Amazon interview questions

Showing 451–480 of 653 results

• Implement a Min-Priority Queue Without a Library HeapMedium

  Tests implementation of a binary min-heap from scratch, including array-based representation, sift-up, and sift-down operations, without relying on built-in priority queue libraries.

  AmazonSoftware EngineerCoding & AlgorithmsOnsite
• Implement a Single-Node Priority Pub/SubMedium

  This question evaluates understanding of concurrent system design, in-memory publish–subscribe semantics, prioritized delivery ordering, API and data structure selection, and thread-safety within the Software Engineering Fundamentals domain.

  AmazonSoftware EngineerSoftware Engineering FundamentalsOnsite
• Implement a formatted shell script outputMedium

  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 a formatted shell script output states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  AmazonSoftware EngineerCoding & AlgorithmsTechnical Screen
• Implement a high-throughput web crawler safelyMedium

  This question evaluates competency in concurrent systems, scalable data ingestion, and algorithmic design for a high-throughput web crawler, covering URL normalization, deduplication, per-host rate limiting, fault-tolerant checkpointing, and bounded-memory queueing.

  AmazonData ScientistCoding & AlgorithmsTechnical Screen
• Implement a robust shell CLIMedium

  This question evaluates proficiency in POSIX-compliant shell scripting, including positional argument parsing and validation, ISO 8601 UTC timestamp generation, file metadata extraction for license determination, construction of user@hostname, error handling and exit-code semantics, and handling of non-ASCII file paths.

  AmazonSoftware EngineerCoding & AlgorithmsTechnical Screen
• Implement ad matching and delivery routingMedium

  Implement ad matching and delivery routing evaluates algorithm design, data structures, correctness, complexity, edge cases, and implementation details in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  AmazonSoftware EngineerCoding & AlgorithmsTechnical Screen
• Implement an Accessible Autocomplete Search ComponentMedium

  Design and implement an accessible, race-condition-resilient autocomplete search component in React or modern JavaScript, focusing on debounced queries, ARIA combobox semantics, and keyboard navigation.

  AmazonFrontend EngineerSoftware Engineering FundamentalsTechnical Screen
• Implement and Analyze 'Merge k Sorted Lists' AlgorithmMedium

  This question evaluates algorithm design skills and complexity analysis for merging multiple sorted sequences, measuring proficiency with appropriate data structures, time-space trade-offs, and correctness reasoning.

  AmazonData ScientistCoding & AlgorithmsOnsite
• Implement and Analyze an LRU Query CacheMedium

  Implement a fixed-capacity least-recently-used cache for integer query results with expected constant-time get and put operations. Refresh recency on hits and updates, evict the correct key on overflow, and handle misses and duplicate puts precisely.

  AmazonSoftware EngineerCoding & AlgorithmsTechnical Screen
• Implement basic calculatorMedium

  This question evaluates skill in parsing and evaluating arithmetic expressions with integer operands and the operators +, -, *, and /, focusing on operator precedence and evaluation order within the Coding & Algorithms domain.

  AmazonSoftware EngineerCoding & AlgorithmsOnline Assessment
• Implement binary search lower/upper boundsMedium

  Implement lower_bound and upper_bound on a non-decreasing sorted array using iterative binary search in O(log n) time and O(1) space. This Amazon ML engineer screen tests precise boundary handling, loop-invariant reasoning, and edge cases like duplicates, empty arrays, and out-of-range targets.

  AmazonApplied ScientistCoding & AlgorithmsTechnical Screen
• Implement decoder-only GPT-style transformerMedium

  This question evaluates implementation-level mastery of transformer architectures, including multi-head masked self-attention, position-wise feed-forward networks, residual connections, and precise tensor-shape reasoning for a decoder-only language model, within the Coding & Algorithms domain of deep learning and neural network engineering.

  AmazonApplied ScientistCoding & AlgorithmsOnsite
• Implement integer division without using divisionMedium

  This question evaluates proficiency in implementing low-level integer arithmetic and algorithmic optimization, including correct handling of overflow and edge cases within 32-bit signed integer constraints.

  AmazonApplied ScientistCoding & AlgorithmsOnsite
• Implement inventory allocation with backordersMedium

  Implement inventory allocation with backorders evaluates algorithm design, data structures, correctness, complexity, edge cases, and implementation details in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  AmazonApplied ScientistCoding & AlgorithmsOnsite
• Implement list overlap and dense-ranked word frequenciesMedium

  This question evaluates a data scientist's ability to implement efficient multiset intersection and streaming top-k word-frequency computations, covering duplicate-aware counting, dense-ranking, Unicode handling, and strict time/space constraints.

