Data Manipulation (SQL/Python) interview questions

Showing 331–360 of 446 results

• Implement and Evaluate Pin Similarity in PythonHard

  Practice a DS-focused Python similarity question using Pin representations, cosine similarity, top-k ranking, and explicit edge-case handling. Extend the implementation discussion to vectorized scaling, approximate retrieval, offline ranking metrics, leakage checks, diversity, and online evaluation.

  PinterestData ScientistData Manipulation (SQL/Python)Technical Screen
• Transform nested dicts with pandas apply/lambdaMedium

  This question evaluates proficiency in pandas-based data manipulation, including flattening nested list and dict structures, parsing nested attributes, computing per-user aggregates and segment metrics, and implementing robust, vectorized extraction logic that handles missing keys and avoids SettingWithCopy pitfalls.

  PinterestData ScientistData Manipulation (SQL/Python)Onsite
• Write Queries for Pinterest Engagement TasksMedium

  This question evaluates SQL-based data manipulation and Python set/co-occurrence computation skills, covering aggregation, joins, timestamp-based cohorting and windowing, freshness detection, CTR calculation, and similarity scoring within a data scientist context.

  PinterestData ScientistData Manipulation (SQL/Python)Technical Screen
• Write SQL to compute max-overlap listsMedium

  This question evaluates proficiency in SQL data manipulation and set-based operations, focusing on deduplication, pairwise aggregation, and similarity metric computation (Jaccard) over relational tables.

  PinterestData ScientistData Manipulation (SQL/Python)Onsite
• Write SQL to rank categories by impressionsMedium

  This question evaluates proficiency in SQL data manipulation—covering joins, aggregations, grouping, tie-breaking, date-window logic, and scalability considerations such as indexing and query performance—in the Data Manipulation (SQL/Python) domain.

  PinterestData ScientistData Manipulation (SQL/Python)Onsite
• Write windowed retention and ARPU SQLMedium

  This question evaluates proficiency with SQL window functions, joins, aggregations and time-windowed analytics for computing retention and ARPU, as well as handling date truncation, UTC day boundaries and common data edge cases across user, session, and order tables.

  PinterestData ScientistData Manipulation (SQL/Python)Onsite
• Calculate Ultimate Loss by Policy TermMedium

  This question evaluates a candidate's ability to perform sequence-aware cumulative aggregation and partitioned data manipulation, assessing competency in calculating running totals (ultimate loss) across policy-term records.

  Plymouth Rock Assurance CorporationData ScientistData Manipulation (SQL/Python)Technical Screen
• Build the auction status tableHard

  This problem tests advanced SQL skills, specifically the ability to perform window functions, handle state transitions, and aggregate time-series data to reconstruct the final status of entities.

  Point72Data ScientistData Manipulation (SQL/Python)Online Assessment
• Convert Dates to Calendar Quarter Labels in SQL/PythonMedium

  This question evaluates a candidate's ability to perform date/time data transformation and normalization in SQL or Python, focusing on converting raw numeric dates into standardized quarter labels for aggregation and reporting.

  Point72Data ScientistData Manipulation (SQL/Python)Technical Screen
• Write SQL for top student per departmentMedium

  This question evaluates proficiency in SQL query formulation, including group-wise selection, joins, NULL handling, deterministic tie-breaking and ordering to identify a top-per-group student.

  Point72Data ScientistData Manipulation (SQL/Python)Technical Screen
• Explain pandas and SQL basicsEasy

  This question evaluates familiarity with pandas data structures (Series vs DataFrame), SQL filtering semantics (WHERE vs HAVING), and practical SQL aggregation for detecting duplicate records, assessing competencies in data manipulation, aggregation, and basic data quality checks.

  RbcroyalbankData EngineerData Manipulation (SQL/Python)Technical Screen
• Query US-based users and most active forumsMedium

  This question evaluates SQL data manipulation skills, including time-based filtering, joins, aggregations, null-safe handling, ratio computation, distinct counts, and top-N ranking to determine user residency and forum activity.

  RedditData ScientistData Manipulation (SQL/Python)Technical Screen
• Fetch and aggregate paginated team data via APIMedium

  This question evaluates API integration, pagination handling, robust error handling (retries/backoff and rate-limit awareness), data aggregation, sorting and top‑N selection skills within the Data Manipulation (SQL/Python) domain.

  ReevoSoftware EngineerData Manipulation (SQL/Python)Online Assessment
• Find Payroll Bookings in the Latest Active Month by CountryMedium

  Tests PostgreSQL date manipulation, filtering, and aggregation by determining the most recent month with active payroll bookings and summarizing totals by country.

  RipplingData AnalystData Manipulation (SQL/Python)Technical Screen
• Create OHLC Aggregates from Tick Data in PythonMedium

  This question evaluates a candidate's ability to perform time-series bucketing and OHLC aggregation from tick data, testing skills in data parsing, stateful aggregation, and handling missing-interval forward-filling; it falls under Data Manipulation (SQL/Python) and assesses practical application rather than purely conceptual understanding.

