Data Manipulation (SQL/Python) interview questions

Showing 241–270 of 446 results

• Resolve Ties for Top-10 Users in SQL QueryMedium

  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.

  MetaData ScientistData Manipulation (SQL/Python)Onsite
• Retrieve Ad Metrics and Rates for Last 7 DaysMedium

  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.

  MetaData ScientistData Manipulation (SQL/Python)Onsite
• Retrieve Top Five Ads by Conversions in 30 DaysMedium

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

  MetaData ScientistData Manipulation (SQL/Python)Onsite
• Return count and renewal percentage of unreturned good copiesMedium

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

  MetaData EngineerData Manipulation (SQL/Python)Technical Screen
• Return top-3 content per categoryMedium

  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.

  MetaData EngineerData Manipulation (SQL/Python)Onsite
• Set up a Python interview environmentMedium

  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.

  MetaSoftware EngineerData Manipulation (SQL/Python)Onsite
• Solve Python and SQL data tasksMedium

  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.

  MetaData EngineerData Manipulation (SQL/Python)Technical 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 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
• Write SQL to infer group-call demandMedium

  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.

  MetaData ScientistData Manipulation (SQL/Python)Technical Screen
• Write SQL/pandas for KPI anomalyMedium

  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.

  MetaData ScientistData Manipulation (SQL/Python)Onsite
• Write advanced SQL for sales support analyticsMedium

  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.

  MetaData ScientistData Manipulation (SQL/Python)Technical Screen
• Write dating profile report with final reviewsMedium

  This question evaluates SQL and data-manipulation proficiency, including temporal joins, versioning, time-bounded snapshotting, windowed aggregations, and validation checks for data quality and idempotency.

  MetaData ScientistData Manipulation (SQL/Python)Onsite
• Compute most popular location with weightsMedium

  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.

  MicrosoftSoftware EngineerData Manipulation (SQL/Python)Technical Screen
• Count words in a document robustlyMedium

  This question evaluates text-processing and algorithmic engineering skills, specifically precise tokenization rule definition, robust handling of Unicode and punctuation, streaming/large-file processing, unit testing for corner cases, and analysis of time and space complexity.

  MicrosoftSoftware EngineerData Manipulation (SQL/Python)Onsite
• Find common friends from directed edgesMedium

  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.

  MicrosoftData ScientistData Manipulation (SQL/Python)Technical Screen
• Query departments and top earnersEasy

  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.

  MicrosoftData ScientistData Manipulation (SQL/Python)Technical Screen
• SQL: count files where bull and bear appear as standalone space-delimited wordsMedium

  SQL practice question: given a table of files and their text content, count how many files contain the words bull and bear as standalone, space-delimited words. It tests careful pattern matching, case sensitivity, NULL handling, and returning a row for each word even when its count is zero.

  MicrosoftSite Reliability EngineerData Manipulation (SQL/Python)Onsite