PM, AI PM and AI Design Interview Questions (2026): 141 Reported Questions and Where Each Was Asked

Contents

1. Ten to prepare first
2. How we built this list
3. What changed in 2025–26
4. The question bank
5. Part 1 · Product management
6. Product sense
7. Metrics & goals
8. Root cause
9. Execution & trade-offs
10. Estimation
11. Strategy
12. Technical
13. Behavioural
14. Part 2 · AI product management
15. AI product sense
16. LLM technical depth
17. Evals & AI metrics
18. AI strategy
19. Responsible AI
20. AI behavioural & mission
21. Part 3 · AI design
22. AI design challenge
23. Uncertainty & trust
24. Errors, control & agents
25. AI system design
26. Portfolio & conversation
27. What Indian companies ask
28. How each company's loop works
29. Frameworks that work
30. What separates offers
31. Prepare by building
32. A four-week prep plan
33. FAQ

141 reported interview questions for PM, AI PM and AI design roles, and where each was asked

We collected questions that candidates report being asked at Google, Meta, Amazon, OpenAI, Anthropic, Flipkart, Swiggy, Zomato and more: 50 companies in all. Each one lists the company, a link to the source, the year and what the interviewer is actually testing.

1. Ten to prepare first
2. How we built this list
3. What changed in 2025–26
4. The question bank
5. Part 1 · Product management
6. Part 2 · AI product management
7. Part 3 · AI design
8. What Indian companies ask
9. How each company's loop works
10. Frameworks that work
11. What separates offers
12. Prepare by building
13. A four-week prep plan
14. FAQ

Picture the new Product Sense with AI round at Meta's Central Products group. You spend 30 minutes designing a product. Then you're asked to build a working prototype of it with an AI tool while the interviewer watches. Another candidate, at Google DeepMind, is asked how Gemini should decide when to step in proactively. A third, at Swiggy, is asked to estimate how many cups of tea Bengaluru drinks in a day.

All three were reported by candidates within the last year. Together they show where product interviews are in 2026. The classic PM loop hasn't gone away. An AI layer has been added on top of it, and for AI roles that layer is now most of the interview.

• The classics still dominate. "Favourite product", "metric dropped X%" and "tell me about a difficult stakeholder" remain the most-answered questions on Exponent in 2026.
• AI questions now appear in regular PM loops. Amazon, Microsoft, DoorDash, Netflix and Lyft now ask how you use AI in your own work, including for roles that have nothing to do with AI.
• The top AI PM themes are how you use AI day to day, how you evaluate AI output, and what safeguards you put on agents that act for users. "Explain RAG" is widely reported too, but mostly in engineering loops.
• Building live is now part of the loop. Meta's Central Products group and Google's Gemini team ask PMs to prototype with AI during the interview.
• Indian loops still lean on RCA and guesstimates. AI-specific questions at Indian companies are rarely documented publicly yet.

00 Ten questions to prepare first, if you only have an evening

1. What's your favourite product, and why? Tagged at 300+ companies on Exponent
2. Tell me about a time you handled a difficult stakeholder. Google, Amazon, Microsoft, Apple, Stripe
3. Tell me about a time you failed, or made a mistake. Google, Amazon, Meta, LinkedIn, Airbnb
4. YouTube comments are up but watch time is down. What do you do? Google/YouTube; 3 reports, last ~Jan 2026
5. How would you measure success for Facebook Events? Meta's Analytical Thinking round
6. As a PM at Swiggy, orders went down 20% last week compared with all previous weeks. How would you do the root cause analysis? Swiggy; the Indian version of the metric-drop round
7. You own an internal AI platform that other teams build agents on. What's your strategy, and if you could own only one metric, what would it be? The top question on Exponent's AI bank in 2026
8. How do AI evals work? What metrics do you use to judge LLM output quality? Intuit, Amazon, Visa, Snap, LinkedIn, Google
9. How would you design safeguards for an AI system that takes actions on a user's behalf? How would you define its permission and control model? OpenAI, Amazon, Meta, Google, Netflix, Stripe
10. How do you use AI day to day? The most-reported AI question on Exponent in 2026 (11 reports)

01 How we built this list (and how far to trust it)

Most "top 100 PM interview questions" articles are lists nobody can check. We wanted every question to answer three things: who asked it, where that claim comes from, and how strong the evidence is.

