finds where you should start. Answer honestly: there's no benefit to guessing.

1 A developer runs the query below and it fails with an "invalid column name 'SalePrice'" error. What best explains why this query fails?

SELECT ListPrice * 0.9 AS SalePrice
FROM   SalesLT.Product
WHERE  SalePrice < 100;

WHERE is evaluated before SELECT, so the SalePrice alias does not yet exist when WHERE runs. WHERE may only compare literals, never columns. The alias collides with a reserved keyword. Column aliases can only be used inside subqueries.

2 In a SalesOrderHeader table, the column CustomerID holds values that each match the primary key of the Customer table. What is CustomerID in SalesOrderHeader? A computed column. The primary key of SalesOrderHeader. An index. A foreign key that references the Customer table.

3 You run the same SELECT (no ORDER BY) twice and the rows come back in a different order each time. What is the correct conclusion? You must add DISTINCT to stabilize order. Without ORDER BY, row order is not guaranteed. The table is missing a primary key. The table is corrupted.

4 Which predicates correctly return products whose ListPrice is between 100 and 200 inclusive? Select all that apply. Select all that apply WHERE ListPrice >= 100 OR ListPrice <= 200 WHERE ListPrice IN (100, 200) WHERE ListPrice >= 100 AND ListPrice <= 200 WHERE ListPrice BETWEEN 100 AND 200

5 You need the average ListPrice per product category, counting only products whose ListPrice is greater than 0, and returning only categories whose average exceeds 500. Where do the two conditions belong? Both conditions in HAVING. ListPrice > 0 in WHERE; AVG(ListPrice) > 500 in HAVING. Both conditions in WHERE. ListPrice > 0 in HAVING; AVG(ListPrice) > 500 in WHERE.

6 A Phone column contains some NULLs. You run SELECT COUNT(*), COUNT(Phone) FROM …. Which statement is true? Both always return the same number. COUNT(*) counts all rows; COUNT(Phone) excludes NULLs, so it is less than or equal to COUNT(*). COUNT(*) errors if any column contains NULL. COUNT(Phone) counts NULLs as empty strings.

7 Two SELECTs return overlapping rows. What is the difference between combining them with UNION versus UNION ALL? UNION removes duplicate rows; UNION ALL keeps every row including duplicates. They produce identical results in all cases. UNION joins columns side by side; UNION ALL stacks rows. UNION ALL removes duplicates; UNION keeps all rows.

8 You write a LEFT JOIN from Customer to SalesOrderHeader to list every customer alongside their orders. You then add a condition on the order's status. Placing that condition in the WHERE clause unexpectedly drops customers who have no orders at all. Why, and what is the fix? WHERE is applied after the outer join and rejects the rows where the order columns are NULL, effectively turning it into an inner join; move the status condition into the ON clause. The ON clause cannot hold extra conditions, so WHERE is required. Add DISTINCT to restore the missing customers. LEFT JOIN is the wrong choice; switch to RIGHT JOIN.

9 Which statements about NULL in SQL Server are correct? Select all that apply. Select all that apply NULL = NULL evaluates to true. To find rows where a column is NULL, use IS NULL rather than = NULL. NULL represents "unknown," not zero and not an empty string. Aggregates such as SUM and AVG ignore NULLs.

10 An INNER JOIN between Customer and SalesOrderHeader on CustomerID returns which rows? All customers, with NULLs for those without orders. Only orders that have no customer. Only customers who have at least one matching order (and only their matching orders). Every combination of customers and orders.

11 You are about to run an UPDATE that should change only a handful of rows in a production-like database. Which habit best protects you? Run the UPDATE and check the affected row count afterward. Run the matching SELECT first to preview the affected rows, then perform the UPDATE inside an explicit transaction you can verify and roll back. Disable constraints first so the update can't fail. Add TOP to every UPDATE to cap the blast radius.

12 You need to convert a free-text column to integers, but some rows contain non-numeric text, and you do not want the whole query to error out. Which approach fits? There is no safe option; the data must be cleaned by hand first. Use CAST — it returns NULL on bad input. Use TRY_CAST — it returns NULL for values that can't be converted instead of raising an error. Use CONVERT with style 0 to skip bad rows.

13 Which are valid, appropriate uses of a CASE expression in a query? Select all that apply. Select all that apply Bucketing rows into categories and then counting per bucket when combined with GROUP BY. Choosing one of several literal results based on a column's exact value (simple CASE). Replacing IS NULL checks so NULLs can be compared with =. Returning a text label based on which numeric range a value falls into (searched CASE).

Low-stakes and anonymous — this finds where you should start, it doesn’t pass or fail you.

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