Data Manipulation (SQL/Python) Interview Questions

Showing 31–60 of 446 results

• AmazonMedium·Business Intelligence Engineer
  Calculate Rolling 7-Day Sum of Answers by Device

  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.

  Data Manipulation (SQL/Python)Onsite
• AmazonMedium·Data Scientist
  Calculate Weekly, Monthly Hours Watched by Premium Users

  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.

  Data Manipulation (SQL/Python)Technical Screen
• AmazonMedium·Business Intelligence Engineer
  Compute First Order Proportions by Day and Category

  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.

  Data Manipulation (SQL/Python)Onsite
• AmazonMedium·Data Scientist
  Compute a Rolling Seven-Day Revenue Sum with Missing Dates

  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.

  Data Manipulation (SQL/Python)Technical Screensqlpostgresqlwindow-functions
• AmazonMedium·Data Scientist
  Compute daily work hours from in/out events

  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.

  Data Manipulation (SQL/Python)Onsite
• AmazonMedium·Data Scientist
  Compute join counts and window ranks

  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.

  Data Manipulation (SQL/Python)Onsite
• AmazonMedium·Software Engineer
  Compute unique visitors per department from clicks

  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).

  Data Manipulation (SQL/Python)Technical Screen
• AmazonMedium·Data Scientist
  Consolidate and Rank Global Salaries in USD

  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.

  Data Manipulation (SQL/Python)Technical Screen
• AmazonMedium·Business Intelligence Engineer
  Count Customers Buying Both 'Kindle' and 'Alexa'

  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.

  Data Manipulation (SQL/Python)Onsite
• AmazonMedium·Data Scientist
  Create Country-Level Spend Report Using Pandas

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

  Data Manipulation (SQL/Python)Technical Screen
• AmazonMedium·Data Scientist
  Create SQL Queries for Sales and Customer Analysis

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

  Data Manipulation (SQL/Python)Technical Screen
• AmazonMedium·Data Scientist
  Design Incremental Load Process for Large Relational Table

  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.

  Data Manipulation (SQL/Python)Technical Screen
• AmazonMedium·Data Scientist
  Design student–course data models and SQL

  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).

  Data Manipulation (SQL/Python)Onsite
• AmazonMedium·Data Scientist
  Diagnose MySQL joins and GROUP BY/HAVING errors

  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.

  Data Manipulation (SQL/Python)Online Assessment
• AmazonMedium·Data Scientist
  Explore Subscription Patterns and Status Transitions with SQL/Pandas

  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.

  Data Manipulation (SQL/Python)Technical Screen
• AmazonMedium·Business Intelligence Engineer
  Find Top Three Books by City in Recent Months

  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.

  Data Manipulation (SQL/Python)Onsite
• AmazonMedium·Data Scientist
  Find Top-3 Salaries Per Department Using SQL

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

  Data Manipulation (SQL/Python)Onsite
• AmazonMedium·Data Scientist
  Find recommended friend pairs by shared songs

  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.

  Data Manipulation (SQL/Python)Technical Screen
• AmazonMedium·Data Scientist
  Find top-spend categories per customer with ranking

  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.

  Data Manipulation (SQL/Python)Onsite
• AmazonMedium·Data Scientist
  Identify First Daily Order for Each Merchant

  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.

  Data Manipulation (SQL/Python)Onsite
• AmazonMedium·Business Intelligence Engineer
  Identify Most Popular First-Watched Movie in Viewing History

  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.

  Data Manipulation (SQL/Python)Onsite
• AmazonMedium·Business Intelligence Engineer
  Identify Top-Buying Customers Daily with SQL Query

  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.

  Data Manipulation (SQL/Python)Onsite
• AmazonMedium·Business Intelligence Engineer
  Identify Unique Unordered City Pairs in Flight Log

  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.

  Data Manipulation (SQL/Python)Onsite
• AmazonMedium·Business Intelligence Engineer
  Illustrate SQL Join Results with Duplicate Keys

  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.

  Data Manipulation (SQL/Python)Onsite
• AmazonMedium·Data Scientist
  List Top Customers and Monthly Order Counts in SQL

  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.

  Data Manipulation (SQL/Python)Technical Screen
• AmazonMedium·Software Engineer
  Manipulate time-series with Pandas groupby

  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.

  Data Manipulation (SQL/Python)Technical Screen
• AmazonEasy·Business Intelligence Engineer
  Reason About Composite Join Keys and Predicate Placement

  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.

  Data Manipulation (SQL/Python)Technical Screensqljoinsdata-manipulation
• AmazonMedium·Data Scientist
  Select Top Customers Using Transaction Data Filters

  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).

  Data Manipulation (SQL/Python)Technical Screen
• AmazonMedium·Data Scientist
  Transform event logs with subscription windows in pandas

  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.

  Data Manipulation (SQL/Python)Onsite
• AmazonMedium·Data Scientist
  Understand SQL: DELETE vs TRUNCATE, VIEW vs TABLE, CROSS JOIN

  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.

  Data Manipulation (SQL/Python)Technical Screen