Other interview questions

Showing 1–30 of 51 results

• 4.1 Design a Live Streaming PlatformMedium

  This question evaluates a candidate's competency in large-scale system design, encompassing distributed systems, networking, storage, real-time processing, and scalability considerations specific to live video streaming.

  OtherSoftware EngineerSystem DesignOnsite
• 4.2 Design a Collaborative Document EditorMedium

  This question evaluates skills in designing real-time collaborative systems, including distributed-systems reasoning, conflict-resolution algorithms, real-time synchronization, presence and offline synchronization, and storage/versioning architecture.

  OtherSoftware EngineerSystem DesignOnsite
• Batch Concurrent Logs into Five-Second WindowsMedium

  Batch concurrent logs into half-open five-second processing-time windows with atomic rotation, a single ordered emitter, late-timer handling, and explicit shutdown and sink-failure policies.

  OtherSoftware EngineerSoftware Engineering FundamentalsOnsite
• Build SQL pivot with lookups and currency conversionMedium

  This question evaluates proficiency in data manipulation using SQL or SQL-like Python transforms, covering point-in-time currency conversion, temporal joins for FX lookups, joins to enrich product/customer metadata, aggregation and pivot-style reporting, ranking, and data-quality checks for missing FX.

  OtherData ScientistData Manipulation (SQL/Python)Technical Screen
• Build a Queue Worker with Backoff and a Dead-Letter QueueMedium

  Design queue-worker retries with exponential backoff, durable delayed delivery, attempt limits, idempotent effects, and dead-letter replay, including AI-code validation.

  OtherSoftware EngineerSoftware Engineering FundamentalsOnsite
• Check if a numeric string is divisible by 8Easy

  Determine whether a very large non-negative integer given as a decimal string is divisible by 8 without converting the whole string to an integer. The key insight is that only the last three digits matter (since 1000 is a multiple of 8), giving an O(1) check; a streaming modular-remainder approach is the O(n) alternative.

  OtherData ScientistCoding & AlgorithmsTechnical Screen
• Clean up all Docker resourcesEasy

  This question evaluates proficiency in Docker resource management, covering containers, images, and volumes and the ability to perform a complete environment cleanup.

  OtherSoftware EngineerSoftware Engineering FundamentalsOnline Assessment
• Compare Strings With DeletionsMedium

  This question evaluates string processing and algorithmic optimization skills, specifically handling simulated deletions (backspace behavior) and performing comparisons under a strict auxiliary space constraint.

  OtherBackend EngineerCoding & AlgorithmsTechnical Screen
• Compute and estimate Markov transition probabilitiesMedium

  This question evaluates understanding of discrete-time Markov chains, multi-step transition probabilities and empirical estimation of transition matrices, testing probabilistic reasoning and matrix-based computation in the Statistics & Math domain for a Data Scientist role; it is commonly asked because it probes both theoretical grasp of stochastic processes and practical data-driven parameter estimation. At an applied algorithmic abstraction level, it requires explicit mapping of state labels to matrix indices, computing k-step transitions from a given row-stochastic matrix, estimating transition probabilities from observed one-step transitions, and addressing zero-outgoing-observation cases and any chosen smoothing policy (or justification for none).

  OtherData ScientistStatistics & MathTechnical Screen
• Compute multi-step Markov probabilities and estimate transitionsEasy

  This question evaluates understanding of discrete-time Markov chains, computation of multi-step transition probabilities from a one-step transition matrix, and empirical estimation of transition matrices from observed state-pair data including handling states with zero outgoing transitions.

  OtherData ScientistStatistics & MathTechnical Screen
• Compute total after discounting most expensive itemMedium

  This question evaluates array manipulation and numeric computation skills—specifically identifying the maximum element, applying a percentage discount, and correctly handling fractional results and flooring—within the Coding & Algorithms domain.

  OtherSoftware EngineerCoding & AlgorithmsOnline Assessment
• Continue Entry Numbers for a Daily Incremental BatchMedium

  Continue per-context entry numbering for a daily batch using stored maximums, ordered row numbers, and correct new-context handling.

  OtherAnalytics EngineerData Manipulation (SQL/Python)Technical Screen
• Count Failing, Passing and Distinction Scores from Percentage ThresholdsEasy

  Given an array of student exam scores as percentages, count how many students failed with under 50, passed with 50 to 80 inclusive, or passed with distinction above 80, and return the three counts in order. It tests careful handling of inclusive and exclusive boundaries and empty input.

  OtherSoftware EngineerCoding & AlgorithmsOnline Assessment
• Count Grid Paths Without Three Consecutive MovesMedium

  Count monotone grid paths that use exact numbers of right and up moves without ever taking three identical moves in a row. Address empty and one-direction paths, return an exact count, and explain how the limits affect time and space costs.

  OtherSoftware EngineerCoding & AlgorithmsOnline Assessment
• Decide launch with asymmetric costsHard

  This question evaluates a data scientist's ability to integrate statistical inference (p‑values, Type I/II errors, and prior probabilities) with decision analysis and financial impact assessment (NPV, rollout and rollback costs) in the context of A/B testing.

