Data Manipulation (SQL/Python) Interview Questions

Showing 121–150 of 256 results

• MetaMedium·Data Scientist
  Compute SHOP spend share and model performance

  This question evaluates proficiency in SQL/Python data manipulation and analytics, focusing on aggregations, joins, time-window computations, handling missing or zero values, and deriving key metrics such as revenue share and revenue-per-conversion.

  Data Manipulation (SQL/Python)Technical Screen
• MetaMedium·Data Scientist
  Compute ad revenue metrics by geography in SQL

  In the Data Manipulation (SQL/Python) domain, this intermediate-level question evaluates SQL aggregation, join logic between impressions and clicks, date-based UTC attribution, user-geo joins, and null/divide-by-zero-safe computation of derived metrics (CTR, RPM) at the country-day granularity.

  Data Manipulation (SQL/Python)Technical Screen
• MetaMedium·Data Scientist
  Compute cohort GMV and payer rate with edge cases

  This question evaluates a data scientist's competency in cohort-based GMV calculation, payer-rate measurement, and handling common edge cases across events, orders, payments, and refunds.

  Data Manipulation (SQL/Python)Technical Screen
• MetaEasy·Data Scientist
  Compute multi-account user distribution and unread pct

  This question evaluates a data scientist's ability to perform relational data manipulation and aggregation with SQL or Python, covering competencies such as handling one-to-many joins, grouping and bucketing counts, deduplicating across related tables, and computing cohort-level percentages from boolean flags.

  Data Manipulation (SQL/Python)Technical Screen
• MetaMedium·Data Engineer
  Compute reservation diff for largest member

  This question evaluates competency in relational data manipulation and aggregation using SQL or Python, including joins, grouping, and handling missing referrer relationships.

  Data Manipulation (SQL/Python)Technical Screen
• MetaMedium·Data Scientist
  Compute shop visibility and intent metrics in SQL

  This question evaluates SQL-based data manipulation and analytical competencies in the Data Manipulation (SQL/Python) domain, focusing on deduplication, time-zone-aware timestamp handling, window functions, aggregation, and construction of visibility, CTR, and intent metrics for shops.

  Data Manipulation (SQL/Python)Technical Screen
• MetaMedium·Data Scientist
  Compute unconnected 60s posts and reactions averages

  This question evaluates data manipulation skills in SQL and Python, focusing on deduplication, join logic between event and reaction tables, date handling for string-typed dates, and aggregation to compute per-post metrics.

  Data Manipulation (SQL/Python)Technical Screen
• MetaMedium·Data Scientist
  Compute unread and multi-account user percentages

  This question evaluates understanding of SQL data manipulation skills such as aggregations, joins, NULL handling, deduplication reasoning, and user- versus person-level computations for accurate percentage metrics.

  Data Manipulation (SQL/Python)Technical Screen
• MetaMedium·Data Scientist
  Compute view prevalence from views and labels

  This question evaluates a candidate's competency in temporal joins, stateful deduplication of moderation decisions, time-aware aggregation, and prevalence metric calculations within content-moderation analytics.

  Data Manipulation (SQL/Python)Technical Screen
• MetaMedium·Data Scientist
  Count heavy callers in 7 days

  This question tests the ability to handle temporal windowing and aggregation to identify user behavior patterns based on call activity frequency.

  Data Manipulation (SQL/Python)Technical ScreenSQLWindow-FunctionsData-Manipulation
• MetaMedium·Data Scientist
  Design SQL Query for Shop Visibility and User Activity Metrics

  This question evaluates proficiency in SQL-based data manipulation—aggregation, JOINs, CASE expressions, window functions, ranking, and metric design for user tenure segmentation and temporal activity comparisons.

  Data Manipulation (SQL/Python)Technical Screen
• MetaMedium·Data Scientist
  Identify 3-Person Call Cycles in Video-Calling App

  This question evaluates SQL data-manipulation competency, including aggregation, distinct counting, date filtering and relational pattern detection (three-node cycles) to measure user engagement and group-call signals.

  Data Manipulation (SQL/Python)Technical Screen
• MetaMedium·Data Engineer
  Return count and renewal percentage of unreturned good copies

  This question evaluates proficiency in SQL data manipulation, including conditional filtering, NULL handling, aggregation, and computing conditional percentages across related tables.