We went through the public candidate-reported question banks on Exponent and PM Exercises, company guides from IGotAnOffer (which compiles Glassdoor reports), first-hand write-ups of interview loops, Blind threads, coach newsletters, and official hiring pages from Amazon, OpenAI, Anthropic and Razorpay. Where a question circulates under a company's name but we couldn't trace it to a real report, we either left it out or marked it as weak.

Read the tags honestly. "Asked at Google" means one or more candidates said they were asked it there. Platforms like Exponent don't verify that. Question wording is also often paraphrased by the person reporting it. Treat each question as a very good sign of the kind of thing a company asks, not a promise that it will come up. Years are approximate because most banks show "asked 9 months ago" rather than a date.

A few things we found along the way. A widely shared "Zepto" question about raising average order value is actually tagged to Uber and Nykaa. "Should Razorpay build its own UPI app?" is a practice case written by a blogger, not a reported interview question. Several "Swiggy AI PM question" pages appear to be generated content with no source. None of them are in this list.

02 What changed in PM interviews in 2025–26

1. An AI round was added to the PM loop. Meta's Central Products group has piloted a "Product Sense with AI" round since around November 2025: about 30 minutes of classic product sense, then 30 minutes building a prototype with an internal Llama-based tool. Lenny Rachitsky called it the first major change to Meta's PM loop in over five years. You're graded on how you steer and critique the AI, not on clever prompts.
2. Building is tested live. Google's Gemini loops include a live prototyping segment where you build your MVP on screen and defend your prompts. Microsoft's AI teams run similar rounds for senior PMs, and Palo Alto Networks uses a take-home AI prototype.
3. "How do you use AI?" is now a standard question. It is tagged at Meta, Amazon, Netflix, Snowflake, Robinhood, HubSpot and Sierra. Amazon has added it to its Leadership Principles rounds under "Invent and Simplify".
4. Companies are also cracking down on AI use during interviews. Amazon's guidelines allow candidates to be disqualified for using generative AI during an interview unless it's permitted. Google is bringing back at least one in-person round because of AI-assisted cheating (Sundar Pichai, 2025). Anthropic's published candidate policy bans AI in take-homes and live interviews unless stated. The rule of thumb: use AI when the round asks you to, and prove your fundamentals without it everywhere else.
5. Rounds were renamed. Meta's "Execution" round is now "Analytical Thinking", and its Central Products loop adds a "Product Architecture" round. Google added a work-style assessment before the loop and moved team matching earlier.

03 The question bank

The 141 questions are split into three parts, and each part is grouped by the round the question usually appears in. Filter by track, or search for a company ("Swiggy", "OpenAI"), a topic ("evals", "RAG") or a round ("root cause"). The full bank is free for Garage Labs members: sign in once and it unlocks.

What's your favourite product, and why?

• Google
• Apple
• Amazon
• Meta
• Microsoft
• Uber
• Spotify
• Stripe
• Flipkart

What it tests: Product taste. Say who the product serves, which design choice makes it work, how it makes money, and one thing you would change. Pick something less obvious than the iPhone.

Source Exponent: Amazon PM bank · Prepfully: Flipkart guide · Evergreen, still reported 2026

You have text-to-music capability. How would you productise it? (Siblings: an animal translator, a 'memory machine'.)

• OpenAI

What it tests: OpenAI's signature format: 'we have magic technology, what do we build?' Go from capability to users, to the first use case, to go-to-market, and cover misuse. Interviewers give little guidance, so impose structure early.

