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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 12 of 26
Computer science concepts
Heaps, Streaming Median, and Top-K Selection
These problems test efficient use of heaps/priority queues and streaming order-statistics to maintain small summaries of large inputs (medians, k-th, top-k…
Computer science concepts
Heavy-Tailed and Zero-Inflated Distribution Analysis
1. What it is Heavy-tailed data have a non-negligible chance of producing very large values (e.g., Pareto/lognormal tails), so means/variances can be…
Computer science concepts
Hierarchical In-Memory Data Structures
These problems test building and manipulating hierarchical in-memory trees (file-system trees and tries) and reasoning about graph dependencies and cache…
Computer science concepts
HTTP API Crawling and URL Mazes
These problems test graph traversal under an I/O contract: traverse a hidden graph exposed as HTTP links, detect termination, and avoid cycles while…
Computer science concepts
Human Feedback And Annotation Data Quality
Interviewers probe your ability to measure, diagnose, and mitigate annotation noise so downstream model and experiment conclusions are valid. They want to…
Computer science concepts
Human Feedback Data Quality
Interviewers look for the candidate's ability to diagnose, quantify, and mitigate data-quality issues that arise in human feedback used to train or…
Computer science concepts
Hypothesis Testing And Confidence Intervals
Interviewers are probing your ability to turn noisy observational or experimental data into defensible statements about causal effects and uncertainty…
Computer science concepts
Hypothesis Testing And Confidence Intervals
1. What's being tested Your ability to translate a business question into formal hypotheses, pick the correct test and assumptions, compute/interpret…
Computer science concepts
Hypothesis Testing, Power, And Confidence Intervals
Interviewers are probing whether you can make statistically defensible product and model decisions when metrics are noisy, effects are small, and the…
Computer science concepts
Hypothesis Tests, Confidence Intervals, And P-Values
You need to show that you can reason from a product claim to a statistically valid decision: define the estimand, choose the right test, interpret…
Computer science concepts
Idempotency, Deduplication, and Delivery Semantics
Candidates must demonstrate practical mastery of idempotency, deduplication, and delivery semantics in distributed systems: how to prevent duplicate…
Computer science concepts
In-Memory Databases And Query Engines
These prompts test translating SQL-like operations into efficient in-memory algorithms: data modeling, indexing, query execution (projection/filter/sort)…
Computer science concepts
In-Memory Databases And Query Processing
Interviewers are probing whether you can design and implement the core of an in-memory database engine: data representation, indexing, query execution…
Computer science concepts
Inclusive Collaboration, Conflict, And Adaptability
Meta is probing whether you can deliver rigorous data science work through people, not just through code, models, or dashboards. For a Data Scientist, the…
Computer science concepts
Instagram Product Analytics
Meta is probing whether a Data Scientist can turn ambiguous product questions about Instagram, Facebook, Stories, Reels, and Shopping into measurable…
Computer science concepts
Instrumentation, Logging, And Data Quality
Interviewers are testing whether you can reason from product behavior to reliable analytical data: what events should be logged, how they should be defined…
Computer science concepts
Instrumentation, Logging, Labeling, And Data Quality
Interviewers are probing whether you can reason from a product or ML question back to the data-generating process: what should be logged, how labels are…
Computer science concepts
Integrity, Fraud, And Content Moderation Measurement
Interviewers are testing whether you can measure safety outcomes when ground truth is sparse, delayed, subjective, and adversarial. The key skill is not…
Computer science concepts
Integrity, Fraud, Bot, And Harmful Content Measurement
Interviewers are probing whether you can measure adversarial, low-base-rate, policy-defined harm under imperfect labels and shifting attacker behavior. The…
Computer science concepts
Integrity, Harm, And Fraud Measurement
1. What's being tested Judgment about designing robust measurement for integrity/fraud interventions: choosing business-relevant metrics, experimental…
Computer science concepts
Interleaving and Multileaving for Ranking Comparison at Scale
1. What it is Interleaving blends the results of two ranking models into a single list for a user session and attributes each click to the model that…
Computer science concepts
Inventory Replenishment And Stockout Analytics
Interviewers probe your ability to turn sales and inventory signals into defensible analytics and ML models that reduce stockouts and optimize…
Computer science concepts
K-Fold Cross-Validation And Model Selection
Candidates must demonstrate practical mastery of K-Fold Cross-Validation as a tool for model selection and honest performance estimation: when to use it…
Computer science concepts
Key-Value Stores, Caches, And WAL Durability
Interviewers probe your ability to design a crash-consistent, single-node key–value engine that balances latency, durability, and memory/disk trade-offs…