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How to Earn HackerPrep Credits by Submitting Real Interview Questions (and What Gets Accepted)

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11 min read

Your interview experience is valuable—even if the process didn’t go perfectly. On HackerPrep, you can earn credits by submitting real interview questions you personally encountered, and those questions help keep the community’s practice bank fresh and high-signal.

Quality standards matter more now than they did even a year ago. In just the last ~60 days, multiple open platforms have publicly tightened rules to fight low-quality and AI-generated spam (Bugcrowd’s “AI slop” policy changes, and Medium’s updated moderation approach in the AI era). Hiring is tightening integrity, too: CodeSignal reported that assessment fraud/cheating attempts more than doubled in 2025. Interview-question communities have to respond the same way—with clear acceptance criteria focused on authenticity, specificity, and safe anonymization.

This post explains how question submissions work on HackerPrep, what gets accepted (and rejected), and how to maximize your acceptance rate so you actually earn credits. If you want to see how questions are organized by company and round, browse examples like /company/google, /company/stripe, and /company/amazon. For a deeper set of “copy-ready” writing templates, also see /blog/submit-interview-questions-templates.

Intro: Turn your interview experience into HackerPrep credits (and help the community)

  • One-liner: you can earn credits by submitting real interview questions you encountered.
  • Why this matters now: noisy, low-quality, and AI-generated submissions are increasing across platforms—so moderators are (rightly) raising the bar.
  • What this post covers: how submissions work, what gets accepted, and how to write submissions that pass review.

What “credits” are on HackerPrep (and what you can use them for)

On HackerPrep, credits are an in-platform currency you earn for approved community contributions (including accepted question submissions). You can redeem credits for in-product perks—commonly things like premium access, content unlocks, mock interviews, or other platform benefits.

  • See current redemption options: /credits
  • Important: credits are granted only after review and approval, not immediately when you click submit.

That “approval-first” model is intentional. It protects the community from spam, keeps the question bank useful, and aligns incentives: you get rewarded for high-signal submissions, not volume.

What counts as a “real interview question” (and what doesn’t)

A question is “real” if:

  • You personally received it during a real hiring process (OA, recruiter screen with technical component, phone screen, onsite/virtual onsite, take-home, etc.).
  • You can provide credible context: company (or anonymized), role/level, round type, and the nature of the evaluation.

Allowed formats (and often preferred):

  • A paraphrased prompt in your own words
  • Key requirements and constraints
  • One or two examples you remember (for coding)
  • What the interviewer cared about (edge cases, complexity targets, tradeoffs)

Not real / typically not accepted:

  • Purely hypothetical prompts you made up to “be helpful”
  • “Heard from a friend” or “I saw it on a forum”
  • Scraped lists from the web
  • Copy-paste from paid sources, courses, or question banks

Special case: the well-known classic. If your prompt is clearly a famous LeetCode-style problem, it can still be accepted—but usually only if you add the missing value:

  • Company + round + level context
  • Any variation (constraints, streaming input, memory limits, concurrency, follow-up changes)
  • What was evaluated (communication, correctness under pressure, complexity reasoning)
  • Enough detail to avoid being a low-signal duplicate

If you need a refresher on what “classic” problems interviewers pull from (and how they’re evaluated), /blog/mastering-coding-interviews-essential-algorithms-and-data-structures-you-must-know pairs well with this.

Before you submit: safety, legality, and interview integrity

The goal is to help people learn patterns and skills—not to enable “memorize-and-dump” cheating.

Use these rules as your baseline:

  • Do not include confidential/proprietary details (internal tool names, private datasets, unreleased product plans).
  • Avoid violating NDAs. When in doubt, paraphrase instead of reproducing word-for-word.
  • Do not upload screenshots or attach take-home PDFs.
  • Do not link to private materials (internal docs, non-public URLs).

Why the strictness? Because integrity risks are rising everywhere in technical evaluation. When cheating attempts and low-quality submissions spike, platforms respond by tightening acceptance criteria. That’s good for serious candidates: higher-signal practice material and less noise.