Source Exponent question page · Exponent candidate experience · Dec 2025 – Mar 2026

Design an AI product that helps you discover books.

• OpenAI (Product Designer)

What it tests: The whiteboard format is 'an app that does X, with AI'. Show the AI does real work rather than being bolted on: taste modelling, cold start, 'why this' explanations, a feedback loop.

Source Exponent: OpenAI Product Designer guide · 2025–2026

Sign in to unlock all 141 questions

• 63 PM, 59 AI PM and 19 AI design questions, grouped by interview round
• Where each one was asked, the source link, the year and what it tests
• Filter by track and search by company or topic (Flipkart, OpenAI, evals…)

04 What Indian companies ask

If you're interviewing in India, here's what the evidence shows.

• RCA and guesstimates are the core of the loop. A first-hand account of a 2020 Flipkart loop describes five rounds of 60–90 minutes, each mixing root cause analysis, a guesstimate, strategy and behavioural questions. Swiggy, Zomato, Paytm and Ola follow the same pattern, and the guesstimates are local: tube lights in Bangalore, tea in Bengaluru, Swiggy orders per hour.
• Operations-heavy cases. Partial deliveries at Zomato, rice returns in grocery, delivery-partner apps. Indian consumer companies run physical operations, so their cases do too.
• Assignment rounds are common. Meesho (per its Director of Product) and CRED both use a take-home or assignment discussion. Meesho's loop runs Hiring Manager, Problem-Solving, Assignment, then CPO, with CEO and business leaders for senior roles.
• Razorpay published its own rubric. Five dimensions: Product Thinking (defend a mini product spec), Problem Solving, Business Acumen, Technology Grounding and Strategic Thinking. It's still the most useful company-written guide from an Indian company.
• AI questions are rarely documented yet. The only Indian-company AI PM question we found with an "asked at" tag is Flipkart's recommendation-system question. Job descriptions are clearer than interview reports. Sarvam AI's PM listings, for example, ask for people who have built agents and understand context windows, evals, tool calling, cost versus latency versus accuracy, and failure modes. Prepare for those topics even if nobody has posted the exact questions.

For an Indian product role in 2026, prepare as you would for a Meta-style execution loop (metrics, RCA, trade-offs), add ten practised India-specific guesstimates, and have one AI story ready that you can go deep on. Interviewers increasingly ask how you use AI, even for roles that aren't AI roles.

05 How each company's loop works

Rounds as reported by candidates and prep platforms for 2025–26. Loops vary by team and level, so confirm the details with your recruiter.

06 Frameworks that work (and which round to use them in)

07 What separates candidates who get offers

Across coach write-ups, recruiter guides and candidate debriefs, the same patterns come up.

1. They lead with evals. A 2026 recruiter guide for hiring AI PMs calls eval-set design and instincts for production failures "the single biggest separator". Strong candidates have an opinion on LLM-as-judge versus human raters versus structured rubrics.
2. They design for failure. OpenAI coaches at IGotAnOffer call ignoring the probabilistic nature of AI output the biggest red flag. Every AI answer needs a "what happens when it's wrong" section.
3. They've built something. Interviewers now ask to see your prompt library or GitHub, and Meta and Gemini test building live. A generic answer about using ChatGPT for writing documents doesn't count.
4. They know when not to use AI. LinkedIn asks directly how you choose between a rule, traditional code and an LLM. Saying "a rule would do this better" earns credit.
5. They design for the next model. Anthropic's take-home asks how your feature changes once the model improves, and OpenAI's "10x capability at 10x cost" tests the same thinking.
6. They're candid. Anthropic's culture round and OpenAI's "what don't you like about us" both reward honest, specific opinions over a polished pitch.

08 Prepare by building: the Applied AI Product Management programme

This bank tells you what gets asked. Section 07 tells you what gets offers: evals, designing for failure, knowing when not to use AI, and having built something you can defend line by line. You can't get those from reading a question list, including this one. You get them by shipping AI products and having your decisions challenged.

