Interview Questions

S G. - "Clarification questions: How are these metrics calculated? Get context on why these metrics are important? Are we looking at any specific role? Benchmarking: What is the current time to hire? How does this compare to competitors? Do we have a desired goal/target for these metrics? Problem Solving: I began by analyzing the various steps in the hiring process and the number of days taken at each stage. Soon realised that the bulk of the time is taken in a few steps (80/20). Drilled down further"See full answer

Prachi N. - "My the current KPIs focus on three areas: 1) the number of designs finalized and signed off by the business. 2) the completion of development for those designs, and 3)the quality and quantity of data loaded. To achieve these, I communicate requirements early and provide all necessary information to the business for accurate design sign-off. I deliver documents that are precise and contain all details the technical team needs for development. I also verify system configuration before data loadin"See full answer

Prachi N. - "In my second project first testing phase, I noticed that many teammates were not fully aware of the dependencies between different objects. This caused delays because one team often had to wait for another without realizing it. To address this, I came up with the idea of creating a visual flowchart that mapped out all the dependencies in a simple way. Additionally, I developed a dashboard that showed real-time updates: which dependencies were completed, which objects were still pending,"See full answer

Jagat mohan B. - "Found bug will not impact on the new requirements, if the engineering team aware of the source of the bug and the severity, than it can easily be handled and regression test to perform for a quality check. The new requirement should be equally prioritize for groom and start creating task and team to work on it. Finally based on the severity of the bug you can shuffle the resources between these items."See full answer

Tesfaye M. - "Designing a system to track review abuse on Amazon.com involves detecting fraudulent, manipulative, or biased reviews while ensuring genuine customer feedback isn't mistakenly flagged. Here's a high-level breakdown: 1. Goals Detect and prevent fake or abusive reviews. Maintain integrity and trust in the review system. Support scalability for millions of products and reviews. 2. Key Abuse Scenarios Fake positive reviews (e.g., sellers boosting their own products). Fake"See full answer

Jacky Y. - "Problem scope: Can this system detect Bot in real-time online or offline? Both. Online traffic: 1M DAU. Latency: 2s. Offline frequency: daily Offline data: 2B activity logs. Data: How do we know a Bot player (Label)? Human label. Imbalance data: reweight, resample. Develop a Bot simulator to generate more data offline for training. Given lower weight to simulator data than human label. Features: Signals from different models online. Log all the features for offline. Propose new features: dail"See full answer

Anonymous Mongoose - "The below system design addresses the requirements for a scalable distributed onboarding service, focusing on robust data validation, asynchronous processing, real-time aggregation, and efficient querying of metadata. Core Principles: Asynchronous Processing: Decouple components to ensure high throughput and responsiveness. Scalability & Elasticity: Utilize technologies that can scale horizontally to handle varying loads. Data Durability & Integrity: Ensure no data l"See full answer