Data Manipulation (SQL/Python) Interview Questions

Showing 211–240 of 446 results

• MetaMedium·Data Scientist
  Compute CTR overall and by campaign type

  This question evaluates data manipulation and analytics skills, specifically SQL aggregations, deduplication of event logs, time-window filtering, joins to campaign metadata, and safe ratio computation including division-by-zero handling.

  Data Manipulation (SQL/Python)Technical Screen
• MetaMedium·Data Scientist
  Compute Each Advertiser's Share of Shop Ad Spend

  This problem tests your ability to perform windowed aggregations, conditional filtering, and percentage calculations across multiple tables using SQL.

  Data Manipulation (SQL/Python)OnsiteSQLAggregationWindow Functions
• MetaHard·Data Scientist
  Compute High-Call Usage Rates

  This question evaluates a candidate's ability to perform advanced data manipulation and analytical SQL tasks, including joins, aggregations, timezone-aware date handling, rolling window metrics, distinct user counting, and counting user participations across roles.

  Data Manipulation (SQL/Python)Technical Screen
• 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 Shop Visibility Rate Using SQL and Python

  This question evaluates proficiency in data manipulation and metric design using SQL and Python, focusing on event-level aggregation, user deduplication, and definition of a business visibility metric within the Data Manipulation (SQL/Python) domain.

  Data Manipulation (SQL/Python)Onsite
• MetaEasy·Data Scientist
  Compute active ad revenue by creation source

  This question evaluates proficiency in data manipulation and analytics, testing skills such as joining event and impression tables, aggregating time-series revenue, and counting distinct active ads broken down by creation source.

  Data Manipulation (SQL/Python)Onsite
• MetaMedium·Data Scientist
  Compute ad impression conversion rates

  This question evaluates proficiency in time-based event attribution, aggregation of business metrics (impressions, conversion rates, spend and revenue), timestamp handling, and user/ad-level matching within the Data Manipulation (SQL/Python) domain, emphasizing practical application over abstract theory.

  Data Manipulation (SQL/Python)Onsite
• 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·Analytics Engineer
  Compute daily active ads

  This question tests your ability to aggregate time-series event data and perform distinct counts based on state transitions.

  Data Manipulation (SQL/Python)OnsiteSQLAggregationData Manipulation
• 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
• MetaEasy·Data Scientist
  Compute this-year spend share of last-year whales

  This question tests the ability to perform cohort analysis and time-based filtering to calculate the contribution of high-value segments across different fiscal years.

  Data Manipulation (SQL/Python)OnsiteSQLAggregationCohort Analysis
• MetaMedium·Data Scientist
  Compute time-spent percentage by app category

  This question evaluates proficiency in data manipulation and analytics, specifically SQL/Python skills for joining tables, aggregating session durations, handling missing app-category mappings, and computing percentage time spent over a recent seven-day window.

  Data Manipulation (SQL/Python)Onsite
• 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
  Define and compute shop visibility in SQL

  This question evaluates the ability to define and compute shop-level visibility KPIs in SQL, testing skills in metric design (visibility_rate and impression_share), deduplication, session/window filtering, and data-quality concerns such as late-arriving events and bot filtering.

  Data Manipulation (SQL/Python)Onsite
• 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
  Determine Old vs. New Users' Shop Visibility Changes

  This question evaluates SQL-focused data manipulation skills and analytical competency in cohort definition and metric selection for comparing behavioral propensities, specifically assessing how to measure the likelihood of old versus new users switching a shop profile to invisible.

  Data Manipulation (SQL/Python)Onsite
• MetaMedium·Data Scientist
  Determine Unhealthy Oculus Usage with SQL Analysis

  This question evaluates SQL-based data manipulation and time-series analysis competencies, including sessionization, user-day aggregation, and detection of sustained behavioral patterns in event data; it is categorized under Data Manipulation (SQL/Python) and targets practical application skills.

  Data Manipulation (SQL/Python)Onsite
• MetaHard·Machine Learning Engineer
  Find A Low-Quality Annotator From Label Data

  This problem tests your ability to clean noisy labeling datasets and design metrics to detect annotator bias or performance degradation.

  Data Manipulation (SQL/Python)OnsitePandasData CleaningMetrics
• MetaMedium·Data Scientist
  Find least active countries

  This question tests the ability to perform multi-step data aggregation using SQL, specifically focusing on joining, filtering by date ranges, calculating distinct counts, and identifying bottom-tier performers across different time periods.

  Data Manipulation (SQL/Python)OnsiteSQLAggregationJoins
• MetaMedium·Data Scientist
  Find the most-used app

  This question evaluates data manipulation and metrics-definition skills, emphasizing SQL and Python proficiency for aggregating user activity and joining app metadata within analytics workflows.

  Data Manipulation (SQL/Python)Onsite
• 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 Scientist
  Identify Top 10 Users by Average Call Duration

  This question evaluates SQL-based data manipulation and time-series aggregation skills, specifically computing per-user metrics from timestamped event data such as call durations.

  Data Manipulation (SQL/Python)Onsite
• MetaMedium·Data Scientist
  Rank Ads by Conversion Rate for Top 10 Performers

  This question evaluates data manipulation and analytics skills for computing time-windowed ad performance metrics, including aggregations of impressions and revenue, join logic between impression and conversion records, conversion-rate calculation, and ranking.

  Data Manipulation (SQL/Python)Onsite
• MetaMedium·Data Engineer
  Recommend two-hop follows in Python

  This question evaluates fluency with graph traversal, set operations, deduplication, and frequency-based ranking in Python, targeting manipulation of directed "follows" relationships represented as dictionaries and is categorized under Data Manipulation (SQL/Python).

  Data Manipulation (SQL/Python)Onsite