Meta interview questions

Showing 631–660 of 666 results

• Troubleshoot a single-node web outageMedium

  This question tests your ability to diagnose a sudden service outage on a single-node web server and your ability to propose architectural changes to prevent future single points of failure.

  MetaSoftware EngineerSystem DesignTechnical Screen
• Troubleshoot a website outage with disk fullMedium

  This question tests your systematic approach to diagnosing a production service outage caused by resource exhaustion and your ability to implement short-term and long-term mitigation strategies.

  MetaSoftware EngineerSoftware Engineering FundamentalsTechnical Screen
• Uncover User Needs for Group Calling EffectivelyHard

  Meta product analytics prompt on group calling, covering user-needs surveys, competitor benchmarking without direct data, experiment design, network effects, contamination, metrics, and guardrails.

  MetaData ScientistAnalytics & ExperimentationTechnical Screen
• Validate Sorted Order Under a Custom AlphabetMedium

  This problem tests your ability to define custom comparison logic using a lookup table and verify if a list of strings adheres to a non-standard lexicographical ordering.

  MetaSoftware EngineerCoding & AlgorithmsTechnical Screen
• Validate a simplified numeric stringMedium

  This question evaluates string-parsing, formal-grammar validation, and edge-case reasoning competencies, including the ability to recognize valid numeric formats and to provide time and space complexity analysis.

  MetaSoftware EngineerCoding & AlgorithmsTechnical Screen
• Validate abbreviations and bracketsMedium

  This question evaluates proficiency in string parsing, pattern validation, and algorithmic correctness, covering abbreviation interpretation and bracket balancing while requiring careful handling of edge cases and input constraints.

  MetaSite Reliability EngineerCoding & AlgorithmsTechnical Screen
• Validate alternating checkout/return logsMedium

  This question evaluates the ability to process sequential event logs, maintain per-entity state invariants (checkout vs return), and reason about algorithmic time and space complexity within a validation task.

  MetaData EngineerCoding & AlgorithmsTechnical Screen
• Validate carpool capacityMedium

  This question evaluates understanding of interval-based resource accounting and algorithmic reasoning with cumulative constraints, assessing skills in processing time-bound events and managing capacity limits.

  MetaData EngineerCoding & AlgorithmsOnsite
• Validate parentheses and find k-th largestMedium

  This problem tests your ability to handle nested structures using a stack and your efficiency in order-based selection using heap operations.

  MetaSoftware EngineerCoding & AlgorithmsTechnical Screen
• Validate word abbreviation and reconcile two abbreviationsMedium

  Validate word abbreviation and reconcile two abbreviations evaluates algorithm design, data structures, correctness, complexity, edge cases, and implementation details in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  MetaSoftware EngineerCoding & AlgorithmsOnsite
• Visualize Netflix metric trendsMedium

  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 Visualize Netflix metric trends states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  MetaData EngineerAnalytics & ExperimentationOnsite
• Walk through a resume deep diveHard

  This question evaluates ownership, technical leadership, system design, and operational reliability by requiring an end-to-end deep dive into an infrastructure project covering problem definition, architecture, scaling (QPS, latency, storage), SLOs, incident response, and impact metrics.

  MetaSoftware EngineerBehavioral & LeadershipOnsite
• Walk through resume and plan team successMedium

  This question evaluates leadership, impact-focused communication, problem-solving, prioritization, and cross-functional collaboration skills via an end-to-end resume walkthrough and a 90-day team success plan, and it sits in the Behavioral & Leadership category; the level of abstraction is practical application, emphasizing demonstrated impact and near-term execution. It is commonly asked in technical interviews to reveal a candidate's ability to articulate measurable accomplishments, explain difficult technical or organizational challenges, and present structured, outcome-oriented plans and metrics that indicate interpersonal influence and operational readiness.

  MetaSoftware EngineerBehavioral & LeadershipTechnical Screen
• What features and feature selection would you use?Medium

  This question evaluates feature engineering, feature selection, and production-aware machine learning system design skills for ranking shop ads, within the Machine Learning domain for a Data Scientist role.

  MetaData ScientistMachine LearningTechnical Screen
• Which clustering algorithm would you use and whyMedium

  A Meta Data Scientist machine learning screen on choosing a clustering algorithm for social-product users. It contrasts traditional feature-vector clustering (k-means, GMM, hierarchical, DBSCAN/HDBSCAN) with social-graph community detection (Louvain/Leiden, spectral, SBM, node embeddings), and covers preprocessing, choosing the number of clusters, evaluation, directed/weighted graphs, and scaling to millions of users.

