Amazon interview questions

Showing 421–450 of 653 results

• Highest-total path through a binary tree encoded in level orderMedium

  A coding problem that asks for the largest sum of any non-empty path in a binary tree given in level order, where a path may bend at one node and need not include the root or a leaf. It tests recursive reasoning over trees, correct handling of negative values, and careful decoding of the input format.

  AmazonSoftware EngineerCoding & AlgorithmsOther
• Hotel Keycard System DesignMedium

  Practice a hotel keycard product and system design interview that covers guest experience, access control, offline validation, cryptographic credential handling, revocation, audit logs, integrations, security threats, and hotel operational failure modes.

  AmazonProduct ManagerProduct / Decision MakingOnsite
• How to evaluate adding video ads in a gameEasy

  This question tests your ability to design an experimentation framework and evaluate the business impact of introducing interstitial video ads in a mobile game, focusing on the trade-off between short-term revenue and long-term user retention.

  AmazonData ScientistAnalytics & ExperimentationTechnical Screen
• How would you evaluate adding video ads?Medium

  This question evaluates a data scientist's competencies in experimental design, causal inference, metrics selection, and product monetization analytics within the Analytics & Experimentation domain, focusing on measuring the impact of video ads on revenue, engagement, retention, and lifetime value.

  AmazonData ScientistAnalytics & ExperimentationTechnical Screen
• How would you test a price increase?Medium

  An Amazon data scientist technical screen case on pricing: decide whether to raise the price of a subscription product, choose the price point and packaging, and design the experiment that proves it. The answer covers per-visitor profit and LTV metrics, guardrails, elasticity estimation, randomization on the billing account, and how to handle existing subscribers at renewal. It also fixes the two traps that sink most answers: judging a subscription price test on short-horizon revenue, and measuring churn only among users who converted.

  AmazonData ScientistAnalytics & ExperimentationTechnical Screen
• 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 P-Value Limitations and Complementary ApproachesMedium

  Identify P-Value Limitations and Complementary Approaches evaluates statistical assumptions, formulas, estimation strategy, uncertainty, edge cases, and interpretation 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 ScientistStatistics & MathOnsite
• 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
• Implement Batch Gradient Descent for Linear RegressionEasy

  Amazon data scientist machine learning prompt on writing batch gradient descent pseudocode for linear regression, including MSE loss, vectorized gradients, parameter updates, stopping criteria, and implementation checks.

  AmazonData ScientistMachine LearningTechnical Screen
• Implement Cache and Count ComponentsMedium

  This question evaluates a candidate's ability to design and implement efficient data structures for constant-time operations and to reason about graph connectivity and component counting, falling under the Coding & Algorithms domain and requiring practical implementation with algorithmic complexity reasoning.

  AmazonSoftware EngineerCoding & AlgorithmsTechnical Screen
• Implement Cache and Rotate MatrixMedium

  This two-part question evaluates competency in data structure design and in-place array manipulation, specifically the ability to implement an LRU cache with O(1)-average operations (including recency management, updates, and evictions) and to rotate an n x n matrix 90 degrees clockwise in place without extra space.

  AmazonSoftware EngineerCoding & AlgorithmsOnsite
• Implement Datacenter Router CommandsHard

  This problem tests your ability to manage stateful data structures, perform geometric calculations (Haversine formula), and implement multi-level sorting logic to simulate router command processing.

  AmazonSoftware EngineerCoding & AlgorithmsTechnical Screen
• Implement Event Filtering and Queue RoutingMedium

  Implement an in-memory event routing engine that matches nested JSON events against subscriber filter policies and routes them in subscription order.

  AmazonSoftware EngineerCoding & AlgorithmsTechnical Screen
• Implement Function to Determine Mode and Prime NumbersMedium

  This question evaluates algorithmic problem-solving skills, including frequency analysis for mode computation, correct handling of ties and uniqueness edge cases, and basic number-theoretic competency for prime identification.

  AmazonData ScientistCoding & AlgorithmsTechnical Screen
• Implement Interview Coding ProblemsEasy

  This question evaluates implementation and algorithmic skills across core data structures and array/interval algorithms, specifically hashing (hash map design including collision handling and resizing), interval merging and intersection logic, and array optimization problems such as maximum container area and trapped rainwater.