  OtherData ScientistAnalytics & ExperimentationTechnical Screen
• Decide launch with p=0.10 at alpha 0.05Hard

  This question evaluates a data scientist's competency in statistical inference and experiment decision-making, including frequentist hypothesis testing, conditional power and sample-size planning, Bayesian updating, and cost-sensitive risk analysis.

  OtherData ScientistStatistics & MathTechnical Screen
• Defend fit and handle pressure in finance interviewMedium

  This Behavioral & Leadership interview question evaluates a candidate's ability to translate data science experience into FP&A and finance analytics competencies—quantifying prior impact, handling monthly close and variance analysis, revenue forecasting and headcount/opex tracking, leveraging SQL/Excel, and managing stakeholder pressure.

  OtherData ScientistBehavioral & LeadershipTechnical Screen
• Define goals and success metrics for subscriber-only featuresHard

  This question evaluates a data scientist's competency in product analytics and experimentation, specifically goal selection, success metric definition, ROI calculation, cannibalization analysis, and end-to-end experiment and instrumentation design for subscriber-only features.

  OtherData ScientistAnalytics & ExperimentationTechnical Screen
• Design a Task SchedulerMedium

  Design a durable task scheduler with due-time indexing, atomic claims, worker leases, retry states, recurring occurrences, and clear duplicate-delivery semantics.

  OtherSoftware EngineerSystem DesignOnsite
• Design an A/B test with guardrailsHard

  This question evaluates a data scientist's skills in experimental design, hypothesis testing, statistical power and sample-size calculation, randomization and contamination control, pre-registration planning, and multiplicity adjustment for A/B testing.

  OtherData ScientistAnalytics & ExperimentationOnsite
• Design anomaly detection and handle imbalanced logistic regressionMedium

  Design a fraud detection setup for 50M transactions at a 0.4% fraud rate: a supervised logistic regression model plus an unsupervised anomaly detector for new merchants with no labels. Cover time-based splits, delayed labels, imbalance, calibration, and a cost-based threshold where a missed fraud costs far more than a false alarm.

  OtherData ScientistMachine LearningTechnical Screen
• Design metrics and experimentHard

  This question evaluates a candidate's competency in experimental design, success-metric definition, event instrumentation, causal inference for heterogeneous treatment effects, and statistical power and sample-size calculations for subscription-product analytics.

  OtherData ScientistAnalytics & ExperimentationTechnical Screen
• Design metrics resilient to data qualityMedium

  This question evaluates the ability to design robust data-quality metrics and apply SQL window-function techniques for deduplication and time-windowed completeness calculations, focusing on quantifying how ingestion delays, duplicates, and negative amounts can bias payment metrics.

  OtherData ScientistAnalytics & ExperimentationTechnical Screen
• Diagnose and fix linear regression violationsMedium

  Work through an OLS model on 10,000 observations: state the Gauss-Markov assumptions, show why residuals are orthogonal to the predictors, and handle three correlated predictors. Decide which violations break unbiasedness versus only inference, and what fixes apply.

  OtherData ScientistStatistics & MathOnsite
• Explain How You Use and Validate AI at WorkMedium

  Explain practical AI use at work through a concrete task, bounded prompts, independent validation, a corrected failure, and accountable engineering decisions.

  OtherSoftware EngineerBehavioral & LeadershipOnsite
• Explain RF/analog IC interview fundamentalsMedium

  This interview question evaluates engineering fundamentals, trade-offs, examples, pitfalls, and operational implications in a realistic interview setting. A strong answer for Explain RF/analog IC interview fundamentals states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  OtherSoftware EngineerSoftware Engineering FundamentalsOnsite
• Fewest Days to Read a Book When Chapters Cannot Be Split Across DaysEasy

  Given the reading time available on each day and the time each chapter of a book takes, find the fewest days needed to read every chapter in order when a chapter can never be split across days, or report that the book cannot be finished. It tests simulation with per-day capacity, ordering constraints, and impossible cases.

  OtherSoftware EngineerCoding & AlgorithmsOnline Assessment
• Filter Logs by an Ordered Severity ThresholdMedium

  Filter structured logs by an ordered minimum severity, preserving threshold-and-higher records while handling empty and unknown levels under an explicit contract.

  OtherSoftware EngineerCoding & AlgorithmsOnsite
• Handle unprofessional, prescriptive interviewersMedium

  This question evaluates interpersonal communication, conflict management, stakeholder negotiation, time management, and the ability to succinctly surface technical and data-quality risks during a constrained SQL screening, reflecting competencies relevant for data scientists in collaborative settings.

  OtherData ScientistBehavioral & LeadershipTechnical Screen
• How would you measure and experiment on harmful content?Easy

  This question evaluates a data scientist's competency in measuring and experimenting on harmful user-generated content, focusing on severity quantification, metric selection, and randomized experiment design within the analytics and experimentation domain.

  OtherData ScientistAnalytics & ExperimentationTechnical Screen