Amazon interview questions

Showing 61–90 of 653 results

• Choose Between Fine-Tuning and RAG for Client ChatbotHard

  This interview question evaluates core ML concepts, assumptions, math intuition, training/evaluation trade-offs, and practical failure modes in a realistic interview setting. A strong answer for Choose Between Fine-Tuning and RAG for Client Chatbot states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  AmazonData ScientistMachine LearningTechnical Screen
• Choose Effective Graphs for Data ExplorationMedium

  Evaluates chart selection for exploratory data analysis across categorical and numerical variables. Strong answers explain pie charts, distributions, comparisons, relationships, missingness, and when visuals mislead.

  AmazonData ScientistAnalytics & ExperimentationOnsite
• Choose Models for Imbalanced Data and Time-Series ForecastingHard

  Choose Models for Imbalanced Data and Time-Series Forecasting evaluates core ML concepts, assumptions, math intuition, training/evaluation trade-offs, and practical failure modes in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  AmazonData ScientistMachine LearningTechnical Screen
• Choose Work-Style Assessment ResponsesMedium

  Tests a candidate's ability to navigate forced-choice and situational judgment items in an automated work-style assessment, balancing trade-offs between competing leadership values such as speed versus quality, autonomy versus consensus, and maintaining internal consistency.

  AmazonSoftware EngineerBehavioral & LeadershipOnline Assessment
• Choose regularization norms and model formulationsMedium

  This question evaluates understanding of regularization norms (L0, L1, L2, L∞), objective formulations for linear and logistic regression, and practical competencies in handling collinearity, outliers, feature scaling, and optimization difficulty.

  AmazonData ScientistMachine LearningTechnical Screen
• Circular Drone Hub Delivery RouteHard

  This question evaluates a candidate's ability to work with circular array structures and prefix sums to compute shortest distances on a ring topology. It tests practical coding skill in efficiently handling wraparound arithmetic and cumulative sums, a common pattern in algorithm interviews assessing array manipulation and optimization under scale constraints.

  AmazonSoftware EngineerCoding & AlgorithmsOnline Assessment
• Clarify internship availability and conversionEasy

  This question evaluates a candidate's clarity about availability, logistical constraints, and intent to convert an internship into a full-time role, testing communication of exact dates, work authorization, relocation, and academic conflicts.

  AmazonSoftware EngineerBehavioral & LeadershipTechnical Screen
• Communicating Top-Down ChangeMedium

  Practice communicating top-down operational change across peer managers, direct-report managers, and front-line employees. The solution covers safety, efficiency, cost goals, audience-specific messaging, pilots, feedback loops, metrics, guardrails, and sustainment.

  AmazonProduct ManagerBehavioral & LeadershipOnline Assessment
• Compare RNNs and Transformers for Long-Sequence Text ClassificationMedium

  Compare RNNs and Transformers for Long-Sequence Text Classification evaluates core ML concepts, assumptions, math intuition, training/evaluation trade-offs, and practical failure modes in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  AmazonData ScientistMachine LearningTechnical Screen
• Compare Random Forests vs Gradient Boosting rigorouslyHard

  This question evaluates a candidate's ability to choose and configure tree-based models (Random Forest vs. Gradient Boosted Trees), handle high-cardinality categorical features and missingness, mitigate class imbalance and label noise, produce reliable feature importance and calibration, and design an experiment and inference strategy that meets strict latency and resource constraints. It is commonly asked in the Machine Learning domain for Data Scientist roles to assess trade-offs in bias–variance, robustness to correlated features and noise, hyperparameter and encoding decisions, experiment design and evaluation, and tests both conceptual understanding and practical application for production-ready systems.

  AmazonData ScientistMachine LearningTechnical Screen
• Compare Regularization Techniques and Their Use CasesMedium

  Evaluates model evaluation, regularization, and regression basics for predictive analytics. Strong answers define precision and recall, compare L1, L2, L0, and L-infinity penalties, state OLS assumptions, and contrast linear and logistic regression formulas and losses.

  AmazonData ScientistMachine LearningTechnical Screen
• Compare Tableau live vs extract and filtersHard

  This question evaluates competency in BI data architecture and large-scale dashboard performance, covering extract vs live connections, refresh and incremental strategies, query pushdown versus in-tool processing, row-level security, filter order-of-operations, performance tuning, and platform migration mapping within the Analytics & Experimentation domain for a Data Scientist role. It is commonly asked to assess trade-off reasoning across data freshness, storage, query performance and security for a 100M-row fact table, combining practical application-level decisions (implementation and tuning) with conceptual understanding (filter ordering, calculations, and migration pitfalls) when moving between BI platforms.