  AmazonSoftware EngineerCoding & AlgorithmsTechnical Screen
• Implement K-means and solve interval/frequency tasksMedium

  This multi-part question evaluates understanding of K-means clustering, interval-merging algorithms, and frequency-based top-k retrieval, assessing competencies in unsupervised learning concepts, algorithmic problem solving, data structures, and complexity analysis.

  AmazonApplied ScientistCoding & AlgorithmsOnsite
• Implement LRU cache with O(1) opsMedium

  This interview question evaluates algorithm design, data structures, correctness, complexity, edge cases, and implementation details in a realistic interview setting. A strong answer for Implement LRU cache with O(1) ops states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  AmazonSoftware EngineerCoding & AlgorithmsOnsite
• Implement Multi-Head Attention from Scratch in NumPyMedium

  This problem tests your understanding of Transformer components by requiring an implementation of Multi-Head Scaled Dot-Product Attention using only NumPy, emphasizing matrix manipulations and numerical stability.

  AmazonApplied ScientistCoding & AlgorithmsOnsite
• Implement Multi-Head Attention with a KV Cache and Cached Grouped-Query AttentionMedium

  Implement causal multi-head attention from scratch, add a key-value cache for token-by-token decoding, and extend the forward pass to grouped-query attention, where several query heads share one key and value head. Tests tensor shape discipline, masking with cached positions, and reasoning about decode compute and cache memory.

  AmazonMachine Learning EngineerMachine LearningOnsite
• Implement Optimal Bucket BatchingHard

  This question evaluates algorithm design and optimization skills, focusing on combinatorial partitioning and batching strategies, efficient utilization of parallel GPUs, and time/space complexity analysis relevant to ML data pipelines.

  AmazonApplied ScientistCoding & AlgorithmsTechnical Screen
• Implement PyTorch training loopMedium

  This question evaluates practical implementation skills in PyTorch, focusing on model and device management, batch-wise training mechanics, gradient handling, and optimizer interaction.

  AmazonApplied ScientistCoding & AlgorithmsOnsite
• Implement Regular Expression MatchingEasy

  This problem tests your ability to handle recursive string matching problems using dynamic programming, specifically focusing on handling wildcard and repetition operators.

  AmazonSoftware EngineerCoding & AlgorithmsTechnical Screen
• Implement Reservoir Sampling; Analyze Time and Space ComplexityMedium

  This question evaluates understanding of streaming randomized algorithms and probabilistic reasoning—specifically reservoir sampling—along with analysis of time and space complexity and use of hash-based structures for duplicate detection; it is in the Coding & Algorithms domain and tests both conceptual probability invariants and practical implementation skills. Such problems are commonly asked to gauge a candidate's ability to design one-pass, linear-time solutions for unbounded data streams and to reason about algorithmic correctness and resource bounds in technical interviews.

  AmazonData ScientistCoding & AlgorithmsOnsite
• Implement Review Content ModerationMedium

  This problem tests your ability to design a content moderation service that efficiently checks for prohibited words and maintains a running count of violations per user using a stateful metadata store.

  AmazonSoftware EngineerSoftware Engineering FundamentalsOnline Assessment
• Implement SGD for linear regression and derive gradientsMedium

  This question evaluates understanding of gradient-based optimization and linear model training, including competency in deriving gradients for mean squared error and implementing stochastic (or mini-batch) gradient descent for parameter estimation.

  AmazonApplied ScientistMachine LearningTechnical Screen
• Implement Top-p (Nucleus) Sampling in NumPyMedium

  This problem tests your ability to implement the nucleus sampling algorithm from scratch using NumPy, focusing on probability distribution processing and efficient array masking.

  AmazonApplied ScientistCoding & AlgorithmsOnsite
• Implement a Half-Open Interval Range ModuleHard

  This problem tests proficiency with balanced binary search trees or interval management techniques to efficiently track and manipulate sets of half-open intervals.

  AmazonSoftware EngineerCoding & AlgorithmsTechnical Screen