Interview Questions

Anonymous Muskox - "Clarification Question What kind of product is a software product or hardware product? Who are we a tech company or Google, how does it is fit into google's mission? Constraint or something? The goal is of adoption User persona Parents of kIds who don't want to eat fruits Young working professional who is busy in their life and they don't have the habit or they forget to eat fruits and vegetables Health enthusiast and Yoga teacher who is very keen to see different kinds of fruits"See full answer

Exponent - "This is a Technical question. It tests your ability to understand high level technical concepts. Even though your job won't have any coding involved, you'll still need to understand these concepts. Being able to cover all these topics with clarity communicates confidence in your interviewer. Unfortunately, there's no formula for technical questions, but some general tips are: Use analogies when you can Break your solution into clear, bite-size steps Don't be afraid to use examples to b"See full answer

Exponent - "This is an Improve a Product question that's quite specific. Here, we're asking to not only improve Slack, but particularly how they allow collaboration on their platform. 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"See full answer

1036 loknadh R. - "No ,MSE is suitable for only regression modes. Although the logistic regression in Its name has regression , but it is a classification problem so MSE is not suitable for classification models like logistic regression."See full answer

Anonymous Starfish - "Increase the Viewer count ; Simply the process of uploading videos > increasing the amount of videos on YouTube> increasing the amount of data the can be collected by creators > leading more viewers"See full answer

Joohi M. - "Get an audience with the big player - 1) Talk about the camera tech KPI that your company has improved/ 2) Talk about the technology itself 3) Talk about how it's better than the big player's competitors 4) Show a demo through a few benchmarks 5) Promise the big company that your company will support their product end to end if integrated into their product 6) Try to differentiate on pricing 7) Also show how the big player can win more in the market - maybe more user acquisition, or more engage"See full answer

Sinchita S. - "Statistical power is defined as the probability that a test will correctly reject a false null hypothesis. In other words, it is the likelihood of detecting an effect (e.g. a real difference between two groups) if one actually exists. It is typically set to 80% meaning that 80% of the time we will can correctly detect a difference between the groups. It is also a critical component of calculating the correct sample size for an experiment. Let's say if we conduct an experiment on a very small sam"See full answer

Ashwin K. - "My strategy has always been to observe the existing systems and people in a company that I go into for 2-3 months before making any drastic changes. In this time, I build relationships one on one and understand each role in the overall system. I also put each of these stakeholders at ease by letting them know that I'm not a threat and have approachable discussions. Once I reach a comfortable level, I pose questions and ask them for solutions. That way, I have seen that the problem also get solve"See full answer

Anonymous Panda - "SDLC stands for Software Development Life Cycle, which is a process used by software development teams to design, develop, test, and deploy high-quality software products. There are several SDLC models, including the Waterfall model, Agile model, and DevOps model. Here's an overview of each model and how I would implement it: Waterfall Model: This model is a linear sequential approach, where each stage of the software development process must be completed before moving on to the next stage. T"See full answer

Prachi G. - "The solution produces the same result as the 'prescribed solution' yet it does not get accepted In the test results section transcript['year'] = transcript['year'].astype(str) df = pd.pivottable(data = transcript, index = 'studentid', columns = 'year', values = 'yearlygpa', aggfunc = 'mean').resetindex() df = df[(df['2021'] < df['2022']) & (df['2022'] < df['2023'])] df['average_gpa'] = df[['2021', '2022', '2023']].mean(axis=1).round(2) return df "See full answer

Anonymous Stork - "AWS"See full answer