OpenAI Coding Interview Question Bank for 2026

OpenAI coding interviews tend to reward practical engineering judgment more than memorized LeetCode patterns. The strongest preparation is not just knowing a possible question list. It is learning how to clarify vague requirements, write tests, handle follow-ups, explain tradeoffs, and keep code correct under pressure.

This guide rewrites the collected 2026 OpenAI coding interview question bank into a practical ExtraBrain prep article. Use it as a study map for implementation-heavy interviews, not as a promise that every candidate will see these exact questions. Interview loops change, interviewers vary, and follow-up questions are often where the real signal appears.

ExtraBrain is a free, local-first Mac desktop AI interview assistant and meeting copilot with live transcription, screen-aware context, local Gemma 4 where installed and compatible, bring-your-own AI providers, and privacy controls. If you use any AI tool while preparing or during a live interview, only use it where the interview, employer, school, workplace, and platform rules allow AI assistance, transcription, screenshots, or notes.

OpenAI coding interview overview

Typical process

A software engineering interview loop can include these stages:

• Application submission.
• Recruiter screen.
• Technical screen.
• Virtual onsite loop.
• Offer discussion.

A full process can take several weeks. The waiting time between stages is useful for targeted practice, especially for test design, implementation speed, and explaining tradeoffs aloud.

What makes the coding round different

The OpenAI coding interview is often described as more implementation-oriented than pattern-recognition-oriented. You may be asked to build a small system, extend it through multiple parts, and defend decisions around correctness, persistence, concurrency, or performance.

Correctness usually matters more than cleverness. A clear brute-force version with strong tests can be better than a half-finished optimized design.

How to use ExtraBrain for preparation

Use ExtraBrain before interviews to rehearse how you think out loud. You can practice with live transcription, review the session afterward, and turn messy reasoning into a clearer checklist.

For coding practice, use it to structure clarifying questions, summarize edge cases, and debrief where your implementation got slow. During live interviews, use ExtraBrain only if the rules for that interview allow it.

The 8 practice problems

Coding problem 1: Time-based key-value store

Prompt: Implement a time-based key-value store using real timestamps. Support set and get operations, versioned reads, persistence to the file system, and restoration from storage. Keys and values are strings and may contain any characters, including newlines. You must implement custom serialization and deserialization without relying on built-in JSON-style libraries.

A complete interview version can include these requirements:

• set(key, value) records a value at the current timestamp.
• get(key, timestamp) returns the latest value at or before that timestamp.
• Timestamps must be strictly increasing or safely handled when clock behavior is unexpected.
• Tests should mock timestamps instead of depending on wall-clock time.
• The store should persist to disk and restore correctly.
• The design should handle concurrent updates.

Follow-up questions:

• How do you ensure update consistency under multithreading?
• What happens when get receives a future timestamp?
• If the current timestamp is 10, get("key", 20) is called, then timestamp 15 adds "value_15", should the earlier future query later reflect that value?
• How would you compare a global lock, per-key lock, and optimistic locking approach?

Approach:

Start with an in-memory map from key to a sorted list of (timestamp, value) pairs. Use binary search for get. Inject a clock interface so tests can control time deterministically. For persistence, use length-prefixed records so arbitrary strings, delimiters, and newlines remain safe.

For concurrency, explain the simplest correct lock first. A global lock is easy and safe but can limit throughput. A per-key lock improves parallelism when keys are independent. Optimistic concurrency can reduce blocking, but it adds retry logic and more failure modes.

Test checklist:

• Set one key once and read it at the exact timestamp.
• Read before the first timestamp.
• Read between two versions.
• Read after the latest version.
• Persist and restore keys with newlines and delimiter-like characters.
• Simulate concurrent writes to the same key.
• Simulate concurrent writes to different keys.

Coding problem 2: cd directory resolver

Prompt: Implement cd(current_dir, new_dir) so it returns the resulting absolute path after applying a directory change. Handle . and .. path segments, reject traversal above root, and normalize redundant separators.

