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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 4741–4770 of 9403 results
- MercorMediumSoftware EngineerProbability Both Drinks from One Coffee Shop Outscore Both Drinks from Another
A probability brainteaser: two drinks are ordered at each of two coffee shops and every drink receives a quality score, and you must find the probability that both drinks from the first shop outscore both drinks from the second. It tests symmetry arguments, counting rankings, and stating independence and tie assumptions.
Statistics & MathTechnical Screen - MercorMediumSoftware EngineerProbability That One Customer's Two Coffees Beat Another's Two
A probability puzzle in which two customers each buy two cups of coffee of random quality, and you find the chance that one customer's cups are better than the other's. It tests clarifying what better means, using symmetry over rankings, checking the result a second way, and reasoning about ties and correlated cups.
Statistics & MathTechnical Screen - MercorMediumSoftware EngineerProject Deep Dive With Product-Level Probing, AI Tool Use, and Startup Motivation
A startup background round: introduce yourself, walk through a recent project probed from a product angle (who owns each decision, what done means, success metrics), explain what the company does and why you want to join, and describe your side project. It also tests how you use AI tools for on-call automation, code review, and design discussions.
Behavioral & LeadershipTechnical Screen - MercorMediumSoftware EngineerReason About Coffee-Quality Comparison Probabilities
Calculate conditional comparison probabilities for coffee quality scores under continuous and discrete distributions, accounting for ties and sample permutations.
Statistics & MathTechnical Screenprobabilitycombinatoricsstatistics - MercorMediumMachine Learning EngineerReview a Compact GPT-Style Transformer Implementation
Assess and debug a compact decoder-only Transformer implementation. Candidates trace tensor dimensions through causal multi-head self-attention, identify flaws in next-token loss alignment and KV-cached generation, and establish equivalence between cached and uncached forward passes.
Machine LearningOnsiteTransformersPyTorchDeep Learning - MercorMediumSoftware EngineerReview a Hand-Rolled Python CSV Serializer for Correctness and Performance Bugs
A code review question on a short Python function that turns a table of rows into CSV text. Candidates must find its correctness and performance problems and write a fixed version, which tests delimiter handling, string building in Python, non-string cells, CSV quoting and escaping rules, and how to verify the fix.
Software Engineering FundamentalsTechnical Screen - MercorHardSoftware EngineerReview an Asynchronous Cache for Races, Memory Growth, and Thread Safety
Tests understanding of asynchronous race conditions, memory leaks, and eviction in single-threaded event loops versus multithreaded synchronization in languages like C++.
Software Engineering FundamentalsTechnical ScreenConcurrencyAsync/AwaitCaching - MercorMediumSoftware EngineerShortest Grid Path with Obstacle Eliminations
This question tests your ability to apply Breadth-First Search (BFS) on an augmented state space where path state includes both coordinates and remaining resources.
Coding & AlgorithmsTechnical Screenbfsgraphmatrix - MercorMediumSoftware EngineerSorting Complexity: Beating O(n^2), Quicksort's Worst Case, and a Slower Sort
A verbal sorting-theory discussion: explain how to sort faster than quadratic insertion sort, identify quicksort's worst case, count the non-empty subsequences and the permutations of an n-element array, and design a correct sort that is deliberately slower than O(n^2). It tests asymptotic analysis, recurrence reasoning, and basic combinatorics.
Software Engineering FundamentalsTechnical Screen - MercorMediumSoftware EngineerStripe Payment Integration: Error Handling, Idempotent Retries and Payment Storage
Explain how to add a Stripe payment integration to a backend service: how to handle errors and retries without charging a customer twice, what payment data to store, and which database to use given ACID and availability needs. Tests idempotency, failure handling, and data modeling for payments.
Software Engineering FundamentalsTechnical Screen - MercorMediumSoftware EngineerThe Worst Possible Sorting Algorithm and How to Enumerate Every Permutation
A verbal algorithms question: name the worst sorting algorithm that still always sorts correctly, analyze its running time, and explain how you would compute every permutation of the input exactly once. It tests reasoning about factorial complexity and knowledge of permutation enumeration methods.
