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Interview Questions
Practice 9403 coding, system design and machine learning questions from 546 companies, sorted by role, topic and interview round. Every question has step-by-step hints, a code runner that supports 8 languages and an AI mentor; members also get the full solution walkthrough.
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Showing 1861–1890 of 3640 results
- MetaHardData ScientistAnalyze Change in App Metrics and Feature Impact
This interview question evaluates metric design, causal reasoning, experiment setup, diagnostics, SQL/statistical checks, and recommendations in a realistic interview setting. A strong answer for Analyze Change in App Metrics and Feature Impact states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Analytics & ExperimentationOnsite - MetaMediumData ScientistAnalyze Comment Distribution Using Statistical Metrics and Tests
This interview question evaluates metric design, causal reasoning, experiment setup, diagnostics, SQL/statistical checks, and recommendations in a realistic interview setting. A strong answer for Analyze Comment Distribution Using Statistical Metrics and Tests states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Analytics & ExperimentationOnsite - MetaMediumData ScientistAnalyze Hashtag Follow Behavior with SQL Queries
This question evaluates a candidate's competency in data manipulation and analytical SQL techniques—specifically aggregation, filtering, joins, and calculation of proportions—to analyze hashtag follow behavior.
Data Manipulation (SQL/Python)Onsite - MetaMediumData ScientistAnalyze Key Metrics for Notification System Success
This interview question evaluates metric design, causal reasoning, experiment setup, diagnostics, SQL/statistical checks, and recommendations in a realistic interview setting. A strong answer for Analyze Key Metrics for Notification System Success states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Analytics & ExperimentationOnsite - MetaHardSoftware EngineerAnalyze Minimum Completion Time for Target Courses in a Prerequisite DAG
Analyze the minimum time needed to finish target courses and only their transitive prerequisites in a DAG. Distinguish total study effort from elapsed time with parallel courses, count shared prerequisites once, exclude unrelated nodes, and detect reachable cycles.
Software Engineering FundamentalsOnsite - MetaMediumData ScientistAnalyze Oculus App Engagement with SQL Queries
This question evaluates proficiency in SQL-based data manipulation and aggregation, time-window filtering, percentage calculations, and the application of basic statistical hypothesis testing for comparing user engagement across categories.
Data Manipulation (SQL/Python)Onsite - MetaMediumData ScientistAnalyze Revenue Shifts to Identify Cannibalization Effects
Evaluates revenue cannibalization analysis across creation sources. Strong answers define Source A, other-source revenue, total revenue, cannibalization ratio, and net lift, then use panel, rollout, difference-in-differences, or synthetic-control evidence to separate source shifting from organic growth.
Analytics & ExperimentationOnsite - MetaMediumData ScientistAnalyze Thirty-Day Ad Performance with SQL
This problem tests proficiency in window functions, time-based aggregation, and join logic to analyze multi-dimensional advertising performance metrics.
Data Manipulation (SQL/Python)OnsiteSQLAggregationTime-Series - MetaMediumData ScientistAnalyze User Comment Distribution and Sampling Effects
Evaluates statistical reasoning about right-skewed user comment counts and sampling distributions. Strong answers place median, mean, and p95 correctly, then use the Central Limit Theorem to explain why group averages become less variable and more symmetric.
Statistics & MathOnsite - MetaMediumData ScientistAnalyze User Transfer Distribution in Initial Launch Period
Evaluates distribution analysis for transfer counts after a new P2P payments launch. Strong answers specify the denominator, describe zero inflation and right skew, locate mean, median, mode, and p95, and explain how adoption, cohorts, and exposure change the distribution over time.
Statistics & MathOnsite - MetaMediumData ScientistAnalyze skewed comments and sampling effects
This question evaluates understanding of descriptive statistics for right‑skewed count data and inferential concepts such as sampling distributions, standard error, percentiles, and the Central Limit Theorem, and is commonly asked to probe how skewness and sample size affect central tendency measures and uncertainty in estimates.
Statistics & MathOnsite - MetaMediumData ScientistAnnotating and forecasting a long‑tail distribution
Evaluates robust summarization and forecasting for long-tailed daily share-count distributions. Strong answers compute mean, median, P1, P99, visualize with log scales or percentile bands, and forecast seasonality with heavy-tail-aware methods.
Statistics & MathOnsite - MetaHardSoftware EngineerAnswer Repeated Range Aggregate Queries on a Static BST
Preprocess a sorted array derived from a static BST to answer many inclusive range queries. Use binary-search boundaries and wide prefix sums to return both the sum and count for each range without rescanning the tree.
Coding & AlgorithmsOnsite - MetaMediumMachine Learning EngineerAnswer behavioral questions on projects and feedback
This set of behavioral questions evaluates communication, leadership, adaptability, handling ambiguity, feedback exchange, and project ownership within the context of machine learning engineering projects.
Behavioral & LeadershipOnsite - MetaMediumMachine Learning EngineerAnswer core behavioral questions using STAR
This question evaluates behavioral and leadership competencies—including communication, teamwork, conflict resolution, time and scope management, and mentorship—within the context of a Machine Learning Engineer role.
