Data Manipulation (SQL/Python) interview questions

Showing 31–60 of 446 results

• Calculate Rolling 7-Day Sum of Answers by DeviceMedium

  This question evaluates proficiency in time-series aggregation and group-wise metric computation on structured data, specifically calculating rolling seven-day sums of answers per device, and assesses familiarity with data manipulation using SQL or Python.

  AmazonBusiness Intelligence EngineerData Manipulation (SQL/Python)Onsite
• Calculate Weekly, Monthly Hours Watched by Premium UsersMedium

  This question evaluates a candidate's ability to perform time-based aggregation and data manipulation in SQL and Python, including grouping, filtering by subscription status, and converting minute-level metrics into hours.

  AmazonData ScientistData Manipulation (SQL/Python)Technical Screen
• Compute First Order Proportions by Day and CategoryMedium

  This question evaluates a candidate's ability to perform event-level data manipulation and customer-level deduplication using SQL or Python, focusing on calculating the proportion of orders that represent a customer's first purchase by day and category.

  AmazonBusiness Intelligence EngineerData Manipulation (SQL/Python)Onsite
• Compute a Rolling Seven-Day Revenue Sum with Missing DatesMedium

  Tests the ability to write a PostgreSQL query that generates a continuous daily time series per seller, handles missing sales dates, filters by order status, and computes a rolling 7-day revenue sum.

  AmazonData ScientistData Manipulation (SQL/Python)Technical Screen
• Compute daily work hours from in/out eventsMedium

  This question evaluates a candidate's ability to manipulate time-series punch-event data, including temporal pairing of in/out events, splitting shifts across calendar days, rounding and aggregating hours, and detecting data-quality issues such as overlaps or orphaned events using SQL or Python.

  AmazonData ScientistData Manipulation (SQL/Python)Onsite
• Compute join counts and window ranksMedium

  This question evaluates SQL data-manipulation and analytic competencies including join semantics, set operations, window functions, view versus materialized view trade-offs, key and partition design, and query-tuning techniques in the Data Manipulation (SQL/Python) domain.

  AmazonData ScientistData Manipulation (SQL/Python)Onsite
• Compute unique visitors per department from clicksMedium

  This question evaluates a candidate's skill in data manipulation and deduplication using SQL/Python, specifically mapping hierarchical product metadata to departments and computing unique user counts without double-counting, and is categorized under Data Manipulation (SQL/Python).

  AmazonSoftware EngineerData Manipulation (SQL/Python)Technical Screen
• Consolidate and Rank Global Salaries in USDMedium

  This question evaluates data consolidation, currency normalization, and ranking competencies, measuring the ability to combine country-specific employee data and convert salaries into a common USD basis within the Data Manipulation (SQL/Python) domain.

  AmazonData ScientistData Manipulation (SQL/Python)Technical Screen
• Count Customers Buying Both 'Kindle' and 'Alexa'Medium

  This question evaluates proficiency in relational data manipulation and set-based reasoning, assessing SQL competency such as joins, aggregation, and identifying distinct customers who purchased multiple specified products.

  AmazonBusiness Intelligence EngineerData Manipulation (SQL/Python)Onsite
• Create Country-Level Spend Report Using PandasMedium

  This question evaluates proficiency in data manipulation using pandas, including relational joins and aggregation to produce country-level summary statistics like totals and averages.

  AmazonData ScientistData Manipulation (SQL/Python)Technical Screen
• Create SQL Queries for Sales and Customer AnalysisMedium

  This question evaluates proficiency in SQL-based data manipulation, covering aggregation and dispersion metrics, null handling, relational joins, and window functions for ranking customers.

  AmazonData ScientistData Manipulation (SQL/Python)Technical Screen
• Design Incremental Load Process for Large Relational TableMedium

  This question evaluates understanding of incremental loading, change-data-capture, idempotent upsert logic, deduplication, partitioning, and data quality controls for large relational tables within the Data Manipulation (SQL/Python) domain.

  AmazonData ScientistData Manipulation (SQL/Python)Technical Screen
• Design student–course data models and SQLMedium

  This question evaluates data modeling, database design, and SQL proficiency—covering normalization, schema constraints, indexing, star-schema warehousing, slowly changing dimensions, partitioning strategies, and complex analytic queries—in the domain of relational databases and data warehousing (Data Manipulation/SQL).

  AmazonData ScientistData Manipulation (SQL/Python)Onsite
• Diagnose MySQL joins and GROUP BY/HAVING errorsMedium

  This question evaluates mastery of SQL join semantics (LEFT, RIGHT, FULL OUTER emulation, CROSS), NULL equality and duplicate handling, GROUP BY/HAVING behavior under ONLY_FULL_GROUP_BY, aggregation result shaping and type/format conversions in the Data Manipulation (SQL/Python) domain.

  AmazonData ScientistData Manipulation (SQL/Python)Online Assessment
• Explore Subscription Patterns and Status Transitions with SQL/PandasMedium

  This question evaluates proficiency in time-series and event-sequence data manipulation, temporal aggregation, and data quality validation using SQL and pandas, focusing on identifying status transitions and date-based uniqueness.

