Schema Checker

An easy Python interview practice problem on DataDriven. Write the Python and run it against test cases, with instant feedback.

Domain
Python
Difficulty
easy
Seniority
L3

Problem

Given a record dict and a list of required keys, return a sorted list of keys that appear in required but NOT in record.

The schema says one thing. The data says another.

Example 1

Input:

record = {"age":30,"name":"Alice","email":"a@b.com"}, required = ["name","age","email","id"]

Output:

["id"]

Example 2

Input:

record = {"a":1,"b":2}, required = ["a","b"]

Output:

[]

Worked solution and explanation

Why this problem exists in real interviews

Schema validation shows up at every API boundary: request bodies, ETL records, config loaders. Interviewers use this to test whether you reach for set algebra, whether you respect the input contract (sorted output), and whether you handle the empty-required and empty-record edge cases without a tangle of branches.

Break down the requirements

Step 1: Frame as set difference

'Keys in required but not in record' is set(required) - set(record.keys()). Set difference is O(len(required)) average and reads exactly like the spec sentence, which is the strongest signal of correctness you can give an interviewer.

Step 2: Sort the result

Sets are unordered, but the spec wants a sorted list. Wrap the difference in sorted(...) to lock in deterministic output, which also makes the function trivially testable with assertEqual.

Step 3: Handle empty inputs naturally

Empty required returns []. Empty record returns sorted(required). The set-difference idiom handles both without a special case, so do not introduce one; extra branches are negative signal here.

The solution

Set difference, sorted
def missing_keys(record: dict, required: list) -> list:
    return sorted(set(required) - set(record.keys()))

Common follow-up questions