Onsite Interview Questions
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Google ML System Design: Fuzzy Video Deduplication
Design a real-time fuzzy video deduplication system using embedding models and ANN search. Learn tradeoffs, scalability, and appeal workflows—prepare for interviews.
Google System Design: Twitter Hashtag Aggregator Guide
Practice Google's backend system design: real-time Twitter hashtag aggregator. Learn ingestion, aggregation windows, DB trade-offs and interview follow-ups.
Home Depot API Design Interview: Surface & Contracts
Design a complete HTTP API surface and contracts for a Home Depot backend: list endpoints, request/response schemas, docs outline, and client pseudocode.
Home Depot Object-Oriented Design: Tool Rental System
Prepare for Home Depot OOD interviews: design a scalable Tool Rental System handling inventory, deposits, overtime fees, payments and receipts. See tips.
IBM Coding Question: Min Insertions to Form 'abc' Problem
Minimize insertions to convert a lowercase string into repeated "abc" blocks. Learn a greedy two-pointer approach, complexity, and interview follow-ups.
Implement k-Fold Cross-Validation From Scratch — Uber
Implement k-fold, stratified, and time-series CV from scratch for ML evaluation. Includes split contracts, reproducibility, and aggregate metric. Read on to prepare.
Intuit Coding Question: Sum of Palindrome Modification Costs
Sum minimum single-character changes to make every substring of a DNA string a palindrome. Learn two-pointer and combinatorics approaches for Intuit coding prep.
Intuit CS Foundation: Database Pagination Techniques
Master pagination in MySQL and DynamoDB: LIMIT/OFFSET, ROW_NUMBER, keyset (seek) and LastEvaluatedKey. See performance trade-offs, optimizations, and examples.
Intuit ML Foundation: Model Optimization Interview
Intuit ML Foundation model optimization: fine‑tuning, hyperparameter tuning, architecture trade-offs, AutoML, and validation metrics. Practice with examples.
LinkedIn Behavioral Interview: Project Management Skills
Prepare for LinkedIn behavioral project management questions on task prioritization, deadline management, and stakeholder communication — practice answers & examples now.
LinkedIn ML: Large-Scale Streaming Mean & Variance
Compute population mean and variance in one pass over massive float streams. Includes mergeable, numerically stable summaries for distributed ML systems — try it.
LinkedIn ML System Design: Real-Time Nearby Recommendations
Build a low-latency, scalable ML system to recommend nearby places in real time. Get architecture, dataflow, personalization tips, and interview follow-ups.
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