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Computer science concepts
Explore algorithm tutorials, coding and system design questions, interview experiences, and computer science study guides.
601 resources · Page 5 of 26
Computer science concepts
Causal Inference And Analysis Populations
Interviewers are probing the candidate's ability to design, analyze, and defend randomized experiments that estimate causal effects of a…
Computer science concepts
Causal Inference And Difference-In-Differences
Meta Data Scientists are expected to separate causal impact from correlation in messy product ecosystems where user behavior, network effects, ranking…
Computer science concepts
Causal Inference And Incrementality
Interviewers are testing whether you can separate correlation from causation and estimate the incremental effect of a product, ranking, ads, or growth…
Computer science concepts
Causal Inference, Difference-In-Differences, And Cannibalization
Meta is testing whether you can turn an ambiguous product or marketing question into a defensible causal estimand, not just describe correlations in DAU…
Computer science concepts
Causal Inference for AI Products
Interviewers probe your ability to design, analyze, and defend causal claims about product changes using rigorous statistical thinking. They expect a Data…
Computer science concepts
Causal Inference And Quasi-Experiments
1. What's being tested — Ability to pick and defend an identification strategy under imperfect randomization, state and test key assumptions, implement…
Computer science concepts
Central Limit Theorem, Confidence Intervals, And Power
Interviewers are probing whether you can turn noisy product data into defensible decisions using sampling distributions, confidence intervals, hypothesis…
Computer science concepts
Central Limit Theorem, Sampling, And Heavy-Tailed Metrics
These interviews test whether you can reason about skewed, zero-inflated, heavy-tailed product metrics rather than blindly applying average-based inference…
Computer science concepts
Chat System Design and Message Delivery
Interviewers are checking practical mastery of designing a real-time, durable messaging pipeline that balances ordering, durability, and multi-device…
Computer science concepts
Designing a ChatGPT-Style LLM Product End-to-End
Interviewers probe your ability to design and operate the full lifecycle for a chat-style LLM product focusing on reliability, performance, and…
Computer science concepts
CI/CD Workflow Orchestration
Candidates must show how to design a reliable, scalable CI/CD workflow orchestration system that turns source events into reproducible builds, tests…
Computer science concepts
CI/CD, Jenkins, Blue-Green, Canary, And Rollbacks
Candidates must show practical mastery of CI/CD pipeline design and deployment strategies that enable safe, repeatable releases: you should reason about…
Computer science concepts
CIDR IPv4 Firewall Matching
Candidates must demonstrate precise IPv4/CIDR arithmetic and set reasoning: convert between dotted quad and 32-bit integers, compute CIDR start/end, and…
Computer science concepts
Classical Machine Learning: KNN, PCA, And K-Means
These questions test practical mastery of K-Nearest Neighbors, Principal Component Analysis, and K-Means: algorithmic behavior, preprocessing effects…
Computer science concepts
Classifier Evaluation And Cost-Sensitive Thresholds
Interviewers are probing whether you can evaluate a binary classifier as a decision system, not just as a model with a high AUC. For Meta Data Scientist…
Computer science concepts
Classifier Evaluation, Calibration, And Thresholding
Meta is testing whether you can evaluate a high-stakes binary classifier in a way that matches product risk, operational constraints, and user impact. The…
Computer science concepts
Classifier Evaluation, Calibration, And Thresholds
What's being tested Ability to evaluate classifier quality beyond raw accuracy: interpreting confusion matrices, selecting thresholds for business utility…
Computer science concepts
Classifier Evaluation, Thresholding, And Calibration
Interviewers are probing whether you can evaluate probabilistic classifiers in a product context where actions have asymmetric costs: hiding a good post…
Computer science concepts
Clean Coding, Requirements, and Edge Cases
These prompts test streaming aggregation and incremental state management for counts, plus robust hierarchical key parsing into runtime data structures.…
Computer science concepts
Clean Implementation, Performance, And Communication
Candidates must demonstrate clean implementation (correct, well-structured code), performance-aware choices (time/space bounds, cache and amortization…
Computer science concepts
Click-Through Rate Prediction
You must demonstrate end-to-end Machine Learning Engineering judgment for click-through rate (CTR) prediction: choosing appropriate probabilistic models…
Computer science concepts
Cloud IDE And DevBox Sandboxing
Candidates must design a multi-tenant, secure, and scalable cloud development environment that balances isolation guarantees, fast developer experience…
Computer science concepts
Cloud Networking, CIDR, And Secure Infrastructure
Candidates must demonstrate practical cloud networking and CIDR fundamentals plus secure system design judgment: how to partition IP space, enforce…
Computer science concepts
CLT, Confidence Intervals, and Hypothesis Tests
1. What it is The central limit theorem (CLT) explains why averages from many independent, similar observations are approximately normal, enabling simple…