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How to Submit Real Interview Questions (and Earn Credits): High‑Signal Templates for Coding + System Design

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11 min read

HackerPrep is building a question bank you can actually trust—because the future of hiring is increasingly about signal quality, not volume. With assessment fraud reportedly more than doubling in 2025, the industry is shifting toward clearer evaluation criteria and better‑vetted prompts. The same “trust tightening” is happening across community judging and moderation: specific, reproducible submissions win; vague prompts get filtered.

That’s why HackerPrep asks for real interview questions (anonymized and paraphrased) and why we’re opinionated about templates. A template forces the details that make a prompt usable: constraints, inputs/outputs, and what the interviewer actually evaluated.

If you’re practicing for interviews at companies like /company/google, /company/stripe, or /company/snowflake, you already know the difference between “leetcode‑ish vibes” and a prompt that reflects a real bar. To align your submissions with how candidates prepare, this post gives you (1) an end‑to‑end submission flow and (2) copy/paste templates for coding and system design.

For prep context, you may also want to cross‑reference how we think about practice depth in /blog/mastering-coding-interviews-essential-algorithms-and-data-structures-you-must-know and how to structure design answers in /blog/system-design-interview-essentials-from-concepts-to-execution.

Suggested tags: submissions, community, interview-questions, system-design, coding-interviews, templates, hackerprep-credits

Intro: Why HackerPrep is asking for real interview questions (and why templates matter)

The problem: most shared interview prompts online are low signal. They’re missing the “spec”: constraints, edge cases, input/output format, timebox, and how solutions were judged. That creates two failures:

  • Candidates can’t practice realistically because they’re forced to guess requirements.
  • Reviewers can’t validate quality because “correctness” is ambiguous.

What HackerPrep wants instead: anonymized, structured, reproducible prompts that reflect real interviews—what you personally received (or administered), paraphrased from memory, with enough detail that another engineer could attempt it under interview conditions.

What you’ll get from this post: a step‑by‑step submission flow and high‑signal templates for coding and system design prompts, plus examples of what “high signal” looks like.

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

A real interview question is something you personally:

  • received in a screen or onsite (coding, system design, debugging, etc.), or
  • administered as an interviewer.

Capture it from memory and paraphrase it.

Allowed context to include

Include details that help others reproduce the experience without revealing proprietary material:

  • Round type: phone / virtual / onsite
  • Role level: intern / junior / mid / senior / staff (approximate is fine)
  • Timebox (e.g., 30/45/60 minutes)
  • Language/environment constraints (e.g., “any language,” “C++ preferred,” “no external libs,” “shared editor”)
  • Evaluation style: pairing vs solo, heavy hints vs no hints, test-driven vs discussion-heavy

Not allowed

  • Verbatim prompt text copied from proprietary platforms or internal docs
  • Screenshots, recordings, or attachments from private portals
  • Links to private assessment pages
  • Anything you’re not permitted to share (NDA/confidential materials)

Goal: preserve learning value while removing identifying/proprietary details.

Why “high-signal” matters more in 2026 (trust, AI, and ambiguity)

The direction of technical hiring is clear: trust and clarity are winning.

  • Higher cheating/fraud pressure means prompts must have stronger specs and evaluation anchors. If the question is underspecified, it’s easier to “pattern match” with AI or memorized solutions.
  • Vague prompts are easy to game. “Do something scalable” doesn’t measure much. A prompt with explicit constraints and tradeoffs forces reasoning.
  • Constraints and specs beat vague prompts—a 2026 content trend for a reason. Constraints force candidates to justify complexity choices, failure modes, and edge behavior.

Contributor takeaway: your job isn’t just to submit “the idea.” Your job is to submit:

  1. the spec (inputs/outputs/constraints), and 2) how it was evaluated (rubric + interviewer pushback).

How HackerPrep submissions work (end-to-end)

Step 1: Choose prompt type

  • Coding: algorithmic, data structures, debugging, parsing, etc.
  • System Design: architecture, scaling, reliability, storage, APIs, etc.
  • Combined round: submit both if the interview flowed from coding → design or design → implementation details.

Step 2: Provide metadata

Metadata makes questions searchable and comparable:

  • Role level
  • Time limit
  • Difficulty (your best estimate)
  • Topic tags (e.g., arrays, graphs, caching, queues, consistency)

Step 3: Paste prompt using the template

Use the template sections below as literal headings. The more you fill in, the less reviewers must infer.