Examples:

cd(/foo/bar, baz) = /foo/bar/baz
cd(/foo/../, ./baz) = /baz
cd(/, foo/bar/../baz) = /baz
cd(/, ..) = Null

Part 2: Add support for ~ as the home directory.

Part 3: Add a soft_link dictionary that maps symbolic-link paths to target paths. Resolve links while walking the path. The longest matching path should take precedence over shorter matches. Detect cycles such as A -> B and B -> A.

Examples:

cd(/foo/bar, baz, {/foo/bar: /abc}) = /abc/baz
cd(/foo/bar, baz, {/foo/bar: /abc, /abc: /bcd, /bcd/baz: /xyz}) = /xyz
cd(/foo/bar, baz, {/foo/bar: /abc, /foo/bar/baz: xyz}) = xyz

Approach:

Normalize paths by splitting them into components and maintaining a stack. For symbolic links, repeatedly check whether the current normalized path has a matching link. Choose the longest matching prefix so specific links override broad links. Track visited link states to detect cycles.

Common pitfalls:

• Treating .. from root as valid.
• Applying a short symbolic link before a longer one.
• Forgetting that a link target may be relative.
• Missing cycles after multiple expansions.

Coding problem 3: Spreadsheet cells with formulas

Prompt: Implement a spreadsheet API with setCell and getCell. A cell may hold an integer value or a formula depending on other cells, such as =A1+A2. The implementation must detect circular dependencies and support tests for updates.

Example:

spreadsheet = Spreadsheet()
spreadsheet.setCell('A1', '1')
spreadsheet.setCell('A2', 2)
spreadsheet.setCell('A3', '=A1+A2')
spreadsheet.setCell('A4', '=A3+A2')
spreadsheet.setCell('A5', '=A3+A4')
spreadsheet.setCell('B1', '=A1+A2+A3+A4+A5')

A node-style example can look like this:

Cell A = Cell(6, NULL, NULL)
Cell B = Cell(7, NULL, NULL)
Cell C = Cell(13, A, B)
print(getCell(C)) # returns 13
# after updating Cell A = Cell(2, NULL, NULL)
print(getCell(C)) # returns 9

Part 1: Implement a simple version where getCell computes formula values on demand with DFS.

Part 2: Optimize so getCell becomes O(1) by updating dependent cells during setCell.

Approach:

Represent dependencies as a graph. Each formula cell points to the cells it reads. Maintain a reverse graph from each cell to the cells that depend on it.

For the first version, evaluate recursively and use a visited set to detect cycles. For the optimized version, update the dependency graph on every setCell, then propagate value changes through downstream dependents.

Test checklist:

• Literal integer cells.
• Formula cells with one dependency.
• Formula cells with multiple dependencies.
• Updating a source cell updates downstream cells.
• Replacing a formula with a literal removes old dependencies.
• Direct cycles are rejected.
• Indirect cycles are rejected.

Coding problem 4: In-memory database

Prompt: Implement an in-memory database that supports table creation, row insertion, and query selection. The select(table_name, where=None, order_by=None) interface should support filtering and sorting while preserving backward compatibility.

Possible requirements include:

• Create tables from column name lists.
• Insert rows into named tables.
• Query by one column or multiple columns.
• Support comparison operators such as >, <, >=, <=, and =.
• Support multiple where conditions.
• Support AND logic at minimum.
• Some variants may ask for OR logic.
• Support single-column and multi-column sorting.

Approach:

Start with a simple table representation that stores rows as dictionaries or typed row objects. Implement filtering as a predicate pipeline. Implement sorting with a stable multi-key comparator.

Make the API explicit. For example, where can be a list of condition objects and order_by can be a list of (column, direction) pairs. If the interviewer asks for backward compatibility, accept older simpler forms and normalize them into the new internal representation.

Tradeoffs to discuss:

• A full scan is simple and correct for small data.
• Indexes can speed repeated queries but complicate inserts and updates.
• Type handling should be clarified before comparing values.
• Missing columns should either be rejected or handled consistently.