  Data Manipulation (SQL/Python)Technical Screen
• MetaMedium·Data Engineer
  Solve Python and SQL data tasks

  This question evaluates data manipulation and engineering competencies by testing Python proficiency with nested data structure handling, complexity analysis and test-case design, alongside SQL proficiency in UTC-aware aggregation and per-user deduplication to compute daily active users; it is categorized as Data Manipulation (SQL/Python) within the data engineering domain. Such problems are commonly asked to assess practical implementation ability and robustness under edge cases, as well as conceptual understanding of time/space complexity and correct date-time handling, reflecting a primarily practical application with elements of conceptual analysis.

  Data Manipulation (SQL/Python)Technical Screen
• MetaMedium·Data Scientist
  Write SQL for CTR and Revenue

  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.

  Data Manipulation (SQL/Python)Technical ScreenSQLData AggregationJoin
• MetaMedium·Data Scientist
  Write SQL for Pixel Signal Metrics

  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.

  Data Manipulation (SQL/Python)Technical Screen
• MetaMedium·Data Scientist
  Write SQL for daily chats and fast replies

  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.

  Data Manipulation (SQL/Python)Technical Screen
• MetaMedium·Data Engineer
  Write SQL for library analytics

  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.

  Data Manipulation (SQL/Python)Technical Screen
• MetaMedium·Data Scientist
  Write SQL for multi-account metrics

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

  Data Manipulation (SQL/Python)Technical ScreenSQLAggregationJoins
• MetaMedium·Data Scientist
  Write SQL for seller and category metrics

  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.

  Data Manipulation (SQL/Python)Technical ScreenSQLAggregationJoins
• MetaMedium·Data Scientist
  Write SQL for social feed metrics and ties

  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.

  Data Manipulation (SQL/Python)Technical Screen
• MetaMedium·Data Scientist
  Write SQL to infer group-call demand

  This question evaluates SQL-based data manipulation and product-analytics competencies, including temporal sessionization, graph connectivity inference for detecting overlapping 1:1 call loops, deduplication trade-offs (UNION vs UNION ALL), and computation of derived metrics to estimate latent feature demand within the Data Manipulation (SQL/Python) domain. It is commonly asked because it measures practical application of event-time windowing, connected-component reasoning and edge-case handling (e.g., excluding failed connections) for producing daily summary metrics, emphasizing hands-on SQL proficiency over purely conceptual understanding.

  Data Manipulation (SQL/Python)Technical Screen
• MetaMedium·Data Scientist
  Write advanced SQL for sales support analytics

  This question evaluates practical SQL data-manipulation skills such as joins, aggregation, window functions, time-based filtering, deduplication, and computation of sales KPIs for rep-level analytics.

  Data Manipulation (SQL/Python)Technical Screen
• MicrosoftMedium·Software Engineer
  Compute most popular location with weights

  This question evaluates a candidate's ability to perform weighted aggregation, data cleaning, tie-breaking logic, and handling of malformed or missing text fields using SQL and/or Python.

  Data Manipulation (SQL/Python)Technical Screen
• MicrosoftMedium·Data Scientist
  Find common friends from directed edges

  This question evaluates a candidate's ability to manipulate relational data and reason about graph reciprocity, specifically testing SQL query formulation, aggregation, deduplication, and indexing for performance in the Data Manipulation (SQL/Python) domain.

  Data Manipulation (SQL/Python)Technical Screen
• MicrosoftEasy·Data Scientist
  Query departments and top earners

  This problem tests your ability to aggregate data using window functions to identify top-performing rows within specific groups and perform conditional filtering across joined tables.

  Data Manipulation (SQL/Python)Technical ScreenSQLWindow FunctionsJoins
• MixpanelMedium·Data Scientist
  Calculate First-Week Retention by Signup Cohort

  Compute first-week activity retention by signup-week cohort with all cohort users in the denominator and zero-retention cohorts included.

  Data Manipulation (SQL/Python)Technical Screen
• MixpanelMedium·Data Scientist
  Compare First-Week Retention by Plan Type

  Compare first-week retention by recorded plan type with distinct-user counts, inclusive denominators, and four-decimal rates.

  Data Manipulation (SQL/Python)Technical Screen
• MixpanelMedium·Data Scientist
  Count Users Active After Signup

  Count distinct users with at least one event on or after signup using a PostgreSQL query.

  Data Manipulation (SQL/Python)Technical Screen
• MixpanelMedium·Data Scientist
  Count Users Retained on Day Seven

  Count users active on the seventh calendar day after signup, without double-counting repeated events.

  Data Manipulation (SQL/Python)Technical Screen