  RobinhoodData ScientistData Manipulation (SQL/Python)Onsite
• Identify Top Users with Declined Transactions in SQLMedium

  This question evaluates proficiency in SQL data manipulation, specifically aggregation, window functions, rolling 24-hour time-window analytics, NULL handling, and conditional classification via CASE WHEN within a fraud/risk detection scenario.

  RobinhoodData ScientistData Manipulation (SQL/Python)Technical Screen
• Identify Transactions During 'Golden' Membership PeriodMedium

  This question evaluates proficiency in temporal data manipulation and aggregation, covering SQL date-range joins, window functions for per-user ranking, and Python frequency counting of senders and receivers, and it falls under the Data Manipulation (SQL/Python) domain for data scientist roles.

  RobinhoodData ScientistData Manipulation (SQL/Python)Technical Screen
• List Transactions During Active 'Gold' Membership PeriodMedium

  This question evaluates proficiency in SQL-based data manipulation and temporal reasoning, specifically the ability to correlate membership periods with transaction records using relational operations.

  RobinhoodData ScientistData Manipulation (SQL/Python)Technical Screen
• Write SQL and Python for transaction analyticsMedium

  This question evaluates data manipulation competency in SQL and Python, covering normalization of transaction direction, JOINs and CASE logic, window functions for ranking and aggregation, and designing single-pass counting algorithms for streaming-like inputs.

  RobinhoodData ScientistData Manipulation (SQL/Python)Technical Screen
• Write SQL to localize trading drop contributorsMedium

  This question evaluates proficiency in SQL-based data manipulation and analytics, including cohorting by signup month, daily aggregation of executed trades per active user, segmentation by platform and geolocation, and competency with joins, timestamps, and operational flags.

  RobinhoodData ScientistData Manipulation (SQL/Python)Technical Screen
• Analyze Recent Orders Dataset with Python/pandasMedium

  This question evaluates proficiency in data manipulation and aggregation using Python/pandas, including extracting per-user maximums, computing overall summary statistics, and producing daily aggregates from time-stamped records.

  RobloxData ScientistData Manipulation (SQL/Python)Onsite
• Compare ROW_NUMBER, RANK, and DENSE_RANK for Top-k ResultsMedium

  Compare ROW_NUMBER, RANK, and DENSE_RANK with ties, exact-row limits, distinct score levels, and deterministic top-k semantics.

  RobloxData ScientistData Manipulation (SQL/Python)Technical Screen
• Compute ARPDAU/ARPPU by countryMedium

  This question evaluates data manipulation and analytics skills for computing user- and revenue-based metrics (ARPDAU, ARPPU), focusing on aggregation, multi-key joins, deduplication, time-window filtering, and correct revenue attribution.

  RobloxData ScientistData Manipulation (SQL/Python)Technical Screen
• Generate Friendship List with Acceptance Dates Using PandasMedium

  This question evaluates proficiency in data manipulation and relational reasoning using Python/pandas, focusing on transforming event logs into canonical pairwise relationships and extracting activation timestamps.

  RobloxData ScientistData Manipulation (SQL/Python)Onsite
• Match requests and accepts into friendships in SQLMedium

  This question evaluates a candidate's competency in SQL data manipulation, focusing on temporal joins, deduplication of bidirectional relationships, selecting the earliest valid event, and reasoning about event-order constraints.

  RobloxData ScientistData Manipulation (SQL/Python)Onsite
• Write SQL for ads metrics and variabilityMedium

  This question evaluates a candidate's ability to perform SQL-based data manipulation and analytics, including deduplication of events, computation of daily and campaign-level metrics (CTR, CPC), and calculation of statistical summaries (mean and standard deviation) and outlier detection over a specified date range.

  RobloxData ScientistData Manipulation (SQL/Python)Technical Screen
• Write SQL for influence score and follower growthEasy

  This question evaluates SQL data manipulation competencies such as joins, aggregations, distinct counts, window functions, and time-based grouping for computing an influence score (engagements on a user's posts plus follower count) and monthly follower growth, and it falls under Data Manipulation (SQL/Python).

  RobloxData ScientistData Manipulation (SQL/Python)Technical Screen
• Write SQL to flag suspect payments and chargebacksMedium

  This question evaluates proficiency with SQL conditional logic (CASE WHEN), JOINs, date/time arithmetic, aggregation, and encoding business rules for flagging suspect payments and chargebacks, and is categorized under Data Manipulation (SQL/Python) for a Data Scientist role.

  RobloxData ScientistData Manipulation (SQL/Python)HR Screen
• Write SQL for profit, growth, retentionMedium

  This question evaluates a candidate's ability to perform advanced SQL data manipulation — specifically rolling-window aggregations, revenue and gross profit computations that account for refunds, order status and test flags, plus cohort retention and week-over-week growth analysis.

  RokuData ScientistData Manipulation (SQL/Python)Technical Screen
• Debug ML pipeline and build text parserMedium

  This question evaluates practical machine learning engineering skills, including debugging unfamiliar codebases, parsing noisy text data, and identifying subtle defects such as train/test leakage or shape mismatches. It tests applied Python proficiency and systematic reasoning under time pressure, core competencies assessed in ML engineer technical interviews.

  Scale AIMachine Learning EngineerData Manipulation (SQL/Python)Technical Screen