Step 4: Add evaluation notes

This is what most submissions miss—and what makes yours valuable:

  • What interviewers cared about
  • Common pitfalls you saw (or fell into)
  • What follow-ups changed the problem

Step 5: Submit → review queue → approval/publish

After you submit, it goes into review.

What happens after:

  • You may get a clarification request (“What were the input bounds?”)
  • You may get suggested edits (tighten the spec, add examples)
  • Or it may be rejected with reasons (duplicate, too vague, policy violation)—use that feedback to resubmit stronger.

How credits are earned (and how to maximize them)

Credits are tied to quality and completeness, not raw volume.

Typical credit triggers

While exact programs evolve, credit systems usually reward:

  • Approved submissions (meets quality + policy bar)
  • Editor-accepted revisions (you improved the prompt after feedback)
  • High engagement (e.g., “helpful” marks, saves, discussion—if/when applicable)

What reduces/blocks credits

  • Low-detail prompts (missing IO/constraints/rubric)
  • Unverifiable claims (“this was asked everywhere” without details)
  • Policy violations (verbatim/IP/confidential materials)
  • Duplicate questions already in the library

Pro tip: submit fewer, better questions. A great template-filled submission often beats five vague ones—and is faster to review.

High-signal CODING prompt template (copy/paste)

Paste this into your submission and fill it in.

A — Metadata

  • Role level: (intern/junior/mid/senior)
  • Round type: (phone/virtual/onsite)
  • Timebox: (e.g., 45 min)
  • Language/environment: (any language / specific language preferred)
  • Allowed libraries: (e.g., standard library only)
  • Format: (pairing/solo; interviewer hints: low/medium/high)
  • Topics/tags: (e.g., hashing, two pointers, DP, graphs)

B — Problem statement (paraphrased)

(3–6 sentences. Avoid company names/products. State the task and what “done” means.)

C — Function signature / IO format

  • Inputs: (types, shape)
  • Output: (type)
  • Example 1: input → output + 1–2 lines explanation
  • Example 2: input → output + explanation

D — Constraints

  • N range:
  • Value bounds:
  • Time expectation: (e.g., should pass in O(n log n) or better)
  • Memory expectation:
  • Must/should requirements: (e.g., stable order, no sorting, streaming)

E — Edge cases checklist

Check all that apply and note expected behavior:

  • Empty input
  • Single element
  • Duplicates
  • Negative values / overflow risk
  • Large values
  • Ties / multiple valid answers
  • Invalid input (if discussed)

F — Expected approach

  • Target complexity:
  • Acceptable alternatives:
  • What optimizations were rewarded: (e.g., early exits, avoiding extra passes)

G — Follow-ups

(1–3 follow-ups you were asked or would ask.)

H — Evaluation rubric (what they scored)

  • Correctness
  • Complexity
  • Communication/clarity
  • Testing strategy
  • Debugging under time
  • Tradeoffs/alternatives

I — Solution outline (optional but high value)

A brief algorithm sketch + invariants. No need for full code.

High-signal SYSTEM DESIGN prompt template (copy/paste)

A — Scenario + objective

  • Scenario: (one-line product)
  • Goal: (one-line measurable objective)

B — Requirements

  • Functional (must-have):
  • Out of scope:
  • Non-functional: latency, availability, consistency, cost, privacy/compliance

C — Scale numbers

(Estimates are fine; label them as such.)

  • DAU/MAU:
  • Read QPS / Write QPS:
  • Peak factor:
  • Data retention:
  • Object sizes:

D — APIs + core entities

  • Core endpoints/events:
  • Request/response shape (high level):
  • Key domain objects:

E — High-level architecture

  • Components (clients, services, storage, queues)
  • Data flow
  • Read path and write path

F — Data model + storage choices

  • Tables/keys/indexes
  • Partition/shard strategy
  • TTL/archival
  • Hot keys and mitigation

G — Caching + performance

  • What to cache
  • Invalidation strategy
  • CDN usage (if applicable)
  • Backpressure / load shedding

H — Reliability

  • Retries + idempotency
  • Queues/streams + DLQ
  • Replication/failover
  • DR plan (RPO/RTO)

I — Consistency tradeoffs

  • Where strong vs eventual consistency is required
  • Conflict resolution strategy

J — Security + abuse

  • Authn/authz
  • Rate limiting
  • PII handling
  • Audit logs

K — Observability

  • Metrics + SLOs
  • Logs + tracing
  • Alerting

L — What the interviewer pushed on

  • Bottlenecks
  • Cost drivers
  • Top 2 risks
  • How you defended decisions

Examples of “high-signal” vs “low-signal” submissions (show, don’t tell)

Coding: low-signal example

“Given an array, find the longest streak of consecutive numbers. Return the length.”

