Interview prep

Google Interview Questions Banned For Being Too Tough (and How To Answer Them)

Tactical strategies for tackling estimation questions.

If you've browsed through TeamBlind or Glassdoor, you’ve probably heard some of the crazy interview questions Google (and other tech companies, like Amazon or Microsoft) famously asked:

  • How many manhole covers are there in San Francisco?
  • What would you charge to wash all of the windows in Seattle?
  • How many golf balls can fit in a school bus?

Outwardly, candidates often respond with frustration, anxiety, or an insistence that more information was needed. Inwardly, they were probably thinking: What does that have to do with search engines? How would I even begin to figure these things out? 

Eventually, these questions were perceived as so tough and unfair that the company banned them. So why are they important?

Why should you learn to solve these?

If you won't be asked these questions, why should you learn to solve them? Upon first glance, these brainteasers don't really relate to your role: they have nothing to do with product management or software engineering. However, they test skills that would make you a good "Googler", like problem solving and an ability to communicate your thought process.

Additionally, they might be more common than you think. The dirty little secret is that Google (and everyone else) still asks these questions, they just make them sound less whacky by relating them to actual business problems, like, “estimate the cost to Google to provide free storage to all Pixel phone users."

Whether you face these questions during the phone interview or the onsite, we want you to be prepared. Here's what the interviewer is looking for.

👉 If you want to learn about more standard questions (think data structures, binary trees, or discussing your favorite google product) check out this guide + these questions.

What response is the interviewer looking for?

To put it simply: start making educated guesses. This may feel unnatural to do during a job interview— after all, isn’t it wrong to guess without asking clarifying questions? Actually no — typically, the interviewer won’t provide any clarifying information, so asking for more information is seen as a distraction or stalling technique.

If you recognize these questions as Fermi problems, you’re already halfway there. That’s why asking these questions can be a little unfair — most people have never heard of Fermi, and have been trained by the education system to try and get every answer exactly right. But that’s not how this game is played.

Fermi Estimates

What is a Fermi estimate?

Enrico Fermi was a physicist famous for being able to make accurate measurements with little or no actual data — like the time he estimated the strength of an atomic bomb test (a highly classified secret at the time) by dropping a few scraps of paper and making a few rough calculations in his head.

How did he do it? Well by observing how far the scraps of paper traveled at the time of the blast, making a few assumptions, and understanding how to calculate the change in volume, he guessed right within 10 kilotonnes.

This method has been used in all sorts of domains since, including helping us calculate the number of potential alien civilizations in the galaxy. Believe it or not, it’s used every day by product managers, software engineers, marketers, and business people to model out the potential impact of decisions before they're made. Odds are, the team members you meet during the interview process have probably used Fermi estimates in their real work. 

Why does guesstimation work?

When you’ve been trained your whole life to take careful, accurate measurements for your calculations, it can be hard to believe these sloppy guesses are useful in any way. Obviously, this feels different than a technical interview or coding interview. But in real life, we rarely need to calculate a number precisely — and there is a cost to more precision.

Here's an example:

Take one of the most famous Fermi questions — “How many piano tuners are there in New York City?” If you were building software for piano tuners to book appointments, would it really matter if the exact answer is 27 and you guessed 28? Not at all!

But if you could be reasonably confident that there were less than 50 or closer to 500, you’d know whether there was a big enough market without incurring the cost of building your product. Sure you could turn to Yelp and see there are about 55, but what about the ones not listed? Do we count the ones that can tune a piano but only do it for friends and family? Have some of these listings gone out of business? What about businesses that have more than one piano tuner on staff?

To get a truly accurate number, we’d have to do an inordinate amount of research, investigating each listing one by one and tracking down anyone who can tune a piano — it’d be prohibitively expensive and maybe even be impossible to get the true number. Thankfully we don’t need it — we can make a series of educated guesses and get to a ‘good enough’ number right now.

And do you know what? Weirdly, Fermi estimates actually do end up being pretty accurate. Maybe it’s the brain’s remarkable ability to make rapid estimates, or potentially with natural variance, our over-estimations end up canceling out our underestimations. But when you do enough of these, be prepared to be quietly impressed with how accurate your numbers get.

How do you put this method into practice?

Let’s tackle the piano tuner question together, so you see how it works. The key is to break the problem into smaller steps. For example, to arrive at an estimate we need to guess the population of New York City, how many people own pianos, how many appointments a piano tuner can keep in a day, amongst other things. As you break these steps down, fill in your best guesses for each stage.

Assumption A — 8 million people in New York City

According to Google, the real number is 8.3, but assuming you’re on the spot you can go with 8. What matters is not the 0.3 difference, but knowing it’s more than 5 million and less than 10.

