FAANG
Big Tech and product companies
Large product companies running a standardised, centrally calibrated loop: an algorithmic screen, one or two design rounds scaled to level, and a behavioural round scored against published principles.
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From deep technical fundamentals to complex system architecture. Access expertly engineered interview scenarios and grading rubrics designed to bridge the gap between where you are and where you want to be.
Calibrated against the engineering standards of industry-leading companies
Stop relying on vague advice. We demystify the hiring process with transparent, data-driven scorecards that evaluate your technical depth and behavioral signals.
Live Rubric Evaluation: Watch your answer get mapped directly to staff-level scoring criteria.
Identify Red Flags: Learn the exact phrases that trigger immediate rejections.
Compare with the Standard Model: See how textbook answers fail to demonstrate actual seniority.
Distributed Caching Strategy
Positive Signals (What Interviewer Scores)
Common Red Flags (Where Points Are Lost)
Most platforms hand you the answer. Preptima hands you the scorecard.
Don't study for Google if you're interviewing at a Series A startup. We calibrate the rubrics specifically to the employer tier you are targeting.
No generic puzzles. Face production scenarios exactly as a Staff Engineer would: scaling challenges, 3 AM outages, and migration complexities.
Learn the exact 4-5 signals interviewers evaluate. Avoid the hidden traps and learn how to navigate the inevitable deep-dive follow-ups.
| Platform | Coding Puzzles | System Design | DevOps & Data Tracks | Employer Archetypes | Explicit Scoring Rubrics | Production Scenarios |
|---|---|---|---|---|---|---|
| Preptima | ||||||
| Other Platforms |
A real loop is several different examinations wearing one name. These are orthogonal tags rather than folders, so a question can be scenario-based and senior and hard without being duplicated into three places.
A realistic situation you must diagnose, decide on, or work through out loud.
Open-ended architecture or component design with trade-off defence.
Direct knowledge checks on how something works and why.
Extended business or product case worked end to end.
Write or debug working code, usually in a shared editor.
Past-experience questions scored against a competency rubric.
Recruiter-round logistics, motivation, and compensation questions.
Asynchronous assignments and what reviewers actually grade.
Medium is where most candidates are actually separated, so that is where the weight sits.
One question is often asked at several levels; the bar shows how many carry each.
Everything you need to master your specific domain.
Different companies weight rounds differently. Select your employer tier to inspect how their hiring panels calibrate candidate rubrics.
Large product companies running a standardised, centrally calibrated loop: an algorithmic screen, one or two design rounds scaled to level, and a behavioural round scored against published principles.
The offshore engineering arm of a global bank, insurer, retailer, telco, or product company, hiring to own a product area rather than to staff a project.
Firms that sell delivery capacity and advisory work to clients, where the interview is partly technical assessment and partly a rehearsal of how you will sound in front of the client paying for you.
Small product teams hiring for end-to-end ownership, where the loop is short, idiosyncratic, and usually anchored on a take-home or a paired session in the actual codebase.
Engineering craft, the languages and platforms on top of it, the leadership and product rounds that decide senior outcomes, and the industry domains that separate two technically equal candidates.
A curated path through the sections that matter for one job, so you are not deciding what to study before you have started.
The full loop for a JVM backend role: coding, Java and Spring depth, databases, service design, and the behavioural round.
Browser-platform depth, framework rounds, a frontend system design round, and the accessibility and performance questions that follow.
Both ends of the stack at moderate depth, with the integration and API-boundary questions specific to full-stack loops.
Pipelines, Spark and warehousing, SQL depth, and the modelling and orchestration questions specific to data platforms.
Infrastructure, pipelines, Kubernetes, and the reliability and incident-response judgement that defines the SRE round.
People scenarios, delivery judgement, hiring calibration, and retained technical credibility across a mixed loop.
Stop guessing what the hiring committee wants. Download our definitive guide containing the exact Probes, Traps, and calibration matrices used by Principal Engineers in modern system design interviews.
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The dialog was built as a styled div, so nothing marks the rest of the page inert. Focus was never moved in, never constrained, never restored on close, and Escape was never wired. A correct dialog moves focus in, keeps it in, restores it, marks background inert and is labelled - which native dialog with showModal gives you almost for free.
A catch handler that returns normally converts a rejected promise into a fulfilled one, so every then after it runs as though nothing failed. Combined with fetch, which only rejects on network errors and resolves happily on a 500, the chain reports success twice over. Fix by re-throwing from catch, checking response.ok, and modelling UI state as a machine.
Treat it as a confirmed breach until proven otherwise: preserve logs, scope who saw what, and start the disclosure clock alongside the investigation. The cause is almost always tenant scoping living in application code, where one forgotten filter leaks everything - so move enforcement below the code, to row-level security.
A timeout requests cancellation; it does not stop code. CancelledError is raised at the task next await point, so a task inside blocking code, shielded, in an executor thread, or swallowing the exception keeps running and commits its side effects late. Fix by re-raising CancelledError, keeping blocking calls off the loop, and using asyncio.timeout with a TaskGroup.
Make the write idempotent before you make it correct: partition-level replace rather than append, so rerunning a day produces the same result whether it runs once or five times. Then work out how far the contamination spread, because a downstream table that accumulated rather than recomputed will not fix itself when the source is repaired.
The contract captured the shape the consumer sends, not the rules the provider enforces. Optionality lived in the schema while the real requirement lived in provider validation, and no recorded example ever omitted the field. Contract tests only verify interactions they contain, so add the negative example and verify against real code.
Everything you need to know about how Preptima accelerates your interview preparation.
While most platforms focus entirely on algorithmic puzzles and binary 'pass/fail' outcomes, Preptima mirrors the reality of a modern hiring loop. We recognize that senior interviews are won on nuance, not rote memorization. For every scenario, we provide the hidden scoring rubric—the specific 'Probes' interviewers use to measure your depth, and the 'Traps' that reveal a lack of production experience. You learn how to defend your architectural decisions rather than just reciting a textbook answer.
The 'one-size-fits-all' interview is a myth. A Big Tech hyperscaler (like Meta or Google) utilizes highly calibrated, algorithm-heavy loops to measure abstract problem-solving. Conversely, an IT Consultancy values stakeholder management and broad architectural breadth, while a Series A startup heavily weights operational instinct and end-to-end ownership. Preptima dynamically re-weights your daily study blocks based on the specific archetype you are targeting so you are not studying for Google when you are interviewing at a startup.
Every question in the Preptima bank originates from genuine production environments and actual hiring loops. Instead of asking you to simply define a distributed cache, we present the scenario exactly as a Staff Engineer would face it: 'You have a massive cache stampede at 3 AM. Your database is recovering, but your service is not. What do you do?' This forces you to navigate ambiguity, capacity estimation, and graceful degradation just as you would on the job.
No. A modern tech organization relies on a diverse ecosystem of specialists, and our curriculum reflects that reality. Beyond exhaustive Frontend and Backend engineering tracks, Preptima features deep, dedicated coverage for DevOps and Cloud Reliability (SRE), Data & AI Engineering, QA Automation, Product Management, and Technical Presales. Each discipline has its own specialized rubrics and case studies.
An Entry-Level developer and a Principal Architect are never scored on the same matrix, even if they are asked the exact same question. Preptima tags every question by seniority level and explicitly outlines how expectations scale. Where a junior candidate is expected to produce working code, a senior candidate is graded on how well they articulate the blast radius of their design, their understanding of multi-tenant isolation, and their ability to own cross-team technical strategy.
Join thousands of engineers mastering the modern tech interview with Preptima's production-grade scoring rubrics.