  AmazonData ScientistData Manipulation (SQL/Python)Technical Screen
• Find Top Three Books by City in Recent MonthsMedium

  This question evaluates proficiency in time-windowed aggregation, group-level ranking and window-function usage on relational datasets, emphasizing handling of recent date ranges and top-N selection.

  AmazonBusiness Intelligence EngineerData Manipulation (SQL/Python)Onsite
• Find Top-3 Salaries Per Department Using SQLMedium

  This question evaluates SQL data manipulation competency, focusing on group-wise aggregation, ordering, and selecting the top-N salary values within each department.

  AmazonData ScientistData Manipulation (SQL/Python)Onsite
• Find recommended friend pairs by shared songsMedium

  This question evaluates data manipulation skills—specifically SQL joins and anti-joins, deduplication, date/time handling, aggregation, and pairwise overlap computation—within the Data Manipulation (SQL/Python) domain at a practical implementation level requiring query-writing to produce per-day overlap counts.

  AmazonData ScientistData Manipulation (SQL/Python)Technical Screen
• Find top-spend categories per customer with rankingMedium

  This question evaluates proficiency with SQL aggregation and JOINs, analytic/window functions (RANK()), tie and NULL/edge-case handling, and percent-of-total calculations for per-customer spend analysis.

  AmazonData ScientistData Manipulation (SQL/Python)Onsite
• Identify First Daily Order for Each MerchantMedium

  This question evaluates proficiency in data manipulation with SQL and related tools, focusing on identifying the first transaction per merchant for each calendar date and handling ordering and deduplication within grouped transactional records.

  AmazonData ScientistData Manipulation (SQL/Python)Onsite
• Identify Most Popular First-Watched Movie in Viewing HistoryMedium

  This question evaluates proficiency in data manipulation and temporal event analysis, assessing competency in identifying per-user earliest views and aggregating frequencies using SQL or Python.

  AmazonBusiness Intelligence EngineerData Manipulation (SQL/Python)Onsite
• Identify Top-Buying Customers Daily with SQL QueryMedium

  This question evaluates a candidate's competency in SQL data manipulation, including aggregation over calendar dates, grouping by customer, and handling ties when determining top buyers.

  AmazonBusiness Intelligence EngineerData Manipulation (SQL/Python)Onsite
• Identify Unique Unordered City Pairs in Flight LogMedium

  This question evaluates competency in data manipulation, deduplication of symmetric relationships, and handling unordered pairs within relational or tabular datasets using SQL or Python/Pandas.

  AmazonBusiness Intelligence EngineerData Manipulation (SQL/Python)Onsite
• Illustrate SQL Join Results with Duplicate KeysMedium

  This question evaluates understanding of SQL join semantics and the effect of duplicate key values on result cardinality, assessing competency in relational data manipulation and reasoning about result sets.

  AmazonBusiness Intelligence EngineerData Manipulation (SQL/Python)Onsite
• List Top Customers and Monthly Order Counts in SQLMedium

  This question evaluates a candidate's ability to perform SQL aggregation, grouping, top‑N selection, and date-based summarization to compute customer spend and chronological monthly order counts.

  AmazonData ScientistData Manipulation (SQL/Python)Technical Screen
• Manipulate time-series with Pandas groupbyMedium

  This question evaluates proficiency in time-series data manipulation, including timezone-aware datetime parsing and DST handling, groupby and rolling-window aggregations, resampling to fill calendar gaps, and user-level retention and DAU calculations using idiomatic, vectorized Pandas or SQL operations.

  AmazonSoftware EngineerData Manipulation (SQL/Python)Technical Screen
• Reason About Composite Join Keys and Predicate PlacementEasy

  This question tests your understanding of composite join keys, the critical semantic difference between filtering in the `ON` versus `WHERE` clause during joins, and techniques to prevent row fan-out.

  AmazonBusiness Intelligence EngineerData Manipulation (SQL/Python)Technical Screen
• Select Top Customers Using Transaction Data FiltersMedium

  This question evaluates a candidate's ability to perform data aggregation, filtering, ranking, and deterministic tie-breaking on transactional datasets, assessing skills in Data Manipulation (SQL/Python).

  AmazonData ScientistData Manipulation (SQL/Python)Technical Screen
• Transform event logs with subscription windows in pandasMedium

  This question evaluates a candidate's competency in time-based event attribution and temporal interval manipulation using pandas, covering interval merging, subscription-aligned revenue aggregation, anomaly flagging, and handling edge cases like overlapping or back-to-back subscriptions.

  AmazonData ScientistData Manipulation (SQL/Python)Onsite
• Understand SQL: DELETE vs TRUNCATE, VIEW vs TABLE, CROSS JOINMedium

  This question evaluates a candidate's understanding of core SQL concepts and data-manipulation competencies, covering the behavior of data-deletion operations, distinctions between logical and physical data structures, and join semantics.

  AmazonData ScientistData Manipulation (SQL/Python)Technical Screen