Data Manipulation (SQL/Python) Interview Questions

Showing 181–210 of 446 results

• KKikoffMedium·Software Engineer
  Parse CSV and format transactions

  This question evaluates a candidate's ability to parse and format structured transactional data, emphasizing CSV parsing, trimming whitespace, numeric formatting to two decimal places, and preserving input row order.

  Data Manipulation (SQL/Python)Technical Screen
• KKneronMedium·Software Engineer
  Count IoT devices matching date and threshold

  This question evaluates handling of paginated REST APIs, JSON parsing and nested-data filtering, month/year date parsing, and numeric threshold comparisons, reflecting skills in data retrieval and programmatic data manipulation.

  Data Manipulation (SQL/Python)Online Assessment
• LLendingClubMedium·Data Scientist
  Query Active Loans by Borrower ID in SQL

  This question evaluates proficiency with SQL data aggregation, grouping, and ordering to compute borrower-level loan totals, assessing practical skills in relational data manipulation within the Data Manipulation (SQL/Python) domain.

  Data Manipulation (SQL/Python)Technical Screen
• LinkedInEasy·Data Scientist
  Analyze member video posting behavior by country

  A LinkedIn data scientist technical-screen question on member video-posting behavior using two relational tables. It tests joins, first-event derivation with MIN/GROUP BY, conditional grouping for US vs non-US comparison, and distinct-member counts per country with a length filter.

  Data Manipulation (SQL/Python)Technical Screen
• LinkedInEasy·Data Scientist
  Compute each member’s current notification status

  A LinkedIn data scientist SQL screen: given an end-of-January notification-status snapshot and a February log of turn_on/turn_off actions, compute each member's current status as of 2020-02-29, keeping members from either table. Follow-ups explore reconstructing state when the action log has no date column — unsolvable in general, but recoverable via action-count parity when every action is a guaranteed valid transition.

  Data Manipulation (SQL/Python)Technical Screen
• LinkedInMedium·Data Scientist
  Count Article Types Viewed

  Count Article Types Viewed evaluates SQL or pandas logic, joins, grouping, window functions, null handling, edge cases, and validation in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  Data Manipulation (SQL/Python)Technical Screen
• LinkedInMedium·Data Scientist
  Device Status without Timestamps

  This question evaluates data manipulation and behavioral analytics competencies, emphasizing aggregation, deduplication, distinct counts, and date-based filtering to measure author and viewer activity.

  Data Manipulation (SQL/Python)Technical Screen
• LinkedInMedium·Data Scientist
  [SQL] Job Ad Metrics with Applicant Filter

  This question evaluates proficiency in SQL data manipulation and deduplication, focusing on aggregations, distinct counts, and conditional filtering of event records (views vs applies).

  Data Manipulation (SQL/Python)Technical Screen
• LyftMedium·Data Scientist
  Query and transform marketplace data in SQL/Python

  This question evaluates mastery of data manipulation and analytics skills in the Data Manipulation (SQL/Python) domain, including SQL aggregation and window functions, time-based joins and grouping, deduplication of status streams, handling late-arriving events, and implementing rolling metrics using both SQL and pandas.

  Data Manipulation (SQL/Python)Onsite
• MWMarshall WaceMedium·Software Engineer
  Find users with multi-country successful logins

  This question evaluates proficiency in relational data manipulation, text normalization, aggregation, deduplication, and sorting within the Data Manipulation (SQL/Python) domain, focusing on practical query-writing and data-processing skills.

  Data Manipulation (SQL/Python)Onsite
• MathWorksMedium·Software Engineer
  Calculate cost from orders with SQL

  This question evaluates a candidate's ability to perform SQL data manipulation and business-rule calculations, including conditional joins, date-range filtering, and numeric arithmetic for discounts and taxes.

  Data Manipulation (SQL/Python)Online Assessment
• MetaMedium·Data Scientist
  Analyze 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
• MetaMedium·Data Scientist
  Analyze 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
• MetaMedium·Data Scientist
  Analyze 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
• MetaMedium·Data Scientist
  Analyze 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
• MetaMedium·Data Scientist
  Analyze 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
• MetaMedium·Data Scientist
  Analyze 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
• MetaMedium·Data Scientist
  Analyze 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
• MetaMedium·Data Scientist
  Analyze 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
• MetaMedium·Data Scientist
  Analyze 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
• MetaMedium·Data Scientist
  Calculate 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
• MetaMedium·Data Scientist
  Calculate CTR and ad revenue

  This problem tests your ability to clean and join datasets, handle currency conversion, and aggregate metrics at different granularities.

  Data Manipulation (SQL/Python)Technical ScreenSQLData AggregationMetrics
• MetaMedium·Data Scientist
  Calculate Daily Survey Response Rates by Country

  Write a PostgreSQL query to compute daily survey response rates at the country level using distinct user counts while handling orphan response logs and edge cases.

  Data Manipulation (SQL/Python)Technical ScreenSQLPostgreSQLData Aggregation
• MetaMedium·Data Scientist
  Calculate French User Engagement and U.S. Call Duration

  This question evaluates proficiency in SQL-based data manipulation and analytics, testing skills such as joining event and profile tables, aggregating metrics, and computing user-level engagement proportions and average durations.

  Data Manipulation (SQL/Python)Technical Screen
• MetaMedium·Data Scientist
  Calculate Survey Response Rate and Quality Metric in SQL

  This question evaluates SQL data-manipulation competencies such as aggregation, NULL handling, deduplication (first-per-group), and computing engagement and quality metrics from event logs.

  Data Manipulation (SQL/Python)Technical Screen
• MetaMedium·Data Scientist
  Calculate Total Revenue in USD Using SQL Query

  This question evaluates proficiency in SQL data manipulation, focusing on joins, aggregations, and handling currency conversion across relational tables.

  Data Manipulation (SQL/Python)Onsite
• MetaMedium·Data Scientist
  Calculate Video Call Usage Metrics by Country and Date

  This question evaluates data manipulation and product-analytics skills, focusing on joining user and call datasets, aggregations for distinct-user percentages, and per-DAU duration calculations.

  Data Manipulation (SQL/Python)Technical Screen
• MetaMedium·Data Scientist
  Calculate and Compare Survey Response Rates for User Tenure

  This question evaluates proficiency in relational data manipulation and metric computation, including SQL joins, aggregation and cohort-based comparisons relevant to survey response analysis, and is categorized under Data Manipulation (SQL/Python) for a Data Scientist role.

  Data Manipulation (SQL/Python)Onsite
• MetaMedium·Data Scientist
  Compare Survey Satisfaction for New and Established Users

  Tests your ability to write PostgreSQL queries that segment users into cohorts based on date logic, filter invalid survey events, and calculate aggregate satisfaction metrics.

  Data Manipulation (SQL/Python)Technical Screensqlpostgresqldata-aggregation
• MetaMedium·Data Scientist
  Compute 7-day views and reactions by relationship

  This question evaluates skills in SQL-based data manipulation—specifically deduplication by longest duration, join-based attribution of reactions to views, window functions, distinct counts, aggregations, and date-range filtering within the Data Manipulation (SQL/Python) domain.

  Data Manipulation (SQL/Python)Technical Screen