Assumption B — 3 people in a household

To get to this assumption, I remembered that there are 2.2 kids on average per couple (family size = 4.4), and then assumed that roughly half of people had no kids (2 + 4.2 / 2 = 3.1). I wasn’t far off — the United States has an average household size of 2.6.

Assumption C — 5% of households have a piano

This is just a wild guess, based on personal experience, so this was the estimate I was least confident about. I arrived here by thinking about which of my friends' families owned pianos — I could only think of 1 in 20, so I guessed 5%. I struggled to find good data on this, other than only 30,000 pianos are made every year, down from 100,000 in 2005. So it’s definitely not going to be north of 10% with a population of 300 million, even if we assume pianos last a long time.

Assumption D — 4 appointments per day

How many appointments can they get to in a day? I guessed 4 here, thinking it’d take about an hour to tune the piano, and then accounted for travel time. The true figure is about 4 to 6 — and it’s okay for us to be on the low side as some piano tuners won’t be doing this full time.

Now we have all the main assumptions we need to make to figure this out. You can hopefully see we’re allowing ourselves to be relaxed about our assumptions — we can, of course, Google each one and plug in the real numbers if we prefer, but we actually got pretty close with our guesses in this case.

If you plug in these assumptions to an Excel or Google sheet, this is what you get:

We guessed 167 piano tuners in New York City — Yelp says there are 55, but it’s reasonable to assume that some of these listings employ multiple tuners, and when you account for anyone that might be unlisted, we’re not far off!

The important takeaway here is this: in a domain that we know nothing about, with some reasonable guesses and assumptions, and simple math, we can arrive at a realistic estimate of the number. If our new business venture or software product needed more than 1,000 piano tuner customers to break even, we now know that’s wishful thinking.

A Real Life Example for Google

These days, Google interview questions won't look as abstract as the example we just ran through. Likely, they'll ask about one of their products (like Gmail or AdWords) or business practices. So, in this example, we'll cover a real life Fermi question Google continues to ask in their interview: “How much would it cost Google to store all photos taken on Android phones this year?”

To answer this question, you run through exactly the same process:

  • Break the problem into smaller steps
  • Make the key assumptions needed
  • Calculate the final number

The first key assumption you’d need to make here is the number of people who have an Android phone (work backward from Android market share, smartphone penetration, and world population). You’d also need an estimate for how many photos people take per day, and the size of those photos, as well as some assumptions on Google’s cost to store a gigabyte. Here’s how it could all look when you’re done.

Now we can’t really check the actual value (this probably isn’t even disclosed to most Google employees), but I guarantee you — give an answer like this in an interview, explain your reasoning as you go, and the hiring manager can’t help but see the value.

What to do when you get it wrong?

The great thing about making a Fermi estimation and being wrong is that you learn something very precise. Because the method forced you to break the problem into smaller steps and put your assumptions down on paper, you now know which exact assumption was wrong, and by how much. This is a rare moment where you can see clearly what you thought before, and how far you were off from reality — this is a fantastic learning opportunity and can help inform your future strategy.

Here's an example:

If you were launching a new mobile app game, you could estimate the number of users you would get in year one. You’re confident you’re making a game so good it’ll go viral — the first initial test users will tell their friends, who will tell their friends, and so on. You can actually model this out, by making a series of assumptions on the number of invitations per user, and conversion rate from the invitations.
Now before you launch your app, you know what needs to be true to hit your numbers. Compare your assumptions to popular benchmarks, and see if it’s even going to be realistic to achieve your goals. Commonly, you find out that even in the best scenarios, you can’t possibly make enough money for this project to be worth it — you just saved yourself a lot of heartbreak.

After launch, if your initial data shows users are only inviting five people each, or the conversion rate on the app store page is lower, you have an early warning you’ll be way off. It also helps you concentrate on fixing the right problem.

If all of your other assumptions are correct, except your app store conversion rate is 20% instead of 40%, you know you need to focus on that. Rewrite the copy, add compelling new screenshots of the app, or add testimonials, awards, and quotes from the press.

Hopefully, you can see the perks of these back-of-the-napkin calculations. This isn’t just a hack or useful method, but a whole other way of thinking. By being willing to be imprecise, you’re free to get to a ‘good enough’ answer and move on.

One big warning — this method works for most problems, but does tend to break down where virality is involved. You can see in the model below how small changes can make a huge difference due to exponentiality. If your job is placing bets on exponential growth, you should err on the side of generosity, lest you pass on the next Facebook, like Andrew Chen famously did.

👉 Want more practice with Fermi questions? Check out this spreadsheet where you can reverse-engineer all the problems mentioned in this post (and many more!).

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