Coding problem 5: Resumable iterator

Prompt: Implement a resumable iterator that supports getState() and setState(). The first parts use simple lists and indices. Later parts extend the same idea to multiple JSON files, empty files, async iteration, 2D lists, and 3D lists.

The state object returned by getState() should allow iteration to resume from the same logical position. The caller should not assume the state is just an index.

Example:

list = [1, 2, 3, 4]
next() -> 1
next() -> 2
getState() -> state after consuming 2
next() -> 3
setState(saved_state)
next() -> 3

Subtasks:

• Define an iterator interface with __iter__, __next__, get_state, and set_state.
• Implement a resumable list iterator.
• Implement a resumable multi-file iterator using an existing file iterator.
• Handle empty files.
• Add tests for exhausted iterators.
• Convert the design to async iteration if requested.
• Extend the pattern to 2D and 3D nested structures.

Test pseudo-code:

def test_iterator(my_iter):
    states = []
    while iterator_has_more(my_iter):
        states.append(my_iter.get_state())
        consume_one(my_iter)
    for state in states:
        test_iter = clone_or_reset_iterator()
        test_iter.set_state(state)
        elements = consume_remaining(test_iter)
        assert elements == expected_suffix_from(state)

Approach:

Design the state as a small serializable object that captures all coordinates needed to resume. For a list iterator, that may be the current index. For a multi-file iterator, that may include file index, byte offset, decoded item index, and whether the current file is exhausted.

The harder part is not the happy path. It is preserving exact semantics around empty files, end-of-iteration, and restoring state after partial reads.

Coding problem 6: Distributed node counting

Prompt: You are given a tree of machines. Each node can communicate only with its parent and children. The system provides sendAsyncMessage(target, message). You must implement receiveMessage(message) so the root can count how many machines exist in the whole tree.

Basic protocol:

• The root sends a count request to its children.
• Each non-leaf node forwards the count request to its children.
• Each leaf responds with count 1.
• Each internal node waits for all child responses.
• Each internal node returns 1 + sum(child_counts) to its parent.
• The root reports the final total.

Example:

root -> 2 children -> each child has 2 leaves
result = 7 machines

Key points:

• Only parent-child message passing is allowed.
• Requests propagate downward.
• Responses aggregate upward.
• Leaf and root nodes need special handling.
• The protocol should consider lost messages, duplicate messages, and missing child responses.

Approach:

Use a request ID for every counting operation. Track pending children and partial counts per request ID. When all children have responded, send the aggregate result upward. For robustness, discuss timeouts, retries, idempotent responses, and duplicate suppression.

Coding problem 7: GPU credit ledger

Prompt: Implement a credit system for GPU usage. Credits can be added at a timestamp, expire at a future timestamp, and be consumed by cost events. A cost event succeeds only if enough unexpired credits are available at that time.

Operations:

• Add(time, amount, expire_time) adds credits that expire later.
• Cost(time, amount) consumes credits if enough are available.
• Expired credits should not be spendable.
• Credits should be consumed from the oldest expiring bucket first.
• Events may be interleaved across timestamps.

Example:

Add(10, 5, expire=20)
Cost(15, 3) -> True

After the cost event, the future expiration at time 20 should effectively have only 2 credits remaining.

Approach:

Process events in chronological order. Maintain a min-heap or ordered structure keyed by expiration time. Before a cost, remove expired buckets. Then check total available balance. If the balance is sufficient, consume from the earliest expiration buckets first.

If events can arrive out of order, clarify whether the system must support retroactive insertion. That version is much harder because a later-added historical event may invalidate previously accepted costs.

Test checklist:

• Spend less than the available balance.
• Spend exactly the available balance.
• Reject spending above the available balance.
• Expire credits before spending.
• Consume from the earliest expiration first.
• Interleave multiple adds and costs.
• Clarify behavior for same-timestamp add, cost, and expire events.

