Real Interview Questions Leaked from 805+ Companies

Why leaked questions beat curated practice problems

Every major prep platform sells you the same thing: a curated list of algorithmic problems chosen for their educational value. LeetCode has 3,000+ problems. Grind 75 tells you which 75 matter most. NeetCode adds video explanations. None of them tell you what Google's L5 system design loop actually looked like last month.

That is what leaked questions solve. When a candidate posts on 1Point3Acres that their Stripe phone screen asked them to design a rate limiter and then walk through the webhook delivery guarantees, that is a primary source. No prep platform invented that question — Stripe asked it, a candidate reported it, and now you know.

The information asymmetry between candidates who have access to leaked reports and those who do not is real. Referrals already come with insider prep. Candidates who know someone at the company get briefed on what to expect. LeakCode is the version of that for everyone else.

How LeakCode aggregates interview leaks

Candidate interview reports are scattered across seven communities, most of which are paywalled, non-English, or difficult to search. LeakCode runs a continuous scraping and enrichment pipeline that collects, normalises, and surfaces these reports in one place.

After collection, every entry goes through quality filters: junk removal (spam, unrelated posts), duplicate detection across sources, company-name normalization (e.g. "GOOG", "Google LLC", and "Google" all resolve to the same company profile), and a quality score that gates display. Only posts that clear all filters appear on company pages.

See the full methodology at /methodology and the source list at /sources.

Coverage by company — top 30 by question count

The table below shows the 30 companies with the most reported questions and experiences in LeakCode's database. Click any company to browse its full question list, filtered by role and round.

#	Company	Questions	Experiences
1	Amazon	2,432	3,373
2	Google	1,098	2,606
3	Meta	953	1,951
4	Microsoft	702	1,277
5	Bloomberg	281	548
6	Uber	425	312
7	ByteDance	84	120
8	LinkedIn	185	481
9	OpenAI	42	45
10	Salesforce	78	112
11	Apple	81	191
12	Oracle	71	200
13	Accenture	18	223
14	DoorDash	94	109
15	Stripe	68	56
16	Adobe	70	128
17	Anthropic	26	14
18	Atlassian	49	82
19	Databricks	49	40
20	Paypal	88	101
21	IBM	76	120
22	Snowflake	66	50
23	Airbnb	68	37
24	Netflix	67	42
25	Coinbase	29	41
26	NVIDIA	24	61
27	Rippling	41	20
28	Cisco	71	137
29	Accolite	106	1
30	Roblox	28	30

View all 805+ companies →

Coverage by source

LeakCode aggregates from seven primary sources. Each contributes a different type of intel: LeetCode tends toward structured algorithmic questions, 1p3a has the most detailed full-loop interview reports, Reddit has the most candid salary and outcome context, and Blind has the strongest signal on culture and process.

See full source details at /sources. Counts reflect post-filter (non-junk, non-duplicate) entries as of May 2026.

What "leaked" means — and what it does not

"Leaked" is the term the candidate community uses for interview questions that appear in public posts after the interview. It does not mean stolen from company systems or obtained through unauthorized access. It means a candidate sat through an interview, remembered a question (or a few), and posted about it online.

This has happened on forums since at least 2005. 1Point3Acres was built around it in 2010. LeetCode Discuss has thousands of company-tagged threads. Reddit's r/cscareerquestions has detailed loop reports going back years. The activity is ubiquitous, legal, and accepted. Glassdoor and Indeed have interview question sections that function identically.

What makes LeakCode different from browsing those sources manually is the aggregation: every post is timestamped, company-normalized, role-tagged, and deduplicated. A question that appears in three separate Reddit posts and two 1p3a threads surfaces as a single entry with a frequency count of five, not as five separate posts buried across different communities.

Frequency is the signal that curated lists cannot replicate. When twelve different candidates report that Amazon's bar-raiser round includes a Leadership Principles deep-dive on "ownership," that pattern — visible only through aggregation — tells you something that no single post can.

How to use leaked interview questions effectively

The common mistake is treating leaked questions as a memorization target — study this exact question and expect it to appear. That rarely works. Companies rotate questions and interviewers vary. The value is in the signal, not the exact text.

Here is how to extract the actual signal:

1. 1

   Filter to your target company, role, and round

   Google L5 system design is a different interview from Google L3 coding. Amazon behavioral is different from Amazon system design. The role and round filters exist precisely because the signal differs.

2. 2

   Sort by recency, then look at the top 10–20 entries

   Questions from 2023 are less predictive than questions from 2025–2026. LeakCode defaults to recency-weighted display. Focus on the recent cluster first.

3. 3

   Identify topic clusters, not individual questions

   If six out of ten recent Stripe coding reports mention distributed systems or eventual consistency, that is a topic cluster. Prep depth on that topic — not the exact problem — is the goal.

4. 4

   Read the full candidate experience, not just the question

   The full report often includes what the interviewer cared about, what follow-up questions were asked, and whether the candidate got an offer. That context is as valuable as the question itself and is only available in the unlocked view.

For a deeper breakdown of interview prep strategy by role and company tier, see the FAANG interview prep guide and the system design questions guide.

Browse leaked questions by company

Amazon Google Meta Microsoft Bloomberg Uber ByteDance LinkedIn OpenAI Salesforce Apple Oracle

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