Interview Questions

Misho S. - "California is a big state and it has roughly 35 million in population, so if we assume there is a gas station for every 500 people so there would be roughly 35,000,000 divided by 500 equal to 70,000 gas station allover the state"See full answer

Rudransh V. - "I got it right"See full answer

Exponent - "This is one of the core behavioral questions that you should expect to cover in any interview. In particular, it asks you to justify why you want to work at a specific company that you've applied for. There's no right answer for this, however we do recommend you list at least three distinct reasons. Here's an example of what you might say: > That's a great question. There are three main reasons why I'd want to work on Twitter's Ads team:Building an impactful product > Working on marketplaces > M"See full answer

Vicky X. - "4000"See full answer

Exponent - "This is a Strategy Question, which asks you to justify high-level business decisions and strategy. Before jumping in, we should evaluate which type of strategy question we're attempting to answer and propose multiple reasons or solutions. Let's go over one possible solution: > There's a few reasons why Facebook may have built the portal. My top three reasons would be:Investing in smart home > Owning more upstream value > XR Integration > Investing in smart home > One hypothetical reason"See full answer

Amparo L. - "In my free time I like to watch iCarly videos."See full answer

Exponent - "This is another Diagnosis problem. To answer this question, we suggest you use our framework (along with the TROPIC method) to be as thorough as possible. The framework is as follows: Ask clarifying questions List potential high level reasons Gather Context (TROPIC)Time Region Other features / products (internal) Platform Industry / Competition Cannibalization Establish a theory of probable cause Test theories Propose solutions Summarize "See full answer

Anonymous Hornet - "label #shape = [batchsize, posids] X #shape = [batch_size, ch] hidden = nn.Linear(batchsize, ch) #shape = [batchsize, ch] logits = nn.Linear(ch, C) #shape = [ch, C] #modifications start here neg_ids = torch.randint(0, C, 0.1 * C) #e.g. if we want to randomly sample only 10% of C outputneg = np.dot(logits[:, negids], hidden(X)) #shape [batch_size, C1] outputpos = np.dot(logits[:, posids], hidden(X)) #shape [batch_size, C2] output = torch.concat(outputpos, outputneg) #concatenate out"See full answer

Toufeeq K. - "To build my career in a lead organization."See full answer