DoorDash HackerRank Questions: My 2025 OA Notes and Prep Guide

The DoorDash HackerRank online assessment I prepared from had two coding questions and a 90-minute time limit. Both questions were medium-level rather than trick-heavy, and both rewarded careful simulation, data-structure choices, and clean edge-case handling.

This guide rewrites the experience for ExtraBrain readers who want practical DoorDash OA preparation without relying on rule-breaking shortcuts. Use any AI interview assistant, including ExtraBrain, only where the interview, employer, school, workplace, and assessment platform rules allow AI assistance, transcription, screenshots, or notes.

DoorDash HackerRank questions I saw

Question 1: Team formation

The first question was a team-selection simulation problem. At each step, you had to choose the maximum score from either the first k candidates or the last k candidates in the current list. If multiple candidates shared the maximum score, the candidate with the smallest original index had to be selected. After selection, that candidate was removed, and the process repeated until team_size candidates had been chosen.

A direct implementation looks tempting. You can slice the first k values, slice the last k values, merge those candidate ranges, find the maximum with the smallest index, remove the chosen value, and repeat. The problem is that removing from the middle of a Python list is O(n), and with n as large as 100000, repeated removals can time out.

The approach I used was to avoid physical deletion. I kept two pointers, left and right, to represent the active candidate window. For each selection, the front candidate segment was [left, min(left + k - 1, right)], and the back candidate segment was [max(right - k + 1, left), right]. I scanned those two segments, picked the best score with the smallest index, added it to the result, and then moved the corresponding pointer past the selected candidate.

That gives a simple O(team_size * k) solution, which is much better than repeatedly shifting a large array. If k can be very large, a stronger version uses heaps for the front and back candidate pools, giving O(team_size * log k) extraction after setup.

A useful implementation plan is:

1. Initialize left = 0, right = len(score) - 1, and total = 0.
2. Repeat team_size times.
3. Define the front range as left through min(left + k - 1, right).
4. Define the back range as max(right - k + 1, left) through right.
5. Scan available candidates and choose the largest score.
6. Break ties by choosing the smallest index.
7. Add that score to total.
8. If the chosen candidate came from the front side, move left to chosen_index + 1.
9. If the chosen candidate came from the back side, move right to chosen_index - 1.
10. Return total.

For example, suppose score = [10, 20, 10, 15, 5, 30, 20], k = 3, and team_size = 2. In the first round, the front range is indexes 0 through 2, and the back range is indexes 4 through 6. The largest score is 30 at index 5, so the total becomes 30, and the right boundary moves left. In the second round, the largest remaining eligible score is 20 at index 1, so the total becomes 50. The answer is 50.

Question 2: Discount events and lazy updates

The second question was an array-query problem with repeated updates. The important operation was a type of query that set all values to the same number. A brute-force solution updates every element for each global assignment, but that becomes too slow when the number of values and the number of queries are both large.

The key idea is lazy global assignment. Instead of rewriting the entire array every time a global update appears, store a global_value and a global_timestamp. For point updates, store the point value together with the time when that individual value was last changed. When answering or finalizing a value, compare the element timestamp with the global timestamp. If the element was updated after the last global assignment, use the element value. Otherwise, use the global value.

This pattern is common in DoorDash-style online assessments because the statement can look like ordinary simulation, while the constraints force you to recognize the need for state compression. It is also a good example of why reading constraints matters before coding the most obvious solution.

A practical implementation plan is:

1. Store the original values in an array.
2. Store a parallel timestamp array initialized to 0.
3. Keep global_value unset at the beginning.
4. Keep global_time = -1 at the beginning.
5. Increment a query counter for each operation.
6. For a global set operation, update global_value and global_time.
7. For a point update, update that index and set its timestamp to the current query counter.
8. For a read, sum, or final output operation, use the point value only if its timestamp is newer than global_time.
9. Otherwise, use global_value.

If the problem asks for running totals after each query, update the total directly instead of recomputing the whole array. For a global set operation on an array of length n, the total becomes n * global_value. For a point update, first resolve the current value at that index, subtract it from the total, add the new value, and record the point timestamp.

DoorDash HackerRank assessment format

Question types

The DoorDash OA commonly focuses on two coding questions. Some candidates may also see a lightweight SQL question, but the core assessment is usually coding-heavy.

The coding problems often fall into these categories:

• Logic simulation where the main challenge is translating dense rules into exact code.
• Array, string, or list processing where edge cases matter.
• Data-structure optimization where brute force passes samples but fails hidden tests.
• Greedy or two-pointer problems where the efficient choice is local but must be justified.
• Lazy update problems where repeated operations need compressed state.

Timing and environment

The typical time limit is 90 minutes for two coding questions. The assessment runs in the HackerRank editor, with common languages such as Python, Java, C++, and JavaScript usually available depending on the role setup. Sample tests and hidden tests are run by the platform. You can often run custom tests, but hidden failing inputs are not shown.

