531,459 interview questions from 6,000+ companies.
How would you optimize a machine learning model?
Design a recommendation system strategy for model cold start and new-user cold start, including serving, evaluation, and safe rollout.
Design a recommendation and ranking system that handles cold start for both new users and new items without hurting feed quality.
How to make a model interpretable and explain its predictions to stakeholders.
Tests awareness of experimental threats like bias, leakage, and incorrect randomization.
Tests statistical analysis choices and correct interpretation of A/B outcomes.
Tests data quality checks, validation strategy, and handling of dirty real-world data.
Tests experimental planning and power analysis for reliable A/B conclusions.
Tests hypothesis testing application and ability to make evidence-based product calls.
Tests production risk awareness, evaluation gaps, and operational mitigation strategies.
Tests understanding of regression feature selection, leakage avoidance, and generalization.
Tests planning, prioritization, and stakeholder management under time constraints.
Tests data preprocessing judgment and robustness techniques for real-world datasets.
Tests root-cause analysis using data, segmentation, and causal reasoning for product health.
Tests metric design, measurement validity, and alignment to product goals.
Tests system design for streaming, NLP/analytics, and operational reliability at scale.
Tests scalability, training strategy, and practical constraints for large-scale ML.
Tests data validation, correctness checks, and safeguards against bad inputs.
Tests prioritization, alignment, and execution under changing product or stakeholder needs.
Tests modeling choices for unstructured or relational data and practical feature/model design.
55 total questions