Behavioral & LeadershipOnsite - MetaMediumSoftware EngineerAnswer four string/array/battery coding questions
This set of four problems evaluates algorithmic competencies including ASCII string processing and character classification, array transformation and operation-count reasoning, cost-minimization for enforcing monotonicity under only-increase constraints, and time-based simulation of resource scheduling with recharge cycles.
Coding & AlgorithmsOnsite - MetaHardSoftware EngineerAnswer key behavioral prompts effectively
This set of behavioral prompts evaluates ownership, communication, conflict resolution, adaptability, and the ability to receive and act on feedback, targeting leadership and interpersonal competencies relevant to software engineering.
Behavioral & LeadershipOnsite - MetaMediumData ScientistApply reinforcement learning to product decisions
This question assesses the ability to determine the appropriate RL framework for recommendation systems, evaluate policies using offline data, and implement safety guardrails.
Machine LearningOnsiteReinforcement LearningRecommendation SystemsOffline Evaluation - MetaHardData ScientistApply sequential testing without p-hacking
This question evaluates a candidate's competency in sequential hypothesis testing, alpha‑spending rules (Pocock vs O’Brien–Fleming), always‑valid inference methods such as mixture‑SPRT and e‑values, and strategies for controlling error rates across multiple metrics.
Statistics & MathOnsite - MetaHardMachine Learning EngineerArchitect an asynchronous RL post-training system
This question evaluates a candidate's ability to design an asynchronous reinforcement-learning post-training system for a production chat LLM, testing competencies in ML system architecture, distributed training and serving separation, streaming data engineering, reward modeling and credit assignment, safety/compliance, and deployment/operations.
ML System DesignOnsite - MetaMediumData ScientistAssess Cultural Fit and Problem-Solving in Reality Labs Role
This interview question evaluates behavioral evidence, ownership, communication, trade-offs, and measurable outcomes in a realistic interview setting. A strong answer for Assess Cultural Fit and Problem-Solving in Reality Labs Role states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Behavioral & LeadershipOnsite - MetaMediumSoftware EngineerBehavioral: Acting on Critical Manager Feedback and a Simplification Beyond Your Depth
A senior behavioral question on growth: explain how you handled critical feedback from your manager, and a time you tried to simplify something before you understood it well enough. It tests self-awareness, learning speed and how you turn a misstep into lasting changes in your habits.
Behavioral & LeadershipOnsite - MetaMediumSoftware EngineerBehavioral: Managing Shifting Priorities and Changing Approach Mid-Project
A senior behavioral question on prioritization and adaptability: how you manage several competing priorities, whether you prefer shifting or steady work, and a project where you had to change approach midway. It probes trade-off reasoning, stakeholder communication and recognizing when to change course.
Behavioral & LeadershipOnsite - MetaMediumSoftware EngineerBuild a FastAPI summarization service
This question evaluates web API development and backend engineering competencies, including FastAPI usage, Python type hints, Pydantic-based request/response validation, error handling, in-memory state management, and modular router/service/model layering, and it belongs to the Coding & Algorithms category with a focus on backend web service and API design. It is commonly asked to assess practical ability to deliver production-ready REST endpoints with proper validation and error handling under constrained resources, and the level of abstraction required is primarily practical implementation with attention to architectural design.
Coding & AlgorithmsOnsite - MetaHardMachine Learning EngineerBuild a Friend Recommender
This problem tests your ability to implement a graph-based recommendation algorithm using common neighbors and popularity constraints to rank potential connections.
Coding & AlgorithmsOnsiteGraphSortingRecommendation System - MetaHardSoftware EngineerBuild a Mistral-powered RAG agent
This question evaluates a candidate's ability to design and implement a retrieval-augmented generation (RAG) system using a large language model API, including document ingestion, chunking, vector indexing and retrieval, streaming completions, service interfaces (CLI and HTTP), and resilience features like retries, persistence, and structured error handling. Commonly asked in the ML System Design domain to assess practical engineering and architectural reasoning, it tests API integration, production-readiness (configuration, error handling, persistence, and testing) and emphasizes practical application with conceptual understanding of retrieval trade-offs.
ML System DesignOnsite - MetaHardData ScientistBuild dashboard; diagnose engagement–purchase gap
This question evaluates a data scientist's competency in product analytics and experimentation, focusing on dashboard specification, precise event- and user-level metric definitions, core dimensions, data freshness SLAs, and instrumentation within the Analytics & Experimentation domain.
Analytics & ExperimentationOnsite - MetaMediumMachine Learning EngineerBuild harmful-content text classifier
This problem evaluates your ability to design a robust text classification system for identifying harmful content, emphasizing strategies for data imbalance, labeling ambiguity, and real-time inference.
Machine LearningOnsiteNLPClassificationMLOps - MetaMediumData ScientistCalculate Ad Insertion Statistics for Two Methods
Meta statistics prompt comparing two ad-insertion strategies, covering binomial moments, deterministic quotas, tail probability for excessive ads, adjacent ad pairs, and risk of long ad runs.
Statistics & MathOnsite - MetaMediumData ScientistCalculate Ad Performance with Click-Through and Conversion Rates
This question evaluates a candidate's competency in designing and computing ad-performance KPIs by performing data manipulation, joining event tables, applying time-window filters, and aggregating metrics using SQL or Python.
Data Manipulation (SQL/Python)Onsite
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