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Interview Questions
Practice 9403 coding, system design and machine learning questions from 546 companies, sorted by role, topic and interview round. Every question has step-by-step hints, a code runner that supports 8 languages and an AI mentor; members also get the full solution walkthrough.
- 9403
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- 546
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- 11
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Showing 331–360 of 4616 results
- AmazonMediumSoftware EngineerFrequency Stack with Recency Tie-Breaking
Design a frequency stack data structure that supports pushing elements and popping the most frequent element, breaking ties by choosing the most recently pushed element, with both operations running in O(1) time.
Coding & AlgorithmsTechnical Screenstackhash-tabledesign - AmazonHardApplied ScientistGRPO Deep Dive: Critic-Free RL, Parallelism, MLA, and Reward Design for a Reasoning LLM
This question tests your deep understanding of Group Relative Policy Optimization (GRPO) for post-training reasoning models, including how critic-free optimization alters training dynamics, distributed memory scaling, KV-cache efficiency via Multi-Head Latent Attention (MLA), and reward hacking mitigations.
Machine LearningTechnical ScreenReinforcement LearningLLM Post-TrainingGRPO - AmazonMediumData ScientistGenerate Synthetic Clickstream Data with Python Function
This question evaluates a candidate's ability to implement synthetic data generation and probabilistic event simulation in Python using libraries like pandas and numpy, emphasizing timestamp handling, random sampling, and feature engineering for clickstream records.
Coding & AlgorithmsTechnical Screen - AmazonMediumData ScientistGenerate primes up to n efficiently
This question evaluates algorithm design and complexity analysis for prime generation, focusing on sieving techniques and time-space trade-offs when producing primes up to large n.
Coding & AlgorithmsTechnical Screen - AmazonMediumSoftware EngineerHandle Workplace Conflict and Criticism
This question tests your ability to navigate professional disagreements and integrate constructive feedback while maintaining focus on project outcomes.
Behavioral & LeadershipTechnical ScreenBehavioralCommunicationLeadership - AmazonMediumSoftware EngineerHandle deadlines and misses
Handle deadlines and misses evaluates behavioral evidence, ownership, communication, trade-offs, and measurable outcomes in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Behavioral & LeadershipTechnical Screen - AmazonMediumApplied ScientistHighest Average-Salary Team in an Org Chart
Tests bottom-up tree traversal to compute subtree aggregates (sum and count) and identify an optimal node using multi-level tie-breaking rules.
Coding & AlgorithmsTechnical Screentreedfsrecursion - AmazonEasyData ScientistHow to evaluate adding video ads in a game
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.
Analytics & ExperimentationTechnical ScreenA/B TestingProduct AnalyticsMonetization - AmazonMediumData ScientistHow would you evaluate adding video ads?
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.
Analytics & ExperimentationTechnical Screen - AmazonMediumData ScientistHow would you test a price increase?
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.
Analytics & ExperimentationTechnical Screen - AmazonEasyData ScientistImplement Batch Gradient Descent for Linear Regression
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.
Machine LearningTechnical Screen - AmazonMediumSoftware EngineerImplement Cache and Count Components
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.
Coding & AlgorithmsTechnical Screen - AmazonHardSoftware EngineerImplement Datacenter Router Commands
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.
Coding & AlgorithmsTechnical ScreenData StructuresGeometryAlgorithms - AmazonMediumSoftware EngineerImplement Event Filtering and Queue Routing
Implement an in-memory event routing engine that matches nested JSON events against subscriber filter policies and routes them in subscription order.
Coding & AlgorithmsTechnical ScreenData StructuresJSON ParsingDesign - AmazonMediumData ScientistImplement Function to Determine Mode and Prime Numbers
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.
Coding & AlgorithmsTechnical Screen - AmazonEasySoftware EngineerImplement Interview Coding Problems
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.
Coding & AlgorithmsTechnical Screen - AmazonHardApplied ScientistImplement Optimal Bucket Batching
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.
Coding & AlgorithmsTechnical Screen - AmazonEasySoftware EngineerImplement Regular Expression Matching
This problem tests your ability to handle recursive string matching problems using dynamic programming, specifically focusing on handling wildcard and repetition operators.
Coding & AlgorithmsTechnical ScreenDynamic ProgrammingStringsRecursion - AmazonMediumApplied ScientistImplement SGD for linear regression and derive gradients
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.
Machine LearningTechnical Screen - AmazonHardSoftware EngineerImplement a Half-Open Interval Range Module
This problem tests proficiency with balanced binary search trees or interval management techniques to efficiently track and manipulate sets of half-open intervals.
Coding & AlgorithmsTechnical ScreenIntervalsBinary SearchData Structures - AmazonMediumSoftware EngineerImplement a formatted shell script output
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.
Coding & AlgorithmsTechnical Screen - AmazonMediumData ScientistImplement a high-throughput web crawler safely
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.
Coding & AlgorithmsTechnical Screen - AmazonMediumSoftware EngineerImplement a robust shell CLI
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.
Coding & AlgorithmsTechnical Screen - AmazonMediumSoftware EngineerImplement ad matching and delivery routing
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.
Coding & AlgorithmsTechnical Screen - AmazonMediumFrontend EngineerImplement an Accessible Autocomplete Search Component
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.
Software Engineering FundamentalsTechnical ScreenReactAccessibilityFrontend - AmazonMediumSoftware EngineerImplement and Analyze an LRU Query Cache
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.
Coding & AlgorithmsTechnical Screen - AmazonMediumApplied ScientistImplement binary search lower/upper bounds
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.
Coding & AlgorithmsTechnical Screen - AmazonMediumSoftware EngineerImplement robust debounce and throttle in JavaScript
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.
Coding & AlgorithmsTechnical Screen - AmazonMediumSoftware EngineerImprove an Inefficient Team Process
This question assesses the candidate's ability to identify systemic bottlenecks, influence stakeholders through data, and implement sustainable process improvements while maintaining system stability.
Behavioral & LeadershipTechnical ScreenBehavioralLeadershipProcess Improvement - AmazonMediumSoftware EngineerJustify choosing JavaScript for interviews
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.
Coding & AlgorithmsTechnical Screen
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