  AmazonData ScientistAnalytics & ExperimentationOnsite
• Compute Edit DistanceMedium

  Tests dynamic programming and string manipulation by calculating the minimum number of single-character edits (insertions, deletions, substitutions) needed to transform one string into another.

  AmazonSoftware EngineerCoding & AlgorithmsTechnical 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 Latencies and Search Grid PathMedium

  This question evaluates parsing and processing of timestamped event logs to compute message latencies and implementation of constrained grid path search, testing competencies in file I/O, record alignment/matching, time-delta computation, graph traversal, and stateful backtracking.

  AmazonSoftware EngineerCoding & AlgorithmsTechnical Screen
• 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 an A/B test p-value by handMedium

  This question evaluates understanding of hypothesis testing for proportions, specifically the two-proportion z-test, p-value computation and interpretation of statistical significance in an A/B test with binary outcomes.

  AmazonData ScientistStatistics & MathTechnical Screen
• Compute and Explain Mean and VarianceMedium

  This question tests your understanding of statistical estimators for mean and variance, the derivation of Bessel's correction, catastrophic cancellation in naive computations, and numerically stable single-pass algorithms like Welford's.

  AmazonApplied ScientistStatistics & MathOnsite
• Compute and interpret quantile loss vs RMSEMedium

  This question tests the ability to distinguish between point-prediction error metrics and quantile loss, and the practical implications of choosing an evaluation metric in the context of asymmetric business costs.

  AmazonData ScientistStatistics & MathTechnical Screen
• Compute array modes with ties and no-mode ruleMedium

  This question evaluates algorithm design and data-structure skills—frequency counting, hashing, and handling edge cases such as ties and a no-mode rule—within the coding and algorithms domain for data scientist roles.

  AmazonData ScientistCoding & AlgorithmsTechnical 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 delay times in priority arrayMedium

  This question evaluates algorithmic efficiency in array processing, specifically reasoning about nearest-smaller relationships and the use of auxiliary structures to achieve subquadratic performance.

  AmazonSoftware EngineerCoding & AlgorithmsOnline Assessment
• Compute fruit spoilage time in a gridMedium

  This question evaluates a candidate's understanding of graph traversal concepts, grid-based state propagation, and reachability analysis in time-evolving systems.

  AmazonSoftware EngineerCoding & AlgorithmsOnsite
• 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 maximum distinct-product pickup daysMedium

  This question evaluates a candidate's understanding of greedy and resource-allocation strategies along with combinatorial reasoning to maximize operations under discrete constraints.

  AmazonSoftware EngineerCoding & AlgorithmsOnline Assessment
• Compute minimal operations and optimal server pairingEasy

  This two-part question evaluates array manipulation and optimization skills—specifically range-update minimization to enforce a nondecreasing sequence and greedy pairing/matching to maximize summed primary resources—testing algorithm design, complexity reasoning, and constraint handling.

  AmazonSoftware EngineerCoding & AlgorithmsOnline Assessment
• Compute optimal locker placement with obstaclesMedium

  This interview question evaluates algorithm design, data structures, correctness, complexity, edge cases, and implementation details in a realistic interview setting. A strong answer for Compute optimal locker placement with obstacles states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  AmazonSoftware EngineerCoding & AlgorithmsOnsite
• Compute p-values, CIs, and adjust multiplesMedium

  This question evaluates a candidate's proficiency in statistical inference, covering hypothesis testing and test selection, confidence interval construction, multiple-testing corrections, error types, and asymptotic reasoning.

  AmazonData ScientistStatistics & MathTechnical Screen
• Compute peak parking lot occupancy intervalsMedium

  This question evaluates a candidate's competency in temporal interval reasoning, event-stream processing, and handling incomplete or unsorted logs to determine peak concurrent occupancy.

  AmazonSoftware EngineerCoding & AlgorithmsTechnical Screen
• Compute product excluding index without divisionMedium

  This question evaluates array-manipulation and algorithmic problem-solving skills, focusing on computing a product-for-each-index using prefix and postfix accumulation concepts while reasoning about edge cases such as zeros, negative numbers, and integer overflow.

  AmazonSoftware EngineerCoding & AlgorithmsOnsite