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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 21 of 26
Computer science concepts
Reliability, Observability, and Incident Diagnostics
Interviewers expect you to demonstrate practical reliability engineering judgment: define measurable service health, design observability that surfaces…
Computer science concepts
Reliable Payment Processing And Idempotency
Interviewers expect you to design a reliable, auditable money flow that avoids duplicate charges, recovers from partial failures, and keeps…
Computer science concepts
Resilient APIs And External Dependency Integration
Interviewers probe your ability to integrate and expose external services reliably: designing APIs and clients that tolerate flakiness, control resource…
Computer science concepts
Retail Demand Forecasting
Interviewers are probing your ability to turn retailer signals into reliable, operational demand forecasts: problem framing, appropriate model choice…
Computer science concepts
Retention and Cohort Metrics
1. What it is Retention measures how many users return and perform a key action after first using a product. Cohort metrics group users by a shared “start”
Computer science concepts
Retention, Cohort, And Funnel Analysis
What's being tested Ability to operationalize retention and funnel analyses: pick clear metrics, define cohorts and windows, implement reliably…
Computer science concepts
Retention, Cohort, Funnel, And Lifecycle Analysis
Interviewers are testing whether you can turn messy user behavior into decision-quality metrics: defining cohorts, measuring retention correctly…
Computer science concepts
Retrieval And Candidate Generation For Meta Ranking
1. What's being tested Ability to design and evaluate scalable, low-latency candidate-generation systems: choosing retrieval algorithms (sparse/dense/ANN)…
Computer science concepts
Retrieval, Candidate Generation, And Ranking Cases
Interviewers are probing whether you can design and evaluate large-scale recommendation/search systems where the core challenge is narrowing billions of…
Computer science concepts
Retrieval Evaluation for LLM RAG and Search
1. What it is Retrieval evaluation measures how well a system finds and ranks the right context for a question, and how much that context improves the…
Computer science concepts
Retrieval Quality And Offline Ranking Metrics
Interviewers are probing whether you can evaluate retrieval and ranking systems before launching them, especially when online A/B tests are expensive…
Computer science concepts
Revenue, Marketplace, And Monetization Analytics
Meta is testing whether you can reason about monetization systems as economic products, not just dashboards: revenue is produced by matching users…
Computer science concepts
Revenue Referral Aggregation And Ranking
These problems test efficient aggregation of event streams into per-customer totals, plus selection/update of the k smallest totals under dynamic inputs.…
Computer science concepts
RLHF And Preference Optimization Basics
You must demonstrate practical ownership of a production RLHF-style loop: building and operating the reward model and preference optimization components…
Computer science concepts
Robust Coding And Numeric Edge Cases
These problems test numeric stability and robust handling of edge cases in algorithmic code: correct use of analytic vs iterative solvers, safe aggregation…
Computer science concepts
Robust Statistical Metrics
This tests whether you can choose and implement a metric whose behavior matches the operational question, rather than treating “average” as a default. For…
Computer science concepts
Root Cause Analysis And Metric Debugging
Interviewers are testing whether you can turn an ambiguous metric movement into a disciplined investigation, not whether you can brainstorm random causes.
Computer science concepts
Root Cause Analysis And Segmentation
Interviewers are testing whether you can diagnose metric movement systematically rather than jumping to a favorite explanation. For a Meta Data Scientist…
Computer science concepts
Safety, Alignment, Guardrails, and Responsible LLM Deployment
The interviewer is probing an engineer's ability to design, implement, and operate technical safety guardrails around large language models in production:
Computer science concepts
Safety And Abuse Monitoring For AI Products
Interviewers are probing your ability to design reliable, scalable monitoring and enforcement systems that detect and mitigate abusive or unsafe outputs…
Computer science concepts
Safety-First Cross-Functional Leadership
Interviewers probe your ability to lead safety-driven, cross-functional decisions while staying in the Data Scientist lane: choose defensible safety…
Computer science concepts
Safety Policy Enforcement and Audit Systems
Interviewers are probing your ability to design reliable, scalable policy enforcement and audit systems that operate in production: low-latency enforcement…
Computer science concepts
Scikit-Learn Classification Pipelines
Candidates must demonstrate building leak-free, reproducible classification pipelines using scikit-learn : correct temporal splitting to avoid future…
Computer science concepts
Secure Distributed Storage, Messaging, and Consistency
Candidates must show practical mastery of designing scalable, durable, and consistent distributed storage and messaging systems: partitioning, replication/…