Interview Questions

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

Sean L. - " import pandas as pd from datetime import datetime def findfastestlike(log: pd.DataFrame) -> pd.DataFrame: log=log.sortvalues(['userid','timestamp']) #get the prev event, time by user log['prevevent'] = log.groupby('userid')['event'].shift(1) log['prevtimestamp'] = log.groupby('userid')['timestamp'].shift(1) True only on rows where the previous event was a login and the current event is a like log['loginlike'] = (log['prevevent'] == 'log"See full answer

Alessandro R. - "import java.util.*; public class NetworkTopology { public int topologytype(int N, int M, int[] input3, int[] input4) { if (M != N - 1 && M != N) return -1; // Fast check for invalid cases int[] degree = new int[N + 1]; // Degree of each node (1-based indexing) // Build the degree array for (int i = 0; i < M; i++) { degree[input3[i]]++; degree[input4[i]]++; } // Check for Bus Topology boolean isBus = (M"See full answer

Exponent - "This is an Improve a Product question. Let's first go over the Improve a Product formula: Ask clarifying questions Identify users, behaviors, and pain points State product goal Brainstorm small improvements Brainstorm bolder improvements Measure success Summarize Now, let's begin! Ask clarifying questions Before we begin listing off recommendations, it's important you ask questions to ensure you and the interviewer are on the same page"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

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

Basant P. - "When launching a new product at Koovs, I began by conducting a thorough competitor analysis to ensure our pricing strategy was competitive. I then defined the target audience based on demographic and psychographic data. We built a comprehensive multi-channel campaign strategy that included SEO for visibility, targeted SEM ads, and engaging social media content. I used tools like Google Analytics to monitor campaign performance and adjusted our tactics based on the data. For instance, we increase"See full answer

Exponent - "This is a Strategy Question, which asks you to justify high-level business decisions and strategy. With many acquisition strategy questions, we can first look at core competencies and synergies before offering more complex reasoning. Let's go over one possible solution: > There's many different reasons why Twilio acquired SendGrid, my top three reasons would be:Owning more of the communications network > More data > Upselling to SendGrid customers > Owning more of the communications netw"See full answer