Interview Questions

S S. - "Seamless user experience Consistent UI Data mapping Technical architecture of the 2 platforms and their compatibility API integrations Security and compliance factors to consider, impacting from the integration. Feature parity — to carry over features, enhance features, drop features etc Load testing, performance testing and end-to-end testing post integration"See full answer

Tony C. - "That I had not been a PM for a formal ML product. But good models need sound data and my five years of sql writing and ensuring that DWHs and marts had the data they needed for reporting would be very relevant"See full answer

Surbhi G. - "Ensure immediate safety: The first priority is to secure the vehicle and its occupants by stopping the engine, activating hazard lights, and unlocking doors. Assess the situation: The car evaluates the extent of damage, determines its location, and checks on the status of passengers. Call for help: Based on the assessment, the car calls emergency services if necessary and notifies the manufacturer. Collect and store accident data: This is crucial for later analysis and potential legal pu"See full answer

Nitish C. - "All purpose cluster remains up and running for longer duration irrespective of the job hence preferred for notebooks, adhoc work whereas job cluster spins up as per the submitted job and shuts down post the completion hence preferred for production scheduled workloads as it also offers compute isolation"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

Shibin P. - "Effective loss functions for computer vision models vary depending on the specific task, some commonly used loss functions for different tasks: Classification Cross-Entropy Loss:Used for multi-class classification tasks. Measures the difference between the predicted probability distribution and the true distribution. Binary Cross-Entropy Loss:Used for binary classification tasks. Evaluates the performance of a model by comparing predicted probabilities to the true binary labe"See full answer

Lucas G. - "I'd recommend to adjust p-values because of the increased chance of type I errors when conducting a large number of hypothesis. My recommended adjustment approach would be the Benjamini-Hochberg (BH) over the Bonferroni because BH strikes a balance between controlling for false positive and maintaining statistical power whereas Bonferroni is overly conservative while still controlling for false positives, it leads to a higher chance of missing true effects (high type II error)."See full answer

Dominique H. - "Starbucks"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