Coding problem 8: Dependency version feature check

Prompt: Given a list of dependency versions, find the earliest version that supports a feature. Versions look like 103.003.02, 103.003.03, and 203.003.02. You must parse and compare version numbers correctly.

Part 1: Implement the basic earliest-supporting-version search.

Part 2: The interviewer provides corner test cases that may invalidate your initial assumptions. For example, one version may support the feature while the next version does not. You need to observe the data, identify the hidden rule, and confirm the requirement with the interviewer.

Approach:

Start by parsing versions into numeric tuples such as (major, minor, patch). Sort or scan according to the input contract. Ask whether feature support is monotonic across versions. If support is monotonic, binary search may be valid. If support is not monotonic, you need a linear scan or a different rule.

Key points:

• Do not assume version strings compare lexicographically.
• Do not assume later versions always support everything earlier versions support.
• Use corner tests to refine the model.
• State assumptions before optimizing.

How to practice these problems

Start with clarifying questions

For each problem, ask what inputs are valid, what output is expected for edge cases, and whether performance constraints matter. This is especially important for the key-value store, symbolic-link resolver, spreadsheet graph, credit ledger, and dependency version problem.

Implement the simplest correct version first

A correct baseline gives you something to test and extend. For these questions, the baseline is often more valuable than an early optimization.

Examples:

• Use DFS before spreadsheet caching.
• Use full table scans before database indexes.
• Use a global lock before a per-key lock.
• Use linear scan before binary search when monotonicity is not proven.

Keep a running test list

Implementation-heavy interviews often reveal bugs through follow-up examples. Keep a visible list of tests as you code. If you use ExtraBrain for preparation, review your transcript afterward and turn missed edge cases into a reusable checklist.

Explain tradeoffs clearly

The interviewer may care as much about reasoning as final code. When you choose a simple design, explain why it is correct. When you mention an optimized design, explain what complexity it adds.

Language choice

Python is a practical language for these problems because it is concise and quick for maps, lists, heaps, parsing, and tests. That said, the best language is the one you can use accurately under time pressure. If you are stronger in Java, C++, Go, or TypeScript, use the language that lets you build correct code and explain it clearly.

Responsible use of AI interview tools

ExtraBrain can help you practice coding interviews by capturing transcripts, organizing follow-up questions, and reviewing your reasoning after a session. It can also help generate answer outlines, technical explanations, and clarifying questions from live transcript and screen context.

You remain responsible for honest and allowed use. Do not use any AI interview assistant to violate interview rules, bypass proctoring, misrepresent your abilities, or ignore an employer’s policy. If a company prohibits AI assistance in a live interview, use ExtraBrain only for preparation and post-practice review.

FAQ

Are these guaranteed OpenAI interview questions?

No. Treat them as realistic practice prompts based on reported interview-style patterns, not as a guaranteed current question bank. The best preparation is learning the underlying implementation skills and follow-up reasoning.

How should I manage time during the coding interview?

Most of these problems can expand into multiple parts. Spend the first few minutes clarifying requirements and listing test cases. Then implement a correct baseline before moving to optimization. If time gets tight, prioritize correctness, clear edge-case handling, and readable code.

Which problem should I practice first?

Start with the time-based key-value store and spreadsheet problems. They combine data structures, tests, edge cases, and follow-up design discussion. Then practice the directory resolver and GPU credit ledger because they expose many subtle state-management bugs.

Can ExtraBrain help during an OpenAI coding interview?

ExtraBrain can support live transcription, screen-aware context, and interview review on Mac when configured by the user. Use it during a live interview only if the interview rules allow AI assistance, transcription, screenshots, or notes. For preparation, it is useful for mock interviews, debriefs, and building a personal checklist from your own mistakes.

What is ExtraBrain?

ExtraBrain is a free, local-first Mac desktop AI interview assistant and meeting copilot with live transcription, screen-aware context, local Gemma 4 where installed and compatible, bring-your-own AI providers, and privacy controls. Extra Brain is a common spaced search alias for the same app.

See also

• ExtraBrain
• Responsible use
• AI providers
• Privacy