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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.
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Showing 691–720 of 9403 results
- AmazonMediumApplied ScientistFrom Early NLP Embeddings to Modern LLM Embeddings
Evaluate the architectural and practical evolution of NLP text embeddings, and formulate a decision framework comparing small bi-encoders versus large LLM-based embedding models based on retrieval quality, latency, and operational cost.
Machine LearningOnsiteEmbeddingsNLPInformation Retrieval - 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 - AmazonMediumMachine Learning EngineerGRPO in NumPy: Group-Relative Advantages, Process Rewards and Shaped Rewards
Implement the core of Group Relative Policy Optimization in a NumPy notebook: group-normalized advantages, the clipped objective with a KL penalty, per-token credit from process rewards, and shaped rewards. Tests fluency with array axes and broadcasting and an understanding of how reward normalization interacts with shaping terms.
Machine LearningOnsite - AmazonMediumSoftware EngineerGenAI Literacy Questions: Daily AI Use, Handling Bad AI Output, and RAG Projects
Behavioral questions on generative AI literacy reported from hiring manager and Bar Raiser rounds: how you use AI tools day to day, a time you caught bad AI output, and a walkthrough of a retrieval-augmented generation project. It tests verification habits, judgment about data boundaries and RAG fundamentals.
Behavioral & LeadershipOnsite - AmazonMediumSoftware EngineerGeneralize Dictionary Merging
Explain an approach to merging two dictionaries and generalizing it efficiently to k dictionaries, explicitly identifying the collision semantics that must be clarified.
Software Engineering FundamentalsOnsite - 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 EngineerGoing Above and Beyond for a Customer and Refusing to Lower the Quality Bar
Two customer-focused behavioral prompts: describe going well beyond what was required to solve a customer's problem, and refusing to compromise on quality or service under pressure to cut corners. It assesses customer focus, judgment about trade-offs and raising standards for the whole team.
Behavioral & LeadershipOther - AmazonMediumSoftware EngineerGroup Anagrams Without Sorted Character Signatures
Group anagrams without sorting each word's characters, preserving repeated words and reasoning about frequency signatures and overflow-prone encodings.
Coding & AlgorithmsOnline Assessment - 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 - AmazonMediumSoftware EngineerHigh Availability and Failover for a Primary-Replica Database
This problem tests your ability to design a resilient database architecture that minimizes downtime and data loss during node failures.
System DesignOnsiteDatabaseHigh AvailabilityReplication - 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 - AmazonMediumSoftware EngineerHighest-total path through a binary tree encoded in level order
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.
Coding & AlgorithmsOther - AmazonMediumProduct ManagerHotel Keycard System Design
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.
Product / Decision MakingOnsite - 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 - AmazonMediumData ScientistIdentify 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 - AmazonMediumBusiness Intelligence EngineerIdentify 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 - AmazonMediumData ScientistIdentify P-Value Limitations and Complementary Approaches
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.
Statistics & MathOnsite - AmazonMediumBusiness Intelligence EngineerIdentify 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 - AmazonMediumBusiness Intelligence EngineerIdentify 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 - AmazonMediumBusiness Intelligence EngineerIllustrate 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 - 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 - AmazonMediumSoftware EngineerImplement Cache and Rotate Matrix
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.
Coding & AlgorithmsOnsite - 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
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