How to submit interview questions on HackerPrep (step-by-step workflow)

  1. Find the submission entry point

    • Use the in-app navigation (typically under a “Contribute” or “Community” area), or go directly to: /submit-interview-questions

  2. Choose the question type

    • Coding / System Design / Behavioral / Debugging / Other

  3. Fill in required metadata (this is where authenticity starts)

    • Company (or select “Anonymized” if needed)
    • Role/level (e.g., new grad, mid-level, senior)
    • Round type (OA, phone, onsite, virtual onsite)
    • Date/season (approximate is okay—e.g., “Fall 2025”)
    • Location/remote
    • Difficulty (your best estimate)

  4. Write the prompt

    • Include key requirements and constraints you were given.
    • For coding: define inputs/outputs and add at least one example if you can.

  5. Add tags/topics

    • Coding: arrays, graphs, DP, greedy, bit manipulation
    • Systems: caching, queues, backpressure, rate limiting, consistency
    • Behavioral: conflict, ownership, ambiguity, leadership

  6. Submit and track status

    • Typical states: Pending → Needs changes (optional) → Accepted/Rejected → Credits issued

The acceptance checklist: what gets accepted

Moderators aren’t looking for perfect prose. They’re looking for signal.

1) Authenticity signals

Accepted submissions usually include:

  • Coherent context (company/round/level)
  • Realistic constraints (time limits, scale, resources)
  • A prompt that reads like something an interviewer would actually say

2) Clarity

  • The problem statement is unambiguous
  • Inputs/outputs are defined (for coding)
  • Success criteria are explicit (“What does ‘correct’ mean?”)

3) Completeness

  • Constraints and edge cases you were told (or clearly implied)
  • Performance targets if they were discussed
  • For system design: scale assumptions and what matters (latency, cost, reliability)

4) Originality / uniqueness

  • Not a near-duplicate of an existing HackerPrep entry
  • If it overlaps with a classic, it adds meaningful variation/context

5) Anonymization done right

  • No names/emails/recruiter details
  • No private URLs
  • No internal jargon unless it’s public and widely documented

6) Educational value

The best submissions teach how to think:

  • What the interviewer was probing
  • Common pitfalls
  • Follow-ups you got
  • Tradeoffs (especially for system design)

7) Anti-spam / anti-“AI slop”

Moderation teams look for:

  • Consistent formatting
  • No hallucinated details (e.g., mismatched constraints)
  • No keyword stuffing
  • No suspicious mass-submission patterns

Common reasons submissions get rejected (so you can avoid them)

These are the failure modes that consistently tank acceptance:

  • Too vague: “Design Twitter” with no requirements, scale, or success criteria
  • Missing constraints/examples: no input/output, no scale, no acceptance rubric
  • Duplicate/near-duplicate: a standard classic with no differentiating details
  • Copy-paste behavior: paid content, copyrighted lists, scraped question dumps
  • Sensitive details included: codenames, proprietary metrics, interviewer names, attachments
  • Low-effort AI-generated text: inconsistent requirements, unnatural phrasing, fabricated context

If you’re writing system design submissions, it helps to align with a consistent structure like the one in /blog/system-design-interview-essentials-from-concepts-to-execution.

Write submissions that get accepted: templates you can copy

You don’t need fancy writing—just a reliable structure.

Coding template

  • Prompt: paraphrase in 3–6 sentences
  • Inputs/Outputs: what the function receives/returns
  • Constraints: sizes, value ranges, time limits, memory limits
  • Examples: at least one
  • Edge cases: empty input, duplicates, negatives, overflow, etc.
  • Follow-ups you were asked: optimizations, streaming variant, “what if memory is limited?”
  • Expected complexity targets: what the interviewer pushed toward

System design template

  • Goals / non-goals
  • Scale assumptions: QPS, data size, latency targets, SLAs
  • APIs: endpoints or events
  • Data model: key entities and indexes
  • High-level architecture: major components and data flow
  • Bottlenecks: what breaks first
  • Tradeoffs: consistency vs availability, latency vs cost
  • Observability: metrics, logs, tracing
  • Failure modes: retries, idempotency, backpressure

Behavioral template

  • Prompt: the question as asked (paraphrased)
  • What they were probing: ownership, conflict, prioritization, etc.
  • Strong answer outline: STAR beats raw storytelling
  • Common pitfalls: vague outcomes, no reflection, blaming others

For more behavioral structure, see /blog/acing-behavioral-interviews-how-to-showcase-your-problem-solving-skills-and-team-fit.