That is how we built the Applied AI Product Management: Advanced Practitioner Program. It's a 12-week programme for experienced PMs moving into AI product leadership. Every week produces a working build and a written artifact (a PRD, a memo, an eval report, an architecture diagram). It ends with a live 0-to-1 capstone that you defend in front of a panel of practitioners.

What interviewers ask, and what you'll have built

Built for you if

• You're a PM, Senior PM, Product Lead or Product Owner with 5+ years of experience
• You're fluent in discovery, PRDs, prioritisation and experimentation
• You're technically curious, even if you aren't technical

Not the right fit if

• You want a beginner AI-tools or prompting course
• You have fewer than 5 years of product experience
• You can't commit to weekend live sessions plus weekday build time for 12 weeks

No programme can promise an offer, and we don't. What it gives you is evidence interviewers probe for: builds you can open on screen, eval reports with numbers, and practice defending your decisions to a panel. The certificate is awarded only after you clear the assessments and the live capstone defence. There's no attendance-only route. LLM API credits for every build are included.

09 A four-week prep plan

1. Week 1: the classics Two product-design questions, two metrics questions and two RCA questions from Part 1, out loud, timed at 35 minutes each. Write six STAR stories and map each to two company values.
2. Week 2: AI fundamentals you can explain Be able to explain RAG, context windows, fine-tuning, hallucinations and evals to a non-technical person in two minutes each. Then do the "Explain how RAG works" and "How do AI evals work?" cards out loud.
3. Week 3: build one thing Ship a small AI feature end to end: a prompt, a 30-example eval set, one failure you found and fixed. This becomes your answer to "How do you use AI?" and your live-build practice. (If you want this at depth, with feedback, it's what the programme does for 12 weeks.)
4. Week 4: company-specific Filter this bank by your target company. Read its mission material (the OpenAI Charter, Anthropic's Core Views, Amazon's Leadership Principles). Do two full mock loops with a peer.

10 FAQ

By volume of candidate reports: "What's your favourite product and why?", "Tell me about a difficult stakeholder", "Tell me about a mistake you made", "Metric X dropped Y%, what do you do?" and "How would you measure success for [product]?" For AI roles, add "How do you use AI?", "How do AI evals work?" and how you'd put safeguards on an agent that acts for users.

The structure is similar (product sense, execution, behavioural) but there's added depth on how LLMs behave, evals, cost and latency, and safety. Frontier labs add company-specific formats: OpenAI's "we have magic technology" prompts, Anthropic's written take-home and culture round, and DeepMind's AI round led by an engineer.

Usually not traditional coding. But live prototyping ("vibe coding") with AI tools is now part of some loops, including Meta's Central Products group and Google Gemini, and technical rounds expect you to reason about system design, batching, latency and model choice.

Mostly root cause analysis ("orders fell 20% last week"), India-specific guesstimates ("Swiggy orders per hour") and market strategy ("launch groceries on Flipkart"). Filter the bank above by company name to see every reported question with its source.

Google's PAIR People + AI Guidebook, Microsoft's 18 Guidelines for Human-AI Interaction (HAX), Apple's Human Interface Guidelines for machine learning and generative AI, and the Shape of AI pattern library. They make good lenses for critique questions and design challenges.

Only when the round explicitly allows it. Amazon can disqualify candidates for unauthorised use, Anthropic bans it in take-homes and live interviews unless stated, and OpenAI says some formats allow AI while others deliberately don't. Using AI to prepare is fine, and Anthropic explicitly encourages it.

Go into your next loop with something you've built

The Applied AI Product Management programme: 12 weeks, live, at most 30 per cohort, ending in a capstone you defend in front of a panel.

Related reading: AI for product managers · RAG vs OKF: a PM's guide to choosing · 26 types of RAG systems · AI product strategy in 2026. Question sources link to the original banks and write-ups, and none of them are sponsored. Found a question that's mis-attributed? Tell us at [email protected] and we'll fix it.