  MetaData ScientistMachine LearningTechnical Screen
• Write SQL filtering, grouping, CASE, UNION tasksMedium

  This question evaluates proficiency with SQL filtering, aggregation, conditional logic, and set operations—specifically skills around WHERE filtering, GROUP BY/HAVING, CASE bucketing, FLOOR on aggregates, and UNION versus UNION ALL—within the Data Manipulation (SQL/Python) domain and emphasizes practical application of ANSI SQL.

  MetaData ScientistData Manipulation (SQL/Python)HR Screen
• Write SQL for CTR and RevenueMedium

  This question tests the ability to join event logs with reference data, perform conditional aggregation to calculate click-through rates (CTR), and apply date-sensitive currency conversions.

  MetaData ScientistData Manipulation (SQL/Python)Technical Screen
• Write SQL for Pixel Signal MetricsMedium

  This question evaluates SQL-based data manipulation and analytical competencies, specifically joining and aggregating event and ad performance tables, computing event-weighted metrics and composite data-quality scores (such as validity and match-rate based measures) across a 30-day window.

  MetaData ScientistData Manipulation (SQL/Python)Technical Screen
• Write SQL for call pickup and usage metricsEasy

  This question tests the ability to join event logs, filter by session timing, and calculate per-user metrics using window functions or group-by operations.

  MetaProduct AnalystData Manipulation (SQL/Python)Onsite
• Write SQL for car rental utilization by cityHard

  This question evaluates proficiency in SQL querying and relational data modeling, focusing on time-interval overlap logic, distinct aggregation across joined tables, null handling, and safe computation of utilization metrics.

  MetaData EngineerCoding & AlgorithmsOnsite
• Write SQL for comment analyticsMedium

  This question evaluates proficiency in SQL analytics, specifically joins, filtering, aggregation (distinct counts), percentile functions, time-windowed grouping, handling soft-deleted records, and writing efficient queries via CTEs.

  MetaData ScientistData Manipulation (SQL/Python)Onsite
• Write SQL for daily chats and fast repliesMedium

  This question evaluates a candidate's ability to manipulate relational data and reason about temporal event sequences, specifically testing skills in SQL aggregation, distinct counting, deduplication, bidirectional relationship handling, and time-windowed event matching.

  MetaData ScientistData Manipulation (SQL/Python)Technical Screen
• Write SQL for library analyticsMedium

  This question evaluates proficiency in SQL-based data manipulation and analytical querying, focusing on relational schema navigation, joins, aggregation, filtering, and handling temporal and conditional metrics within a library dataset.

  MetaData EngineerData Manipulation (SQL/Python)Technical Screen
• Write SQL for multi-account metricsMedium

  This question tests your ability to perform complex aggregations across multiple tables, specifically using conditional logic and joins to compute user engagement metrics.

  MetaData ScientistData Manipulation (SQL/Python)Technical Screen
• Write SQL for retention, conversion, and churnMedium

  This question evaluates proficiency in SQL-based analytics and time-aware data manipulation, covering cohort and retention computation, conversion and churn metrics, user-level deduplication across platforms, timezone-local day handling, rolling-window comparisons, and calculation of statistical confidence intervals.

  MetaData ScientistData Manipulation (SQL/Python)Onsite
• Write SQL for seller and category metricsMedium

  This question tests the ability to perform complex aggregations, handle date filtering, and calculate performance ratios while managing potential division-by-zero errors in a multi-table SQL environment.

  MetaData ScientistData Manipulation (SQL/Python)Technical Screen
• Write SQL for social feed metrics and tiesMedium

  This question evaluates advanced SQL analytics skills and competency in data modeling for social feed metrics, including relationship joins, viewer-day temporal aggregation, weighted interaction scoring, and per-user top-N ranking.

  MetaData ScientistData Manipulation (SQL/Python)Technical Screen
• Write SQL to analyze shop visibilityMedium

  This question evaluates proficiency in SQL-based time-series and event-data manipulation, including deduplication of consecutive states, window functions, aggregation, cohort analysis, and percentile/summary statistics.

  MetaData ScientistData Manipulation (SQL/Python)Onsite
• Write SQL to compare exclusive category engagementMedium

  This question evaluates SQL-based data manipulation and cohort analysis competencies, including cohort definition, session-level filtering and aggregation for metrics such as user counts, median active days, proportions, and per-user average minutes while applying data-quality constraints over a fixed date window.

  MetaData ScientistData Manipulation (SQL/Python)Onsite
• Write SQL to flag coordinated fake accountsMedium

  This question evaluates proficiency in SQL-based data manipulation, temporal windowing, event-table joins, aggregation and feature engineering for fraud detection, including computing weighted risk_score flags across user activity.

  MetaData ScientistData Manipulation (SQL/Python)Onsite