Software Engineering FundamentalsTechnical Screen - MergeMediumSoftware EngineerDesign a Common Data Class to Normalize Different API Responses
Design a common data class that can hold API responses describing the same kind of record but differing in field names, nesting, ID types and timestamp formats, plus the code that maps each response into it. It tests data modeling, normalization, handling of missing and extra fields, and how the records would be stored in a database.
Software Engineering FundamentalsTechnical Screen - MergeMediumSoftware EngineerOrg Chart from a Reporting List: Count Orgs, Print by Level, Find a Common Manager
Given a company's employees as (id, title, reports_to) tuples with heads reporting to None, count the separate orgs, print employees level by level, and find the first manager two employees share. It tests building a tree from manager pointers, level-order traversal, ancestor queries and edge cases under a tight time limit.
Software Engineering FundamentalsTechnical Screen - MetaMediumSoftware EngineerAdd Two Arbitrarily Long Non-Negative Integers Given as Decimal Strings
Add two non-negative integers that are given as decimal digit strings, possibly thousands of digits long, and return the sum as a string without converting to built-in numbers. Tests digit-by-digit addition with carries, inputs of unequal length and canonical output without leading zeros.
Coding & AlgorithmsOther - MetaHardData ScientistAnalyze Algorithm's Impact on Diverse Demographics and Validate Causes
Analyze Algorithm's Impact on Diverse Demographics and Validate Causes evaluates metric design, causal reasoning, experiment setup, diagnostics, SQL/statistical checks, and recommendations in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Analytics & ExperimentationOnsite - MetaMediumData ScientistAnalyze Bookstore Data to Identify Top Payment Methods
This question evaluates data manipulation and analytical SQL/Python competency, focusing on relational joins, aggregations, string filtering, ranking, and proportion calculations across multiple tables in a bookstore schema.
Data Manipulation (SQL/Python)Technical Screen - 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 Mobile Promo Orders with SQL Query and Metrics
This question evaluates SQL data-manipulation and analytical competencies, including filtered joins, conditional aggregation, date-window filtering, and business-metric design to measure promotional performance on mobile orders.
Data Manipulation (SQL/Python)Technical Screen - 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 Seller Activity and Vehicle Listing Interactions
This interview question evaluates SQL or pandas logic, joins, grouping, window functions, null handling, edge cases, and validation in a realistic interview setting. A strong answer for Analyze Seller Activity and Vehicle Listing Interactions states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Data Manipulation (SQL/Python)Technical Screen - MetaMediumData ScientistAnalyze Social Media Engagement with SQL Queries
This question evaluates SQL data-manipulation and analytics competencies, including aggregation, DISTINCT counting, joins between view and action logs, date-range filtering, and conditional metric computation from event data.
Data Manipulation (SQL/Python)Technical Screen - 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 Top Call Initiators and Active French Video Callers
This question evaluates proficiency in SQL-based data manipulation and analytics—specifically aggregation, grouping, time-window filtering, and percentage calculations—by asking for the top call initiators over the last seven days and the proportion of active users in France on video calls yesterday, and is categorized under Data Manipulation (SQL/Python) for a Data Scientist role with a focus on practical application. It is commonly asked in technical interviews to assess a data scientist's ability to extract actionable engagement metrics from event logs, reason about temporal windows and population denominators, and produce accurate, explainable reporting from production-style datasets.
Data Manipulation (SQL/Python)Technical Screen - 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 Engagement and Spammer Read-Rate in SQL
This question evaluates SQL-based data manipulation and analytics skills—specifically joins, time-window filtering, aggregation, and metric calculation for measuring user engagement and spammer read-rate—within the Data Manipulation (SQL/Python) domain.
Data Manipulation (SQL/Python)Technical Screen
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