This interview evaluates an AI Engineer's ability to navigate workplace culture, emphasizing growth mindset, customer orientation, and cross-team collaboration.

AI Engineer Interview

This interview revolves around the design of an enterprise-ready feature that summarizes meetings and generates action items, with discussions on Responsible AI, privacy, reliability, and cost.

This interview focuses on influencing stakeholders, improving processes, exceeding project expectations, and handling complex challenges.

Data Analyst Interview

This interview assesses the candidate's ability to utilize data analytics to solve business problems, evaluate marketing strategies, and influence strategic decisions.

This interview assesses a Product Designer's ability to navigate complex product environments and align with company culture, with a focus on leadership, decision-making, and collaboration.

Product Designer Interview

This interview emphasizes comprehensive product thinking, inclusive design, and collaboration applied to real-world problems.

The Microsoft Product Manager Case Study Interview evaluates candidates' strategic thinking and execution within Microsoft's product culture, using a real-world business problem related to a Microsoft product or service. Candidates are expected to analyze the problem, propose innovative solutions, and outline an implementation plan, all while aligning with Microsoft's mission and values.

Microsoft Product Manager Product Sense Interview

The Microsoft Software Engineer Technical Interview evaluates a candidate's problem-solving abilities and their understanding of algorithms and data structures. Candidates should be ready to discuss their thought process, optimize solutions, and handle follow-up questions related to their approach.

Microsoft Software Engineer Technical Interview

This interview assesses leadership, cognitive ability, and role-related knowledge, emphasizing problem-solving, decision-making, cross-functional collaboration, and resilience.

This interview assesses the candidate's ability to design systems and tackle data strategy challenges. It also evaluates their communication and collaboration skills.

This interview evaluates the candidate's ability to make data-driven decisions, manage stakeholders, prioritize tasks, and learn from failures.

Data Analyst Behavioral Interview

This interview revolves around diagnosing and measuring the impact of a feature change on a core product, proposing an experiment plan, and effectively communicating insights.

This interview assesses a Product Designer's ability to balance user needs, business goals, and handle conflicts. It also evaluates their capacity for cross-functional collaboration, decision-making under ambiguity, leadership, and adaptability.

Product Designer Behavioral Interview

A case modeled on real Google interviews. You’ll tackle an ambiguous, user-focused problem in a core Google surface (Maps, Photos, Search, YouTube, or Workspace) and demonstrate end-to-end product design with strong engineering awareness.

Format and timeboxes:
- 0–3 min: Introductions and context setting.
- 3–8 min: Clarify the problem, target users, and constraints at Google scale (billions of users, low latency, reliability, abuse risk).
- 8–25 min: Frame the opportunity (jobs-to-be-done, primary/secondary personas, top use cases). Define success metrics and guardrails (activation, task completion, retention, content quality, abuse rate, accessibility compliance, privacy incidents). Prioritize with simple scoring or ICE and articulate trade‑offs.
- 25–40 min: Explore solution directions. Sketch key flows and states (happy path, error/offline, edge cases). Show how the UI leverages Material Design 3 and adapts across Android, iOS, and web. Call out information architecture, interaction patterns, and how you’d instrument events for A/B testing.
- 40–52 min: Execution depth. Discuss tech feasibility with an engineer’s lens (client vs server, on‑device ML vs cloud inference, performance budgets, offline and internationalization). Outline a v0/v1 roadmap and safe‑launch plan (dogfood, percent ramps, experiment design, alerting/rollback).
- 52–60 min: Recap and Q&A. Summarize the problem, chosen solution, risks, and how you’d measure impact.

What interviewers evaluate (specific to Google’s culture and style):
- Product sense and user empathy: Clear problem framing, focus on user value over features, and principled decision making.
- Systems thinking at Google scale: Reliability, abuse & trust/safety considerations, privacy by design (data minimization, consent), accessibility and internationalization as first‑class constraints.
- Craft and clarity: Simple, legible flows; consistent use of Material Design; clear visual hierarchy and state management.
- Data rigor: Well‑defined north‑star and input metrics, experiment hypotheses, instrumentation plan, and interpretation of trade‑offs.
- Collaboration and Googliness: Humility, curiosity, structured communication, and ability to reason with PM/Eng under ambiguity.

Sample prompts (representative of real cases):
- Design a lightweight, privacy‑first sharing flow in Google Photos for intermittent connectivity markets.
- Help first‑time visitors contribute high‑quality, non‑spam reviews in Google Maps while protecting user safety.
For either prompt, expect to discuss edge cases (abuse, offline, low‑end devices), measurement (what success looks like at week 1 and month 3), and a phased rollout plan.

Google Product Designer Case Interview (Engineering) — Product Sense, Systems Thinking, and Execution

This interview emphasizes problem-solving, design thinking, user-first thinking, accessibility, inclusion, and data-informed decisions.

This interview evaluates a Product Designer's ability to collaborate with team members and provide effective solutions, focusing on problem definition, decision making, and inclusive design.

This interview assesses a candidate's ability to strategize, solve problems, and make decisions through real-world product challenges.

Product Manager Interview

This interview evaluates the candidate's problem-solving abilities and their capacity to articulate their thought process effectively.

Software Engineer Interview

This interview assesses decision-making, leadership, and customer service skills within the realm of AI Engineering.

This interview focuses on the conceptual design of AI systems, including the identification of necessary datasets, model choices, and strategies for product categorization and recommendation.

Practice Interviews

Your career is worth investing in.

Get unlimited referrals with Premium.