Data Manipulation (SQL/Python) Interview Questions

Showing 61–90 of 134 results

• 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 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
  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
• 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
• 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 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
  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
  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 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
• MetaMedium·Data Scientist
  Resolve Ties for Top-10 Users in SQL Query

  This question evaluates proficiency in SQL ranking and tie-resolution techniques for generating deterministic top-N leaderboards, focusing on handling identical scores while producing an exact set of rows.

  Data Manipulation (SQL/Python)Onsite
• MetaMedium·Data Scientist
  Retrieve Ad Metrics and Rates for Last 7 Days

  This question evaluates proficiency in SQL-based data manipulation including joins, conditional aggregation, time-based filtering, and computation of advertising metrics such as impressions, clicks, CTR, and conversion rate.

  Data Manipulation (SQL/Python)Onsite
• MetaMedium·Data Scientist
  Retrieve Top Five Ads by Conversions in 30 Days

  This question evaluates a candidate's competency in SQL data manipulation, specifically aggregating event data and applying time-window filters to produce ranked metrics.

  Data Manipulation (SQL/Python)Onsite
• MetaMedium·Data Engineer
  Return top-3 content per category

  This question evaluates the ability to perform grouped top-k selection, deterministic tie-breaking and ordering, and to reason about algorithmic efficiency when manipulating structured datasets in the Data Manipulation (SQL/Python) domain for a Data Engineer role.

  Data Manipulation (SQL/Python)Onsite
• MetaMedium·Software Engineer
  Set up a Python interview environment

  This question evaluates proficiency in Python development environment setup, including dependency and virtual environment management, secure handling of environment variables for secrets, tooling for rapid feedback, reproducibility practices, and basic service scaffolding.

  Data Manipulation (SQL/Python)Onsite
• MetaEasy·Product Analyst
  Write SQL for call pickup and usage metrics

  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.

  Data Manipulation (SQL/Python)OnsiteSQLAggregationJoins
• MetaMedium·Data Scientist
  Write SQL for comment analytics

  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.

  Data Manipulation (SQL/Python)Onsite
• MetaMedium·Data Scientist
  Write SQL for retention, conversion, and churn

  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.

  Data Manipulation (SQL/Python)Onsite
• MetaMedium·Data Scientist
  Write SQL to analyze shop visibility

  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.

  Data Manipulation (SQL/Python)Onsite
• MetaMedium·Data Scientist
  Write SQL to compare exclusive category engagement

  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.

  Data Manipulation (SQL/Python)Onsite
• MetaMedium·Data Scientist
  Write SQL to flag coordinated fake accounts

  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.

  Data Manipulation (SQL/Python)Onsite
• MetaMedium·Data Scientist
  Write SQL/pandas for KPI anomaly

  This question evaluates proficiency with SQL window functions and equivalent pandas operations for KPI anomaly detection, covering baseline computation using same-weekday medians over eight weeks, per-country and per-post_type Likes-per-DAU metrics with deduplication of users for DAU, outage filtering, attribution of declines between new and existing users, and time-series comparisons like friend-request acceptance rates while explicitly handling duplicates, sparse or missing DAU rows, and timezone issues. It is commonly asked in Data Manipulation (SQL/Python) interviews to assess practical data engineering and analytical skills, primarily testing practical application with an underlying need for conceptual understanding of baselining, attribution, and data-quality edge cases.

  Data Manipulation (SQL/Python)Onsite