What made the DoorDash OA challenging

The hard part was not obscure algorithms. The hard part was reading precision.

In the team formation problem, every detail mattered: choose from the first k or last k, break ties by smaller index, remove the chosen candidate, and repeat on the remaining list. Missing one rule changes the answer.

The second problem had the same pattern in a different form. A simple simulation was easy to write, but the constraints pushed the solution toward lazy updates and timestamp tracking. That is the kind of practical optimization that shows up in real engineering work.

How I would prepare for DoorDash HackerRank

Practice medium simulation problems

Start with medium-level HackerRank or LeetCode problems involving arrays, strings, queues, heaps, and greedy choices. Do not only practice advanced dynamic programming. DoorDash-style online assessments often care more about whether you can model business-like rules correctly and optimize the obvious approach.

Good practice filters include:

• Arrays
• Greedy
• Simulation
• Two pointers
• Heap or priority queue
• Hash map
• Prefix sums
• Lazy propagation or delayed updates

Read constraints before choosing an approach

Spend the first few minutes identifying the real bottleneck. Ask whether the brute-force operation is inside a loop. If an O(n) operation appears inside q queries, calculate whether n * q is realistic. If it is not, look for a way to store only the changed state.

For the team formation problem, the bottleneck was deletion from the middle of a list. For the discount-events style problem, the bottleneck was rewriting the whole array after every global update.

Build a clean skeleton first

Before solving every detail, set up input parsing, output formatting, helper functions, and the main loop. This prevents wasting time on a perfect algorithm that does not match the required function signature.

After the skeleton runs, fill in the selection logic, update rules, and edge-case handling. This also makes debugging easier because you can test one small piece at a time.

Use small custom tests

Create tiny cases that force tie-breaking, overlapping ranges, empty boundaries, repeated global updates, and point updates after global updates. The samples are useful, but they rarely cover every failure mode.

For team formation, test cases where the maximum appears on both sides. For lazy updates, test a point update before a global update, then another point update after it.

Practice explaining the tradeoff

If you move to a live technical round after the OA, you may be asked to explain why your solution works. Practice saying the complexity tradeoff clearly. For example, say that physical deletion is expensive, so you represent the remaining list with boundaries or maintain candidate pools with heaps.

ExtraBrain can help during allowed practice sessions by transcribing your explanation, capturing screen context, and helping you review whether your reasoning sounded clear. The core Mac app is free, and local-first options are available when local Parakeet transcription and local Gemma 4 are installed and compatible.

Responsible AI use for online assessments

A tool like ExtraBrain is best used for preparation, mock interviews, debriefs, and review unless the assessment rules explicitly allow live assistance. ExtraBrain is a free, local-first Mac desktop AI interview assistant and meeting copilot with live transcription, screen-aware context, bring-your-own AI providers, local Gemma 4 where installed and compatible, and privacy controls.

That does not change the responsibility of the candidate. Follow the rules from DoorDash, HackerRank, your recruiter, your school, your employer, and any proctoring or assessment policy. If an assessment prohibits outside help, do not use AI assistance during the live test.

A safe preparation workflow is:

1. Practice similar questions before the assessment.
2. Use ExtraBrain during allowed mock interviews or self-practice to organize your explanation.
3. Review transcripts afterward to find unclear reasoning or missed edge cases.
4. Build your own solution templates for arrays, heaps, hash maps, and lazy updates.
5. Enter the live assessment ready to solve independently within the stated rules.

FAQ

How many questions were on the DoorDash HackerRank OA?

The experience described here had two coding questions and 90 minutes. Other candidates may see a slightly different mix depending on role, location, timing, and recruiting process.

What was the most important DoorDash HackerRank topic?

The biggest theme was precise simulation with optimization. The problems were not about memorizing rare algorithms. They were about reading dense instructions, identifying bottlenecks, and replacing brute force with two pointers, heaps, hash maps, or lazy state.

Should I optimize immediately or write brute force first?

Write down the brute-force idea first, but check the constraints before implementing it fully. If brute force is clearly too slow, use it only as a mental model and code the optimized version. If the constraints are moderate, a correct simple solution may be enough.

What should I do if hidden tests fail?

First check tie-breaking, boundary ranges, indexing, and repeated updates. Then create custom tests around the smallest failing pattern you can imagine. For the team formation problem, focus on duplicate maximum values and overlapping front and back windows. For lazy update problems, focus on the order of point updates and global updates.

What happens after passing the DoorDash OA?

In the original experience, the next stage came about two weeks later and included multiple interviews, including coding and behavioral rounds. Timelines vary by team and recruiting cycle, so use that only as a rough expectation.

See also

• ExtraBrain for interview preparation
• Responsible use of ExtraBrain
• ExtraBrain privacy controls
• ExtraBrain AI providers