Is HireStepX free to use?

Yes. Start with 2 full AI mock interviews, complete with real-time feedback, scores, and detailed performance reports. No credit card required.

How does the AI mock interview work?

Upload your resume, pick your target company and role, and choose from 10 interview types. The AI interviewer asks role-specific questions via voice, listens to your answers, asks follow-up questions, and delivers scored feedback after each session.

What types of interviews can I practice?

10 types: Behavioral, Technical, Strategic, Case Study, Campus Placement, HR Round, Panel, Management, Salary Negotiation, and Government/PSU. Each with 3 difficulty levels and mini or full session options.

Can I practice for specific companies like TCS, Infosys, or Google?

Yes. We support 200+ target companies including Google, Amazon, TCS, Infosys, Flipkart, Razorpay, McKinsey, Deloitte, and more, each with distinct interview patterns.

How is this different from ChatGPT or practicing with friends?

ChatGPT is text-only with no voice, no scoring, no resume integration, and no progress tracking. HireStepX is a purpose-built interview simulator: voice-based, resume-personalized, with detailed analytics.

Is my interview data private and secure?

Yes. Data is encrypted via Supabase with row-level security. Recordings and transcripts are never shared with employers or third parties. Delete everything anytime from Settings.

Does HireStepX work on mobile?

Yes, on any modern browser. For the best experience during mock interviews, use a laptop or desktop with a microphone.

How much does it cost compared to a career coach?

A single coaching session typically costs ₹3,000-10,000. HireStepX is ₹9 per session or ₹39/month for 5 sessions (Sprint Pack) with full AI coaching and analytics, available 24/7.

What is the recruitment process at Google?

The typical Google recruitment process has 4 stages: Recruiter screen and online assessment or phone screen → Technical phone or virtual coding round → Onsite loop of multiple coding rounds plus behavioral → Hiring committee review, then team match and offer.

What are the interview rounds at Google?

Google typically conducts 3 interview rounds: Round 1 (45 min): phone or virtual coding round on one to two DSA problems.; Round 2-4 (45 min each): onsite coding rounds on graphs, DP, and arrays or strings.; Round 5 (45 min): behavioral and Googleyness round on collaboration and impact..

What framework should I use for Google technical interviews?

HireStepX recommends the Clarify-Brute-Optimise framework for this type of interview: Clarify inputs and constraints, state the brute force and its complexity, optimise while narrating the key insight, then code cleanly and analyse

Given a stream of integers, design a data structure that returns the median in O(log n).

To answer this question well, HireStepX recommends the Clarify-Brute-Optimise approach: Clarify inputs and constraints, state the brute force and its complexity, optimise while narrating the key insight, then code cleanly and analyse Ground your answer in a specific real example from your own experience.

You have a service that's slow only on Tuesdays. Walk me through how you'd debug.

To answer this question well, HireStepX recommends the Clarify-Brute-Optimise approach: Clarify inputs and constraints, state the brute force and its complexity, optimise while narrating the key insight, then code cleanly and analyse Ground your answer in a specific real example from your own experience.

You're the Staff Engineer rolling out Cursor/Copilot to a team of 25 with mixed seniority. What's your 90-day plan and how do you measure success?

To answer this question well, HireStepX recommends the Clarify-Brute-Optimise approach: Clarify inputs and constraints, state the brute force and its complexity, optimise while narrating the key insight, then code cleanly and analyse Ground your answer in a specific real example from your own experience.

You launch a feature and DAU rises but session length falls. Design an analysis to decide whether the feature is net positive. What metrics and tests do you use?

To answer this question well, HireStepX recommends the Clarify-Brute-Optimise approach: Clarify inputs and constraints, state the brute force and its complexity, optimise while narrating the key insight, then code cleanly and analyse Ground your answer in a specific real example from your own experience.

Explain the bias-variance trade-off and how you'd diagnose whether a model is underfitting or overfitting from its learning curves.

To answer this question well, HireStepX recommends the Clarify-Brute-Optimise approach: Clarify inputs and constraints, state the brute force and its complexity, optimise while narrating the key insight, then code cleanly and analyse Ground your answer in a specific real example from your own experience.

Derive why maximum-likelihood estimation for linear regression with Gaussian noise gives the least-squares objective.

To answer this question well, HireStepX recommends the Clarify-Brute-Optimise approach: Clarify inputs and constraints, state the brute force and its complexity, optimise while narrating the key insight, then code cleanly and analyse Ground your answer in a specific real example from your own experience.

How does Google grade coding interviews?

On four axes: problem-solving (how you reason toward the optimal), coding (clean, correct, bug-free), communication (thinking aloud, clarifying), and testing (walking through edge cases). The final answer alone is not enough.

How should I approach a Google coding problem?

Clarify constraints first, state a brute-force baseline with its complexity, then optimise while narrating the insight, code cleanly, and dry-run your solution on an edge case.

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Google Technical Interview Questions India 2026

Google's coding rounds are the most documented in the industry, and the bar in India is the same as anywhere: medium-to-hard algorithmic problems where the interviewer cares as much about how you think as whether you finish.

Practice with AI voice feedback 6 questions · Technical · 2 sessions free

In 2026 expect graph traversal, dynamic programming with memoisation, sliding-window and two-pointer patterns, and clean, bug-free implementation. Strong candidates clarify the problem, state the brute force, optimise while narrating the invariant, and analyse time and space honestly. Communication and structured problem-solving are graded, not just the final answer.

About Google

Alphabet's flagship: search, advertising, Android, Cloud, YouTube, AI infrastructure.

Products: Search · Ads · Android · Google Cloud Platform · YouTube · Workspace · Pixel
Competitors: Microsoft · Amazon (cloud, ads) · Meta (ads) · Apple (mobile) · OpenAI/Anthropic (AI)

Google recruitment process

01
Recruiter screen and online assessment or phone screen
02
Technical phone or virtual coding round
03
Onsite loop of multiple coding rounds plus behavioral
04
Hiring committee review, then team match and offer

What to expect in each round

01
Round 1 (45 min)
phone or virtual coding round on one to two DSA problems.
02
Round 2-4 (45 min each)
onsite coding rounds on graphs, DP, and arrays or strings.
03
Round 5 (45 min)
behavioral and Googleyness round on collaboration and impact.

Technical questions Google asked

Sourced from 2+ candidate post-mortems. Hit Practice to answer any one with AI voice feedback.

01
Hard
Given a stream of integers, design a data structure that returns the median in O(log n).
Practice free →
02
Medium
You have a service that's slow only on Tuesdays. Walk me through how you'd debug.
Practice free →
03
Hard
You're the Staff Engineer rolling out Cursor/Copilot to a team of 25 with mixed seniority. What's your 90-day plan and how do you measure success?
Practice free →
04
Hard
You launch a feature and DAU rises but session length falls. Design an analysis to decide whether the feature is net positive. What metrics and tests do you use?
Practice free →
05
Medium
Explain the bias-variance trade-off and how you'd diagnose whether a model is underfitting or overfitting from its learning curves.
Practice free →
06
Hard
Derive why maximum-likelihood estimation for linear regression with Gaussian noise gives the least-squares objective.
Practice free →

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Stop reading, start answering.

The AI asks Google-style questions, listens to your voice, and scores your answer in two minutes. 2 sessions free, no card.

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Google Technical Interview Questions India 2026