Debugging template

  • Symptom: what was wrong
  • Environment: language/runtime, OS, dependencies
  • Repro steps: minimal and clear
  • Logs/errors: redacted
  • Expected behavior: what “good” looks like
  • What fixed it: the root cause and the change

Examples: accepted vs. rejected (mini case studies)

Example A (Accepted)

A paraphrased coding question from a recent OA:

  • Includes input/output, constraints (n up to ~200k), and at least one example
  • Mentions a follow-up: “Can you do it in one pass with O(1) extra memory?”
  • Notes what was evaluated: correctness, complexity reasoning, and edge-case handling

Why it passed: it’s specific, complete, and credible, even if the underlying pattern is familiar.

Example B (Rejected)

“One sentence: ‘Find the longest substring without repeating characters.’”

Why it failed: near-duplicate classic with no context (company/round), no constraints, no follow-up, no value-add.

Example C (Accepted)

System design prompt:

  • “Design a rate limiter for an API gateway” plus explicit scale (e.g., 50k RPS bursts)
  • Mentions SLAs and correctness expectations
  • Includes non-goals (no user-auth redesign)
  • Captures follow-ups about multi-region and consistent enforcement

Why it passed: the scale + rubric + follow-ups turn a common topic into a high-signal interview artifact.

Example D (Rejected)

Submission includes a take-home PDF screenshot and a private link.

Why it failed: safety/legal risk and too close to distributing protected materials.

Review process: what happens after you hit submit

Most submissions go through a predictable moderation pipeline:

  1. De-dupe check: is this already in the bank (or extremely close)?
  2. Quality check: clarity, completeness, educational value
  3. Safety/legal check: sensitive info, attachments, NDA risk
  4. Formatting pass: light edits for readability and tagging

Possible outcomes:

  • Accepted as-is
  • Accepted with minor edits
  • Returned for revisions (if the workflow supports “Needs changes”)
  • Rejected (usually for duplicates, copy-paste risk, or missing signal)

Timeline expectations: review time varies with volume, but a reasonable expectation is a few days up to ~2 weeks. High volume, unclear prompts, or submissions that require safety review can slow things down.

When credits post: credits are issued after acceptance, and you’re typically notified in-product (and/or via account notifications).

Pro tips to earn more credits over time (without spamming)

  • Prioritize recency and specificity. Recent rounds with clear constraints are usually the most valuable.
  • Submit variations and follow-ups. “What changed” is often the real signal.
  • Bundle context. Level + round type + what the interviewer emphasized makes a question more useful than the raw prompt.
  • Quality over quantity. Two excellent submissions beat twenty vague ones.
  • Keep an ‘interview notes’ doc. Write prompts down right after interviews while constraints and follow-ups are still fresh.

FAQ

Can I submit if I don’t remember the exact wording? Yes. Paraphrase accurately and clearly label anything you’re unsure about. Exact wording is less important than correct requirements and constraints.

Do I have to name the company? Not always. If naming the company increases risk (NDA, confidentiality, or personal comfort), anonymize. But provide as much safe context as possible (industry, role level, round type, approximate date).

Can I submit questions from an OA? If you personally took the OA, it can be acceptable—as long as you’re not copying protected content verbatim or uploading screenshots/attachments. Paraphrase and focus on skills and constraints.

Why was my submission marked duplicate? Duplicates aren’t only exact matches. If your question is a near-clone of an existing entry, it may be rejected unless you add differentiating context (variation, constraints, follow-ups, rubric).

Can I resubmit after rejection? Often, yes. If you can address the reason (add constraints, remove sensitive info, clarify uniqueness), revise and resubmit. If the platform provides “Needs changes,” use that path—it’s the fastest route to approval.

Conclusion + CTA

Submitting real interview questions is a win-win: you earn HackerPrep credits, and the community gets a fresher, higher-signal question bank that reflects what’s actually being asked right now.

Before you submit, do a quick self-check: real, clear, complete, anonymized, original, useful.

Ready to contribute? Start with 1–2 high-quality questions here: /submit-interview-questions — and review redemption options at /credits.

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