  AmazonData ScientistCoding & AlgorithmsOnsite
• Implement robust debounce and throttle in JavaScriptMedium

  This question evaluates a developer's ability to implement debounce and throttle utilities in JavaScript, testing competencies in asynchronous timing, timer management, cancellation/flush semantics, error propagation, and handling edge cases such as rapid bursts, timer drift, and re-entrancy.

  AmazonSoftware EngineerCoding & AlgorithmsTechnical Screen
• Implement streaming k-way merge with constraintsMedium

  This question evaluates competence in streaming algorithms and resource-constrained algorithm design, specifically implementing a k-way merge over blocking, potentially unbounded integer iterators while preserving stable duplicates and reasoning about time and O(k) space complexity.

  AmazonData ScientistCoding & AlgorithmsOnsite
• Implement tree left/right views via BFS and DFSMedium

  This question evaluates understanding of binary tree traversal techniques and algorithmic reasoning for producing per-depth left and right views, assessing competencies in data structures, traversal ordering, and correctness across edge cases.

  AmazonSoftware EngineerCoding & AlgorithmsOnsite
• Implement two array-cost minimization algorithmsMedium

  This question evaluates algorithm design, data structure selection, and complexity analysis by requiring implementations for minimizing total merge cost and for removing a contiguous block to minimize the sum of adjacent absolute differences.

  AmazonSoftware EngineerCoding & AlgorithmsOnsite
• Improve an Inefficient Team ProcessMedium

  This question assesses the candidate's ability to identify systemic bottlenecks, influence stakeholders through data, and implement sustainable process improvements while maintaining system stability.

  AmazonSoftware EngineerBehavioral & LeadershipTechnical Screen
• Influence a Colleague Who Prefers a Different ApproachMedium

  Describe influencing a colleague through shared goals, fair comparison, evidence, willingness to revise, and a concrete collaborative decision.

  AmazonSoftware EngineerBehavioral & LeadershipOnsite
• Innovation, Root Cause, and Deadline Management StoriesMedium

  Practice behavioral PM answers for innovation, root-cause analysis, and urgent deadline management. The guide uses STARL examples with metrics, trade-offs, stakeholder handling, prevention mechanisms, and probe-ready details for interview follow-up questions.

  AmazonProduct ManagerBehavioral & LeadershipOnsite
• Intervene When Your Team Is Heading in the Wrong DirectionMedium

  This question assesses a candidate's ability to identify technical or strategic misalignments within a project and their skill in providing constructive, data-driven feedback while maintaining team cohesion.

  AmazonFrontend EngineerBehavioral & LeadershipOnsite
• Inventory Allocation by Bid PriorityMedium

  Implement a tiered inventory allocation system that distributes limited stock among buyer bids based on price tiers and round-robin priority rules.

  AmazonSoftware EngineerCoding & AlgorithmsOnline Assessment
• Investigate a Difficult Problem Until You Found the Root CauseMedium

  This question assesses the candidate's ability to systematically troubleshoot complex technical issues, demonstrate technical ownership, and implement long-term solutions rather than quick fixes.

  AmazonFrontend EngineerBehavioral & LeadershipOnsite
• Jukebox That Plays the Most-Requested Song FirstMedium

  Design a request queue where each play takes the song with the most pending requests, with ties going to the most recent request. Tests keeping frequency and recency together so both operations run in O(1).

  AmazonSoftware EngineerCoding & AlgorithmsOnsite
• Justify choosing JavaScript for interviewsMedium

  This question evaluates a candidate's ability to articulate programming language trade-offs, understand language-specific runtime behaviors (types, hoisting, closures), and justify tool choice for front-end coding tasks within the Coding & Algorithms domain.

  AmazonSoftware EngineerCoding & AlgorithmsTechnical Screen
• Kth Largest Perfect Binary SubtreeMedium

  This problem tests tree traversal techniques, the ability to validate binary tree properties recursively, and the use of selection algorithms to find order statistics.

  AmazonSoftware EngineerCoding & AlgorithmsOnsite
• LLM Fundamentals: Tokenization Design and KL-Regularized SFTMedium

  This problem tests your understanding of subword tokenization strategies and the theoretical motivation behind KL-divergence constraints during supervised fine-tuning.

  AmazonApplied ScientistMachine LearningOnsite
• Launching Alexa in a New-Language MarketHard

  Practice launching Alexa in a new language market with product vision, personas, MVP use cases, ASR and NLU quality metrics, data scarcity solutions, localization, privacy regulations, partnerships, GTM, risk mitigation, and launch gates.

  AmazonProduct ManagerProduct / Decision MakingOnsite