Why reviewers can’t validate it:

  • No definition of “consecutive” (difference 1? strictly increasing?)
  • No IO format or examples
  • No constraints → can’t judge expected complexity
  • No evaluation notes → unclear what the interviewer cared about

Coding: high-signal rewrite (same idea)

Problem (paraphrased): Given an unsorted list of integers, return the length of the longest set of distinct values that form a run of consecutive integers (…x, x+1, x+2…). Order in the input does not matter.

  • Input: list of integers nums
  • Output: integer length
  • Example 1: [100, 4, 200, 1, 3, 2] → 4 (run is 1,2,3,4)
  • Example 2: [1, 2, 2, 3] → 3 (duplicates don’t extend run)
  • Constraints: n up to ~200k; values in 32-bit signed range
  • Expected: ~O(n) average time using hashing; sorting solution discussed as acceptable but less ideal
  • Edge cases: empty list → 0; all duplicates
  • Rubric notes: interviewer emphasized explaining why each number is processed O(1) times and testing duplicates

System design: low-signal example

“Design Twitter.”

Why it’s low signal:

  • No scope (timeline vs full product)
  • No numbers (QPS, fanout, retention)
  • No non-functional priorities (latency vs consistency)
  • No evaluation anchors (what tradeoffs matter)

System design: high-signal rewrite (narrowed + evaluable)

Scenario: Design a “home timeline” service that shows recent posts from accounts a user follows.

  • Scope: timeline generation + read API; exclude messaging, search, ads
  • Scale (est.): 20M DAU, peak 150k read QPS, 10k write QPS, 30-day retention
  • NFRs: p99 read latency < 200ms; high availability; eventual consistency acceptable for timeline freshness within ~seconds
  • Push points: fanout strategies, celebrity accounts, cache invalidation, backpressure, DR

Now reviewers can judge whether the prompt is realistic and whether a candidate can practice it.

Anonymization + compliance checklist (before you hit submit)

  • Remove company/team names, internal code names, and unique product identifiers
  • Don’t paste exact wording from proprietary question banks or take-home docs—paraphrase and restructure
  • Avoid sharing non-public metrics; use approximate ranges and label them estimates
  • If unsure, add a note: “Approximate numbers; paraphrased from memory.”

Quality rubric HackerPrep reviewers likely apply (use it as your self-check)

  • Reproducibility: another candidate can attempt it without guessing missing details
  • Specificity: constraints + IO + success criteria are explicit
  • Realism: matches a real interview bar (timebox + difficulty + follow-ups)
  • Signal: tests reasoning and tradeoffs, not trivia
  • Safety: respects confidentiality and avoids proprietary verbatim content

FAQ: Common submission questions

“What if I don’t remember exact numbers?” Provide ranges and label assumptions (e.g., “~10k QPS peak (estimate)”). Rough, consistent numbers are better than none.

“Can I submit a question I found online?” Aim to submit prompts from your personal interview experience (received or administered). If policy allows “inspired by,” be explicit—but avoid copying.

“Do I need to include a solution?” Optional, but a solution outline dramatically improves approval odds because it proves the prompt is coherent and scoped.

“How long until credits arrive?” Credits generally gate on review + approval. Fastest path: use the template, include examples, and add evaluation notes so reviewers don’t need follow-ups.

Conclusion: one great prompt beats five vague ones

High-signal submissions make the community’s practice sets more trustworthy—and they align with where hiring is going: clearer specs, stronger rubrics, less ambiguity to exploit.

Your next step: submit your next real interview question using the template above. Aim for one excellent prompt this week—with constraints, examples, and “what the interviewer pushed on.”

Credits reward clarity and completeness, and templates are the fastest path to both.

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