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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
- Questions
- 546
- Companies
- 11
- Topics
- 8
- Languages
Showing 151–180 of 4616 results
- AmazonMediumData ScientistCalculate Weekly, Monthly Hours Watched by Premium Users
This question evaluates a candidate's ability to perform time-based aggregation and data manipulation in SQL and Python, including grouping, filtering by subscription status, and converting minute-level metrics into hours.
Data Manipulation (SQL/Python)Technical Screen - AmazonEasySoftware EngineerCheck a Palindrome While Ignoring Case and Punctuation
Tests string manipulation and two-pointer traversal techniques to verify if a string is a palindrome while ignoring non-alphanumeric characters and case.
Coding & AlgorithmsTechnical ScreenStringsTwo PointersEasy - AmazonMediumApplied ScientistCheck if adding edge creates cycle in digraph
This question evaluates understanding of graph algorithms, reachability, and algorithmic complexity by determining whether adding a directed edge would introduce a cycle in an otherwise acyclic directed graph.
Coding & AlgorithmsTechnical Screen - AmazonHardData ScientistChoose Between Fine-Tuning and RAG for Client Chatbot
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.
Machine LearningTechnical Screen - AmazonHardData ScientistChoose Models for Imbalanced Data and Time-Series Forecasting
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.
Machine LearningTechnical Screen - AmazonMediumData ScientistChoose regularization norms and model formulations
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.
Machine LearningTechnical Screen - AmazonEasySoftware EngineerClarify internship availability and conversion
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.
Behavioral & LeadershipTechnical Screen - AmazonMediumData ScientistCompare RNNs and Transformers for Long-Sequence Text Classification
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.
Machine LearningTechnical Screen - AmazonHardData ScientistCompare Random Forests vs Gradient Boosting rigorously
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.
Machine LearningTechnical Screen - AmazonMediumData ScientistCompare Regularization Techniques and Their Use Cases
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.
Machine LearningTechnical Screen - AmazonMediumSoftware EngineerCompute Edit Distance
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.
Coding & AlgorithmsTechnical ScreenDynamic ProgrammingStringsAlgorithms - AmazonMediumSoftware EngineerCompute Latencies and Search Grid Path
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.
Coding & AlgorithmsTechnical Screen - AmazonMediumData ScientistCompute a Rolling Seven-Day Revenue Sum with Missing Dates
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.
Data Manipulation (SQL/Python)Technical Screensqlpostgresqlwindow-functions - AmazonMediumData ScientistCompute an A/B test p-value by hand
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.
Statistics & MathTechnical Screen - AmazonMediumData ScientistCompute and interpret quantile loss vs RMSE
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.
Statistics & MathTechnical ScreenStatisticsForecastingLoss Functions - AmazonMediumData ScientistCompute array modes with ties and no-mode rule
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.
Coding & AlgorithmsTechnical Screen - AmazonMediumData ScientistCompute p-values, CIs, and adjust multiples
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.
Statistics & MathTechnical Screen - AmazonMediumSoftware EngineerCompute peak parking lot occupancy intervals
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.
Coding & AlgorithmsTechnical Screen - AmazonMediumSoftware EngineerCompute shortest path with obstacles
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 shortest path with obstacles states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Coding & AlgorithmsTechnical Screen - AmazonEasySoftware EngineerCompute the Minimum Number of Meeting Rooms
This problem tests your ability to manage overlapping time intervals efficiently, requiring an understanding of sorting or priority queues to track resource utilization.
Coding & AlgorithmsTechnical ScreenSortingIntervalsGreedy - AmazonMediumSoftware EngineerCompute total revenue from greedy VM rentals
This problem tests your ability to use greedy strategies combined with efficient data structures to manage resource allocation and optimize revenue based on priority-based demand.
Coding & AlgorithmsTechnical ScreenGreedyData StructuresHeap - AmazonMediumSoftware EngineerCompute tree diameter
This question evaluates understanding of binary tree algorithms, recursion versus iterative traversal strategies, and competence in algorithmic analysis including time and space complexity and handling deep-recursion stack overflow when computing a tree's diameter.
Coding & AlgorithmsTechnical Screen - AmazonMediumSoftware EngineerCompute unique visitors per department from clicks
This question evaluates a candidate's skill in data manipulation and deduplication using SQL/Python, specifically mapping hierarchical product metadata to departments and computing unique user counts without double-counting, and is categorized under Data Manipulation (SQL/Python).
Data Manipulation (SQL/Python)Technical Screen - AmazonMediumData ScientistConsolidate and Rank Global Salaries in USD
This question evaluates data consolidation, currency normalization, and ranking competencies, measuring the ability to combine country-specific employee data and convert salaries into a common USD basis within the Data Manipulation (SQL/Python) domain.
Data Manipulation (SQL/Python)Technical Screen - AmazonMediumSoftware EngineerControl Robot Charging with Repeated Polling and Exclusive Station Assignments
Design a repeatedly invoked robot-charging controller with strict battery thresholds, exclusive station assignments, waiting robots, and safe command retries.
Software Engineering FundamentalsTechnical Screen - AmazonMediumSoftware EngineerCount Connected Components in an Undirected Graph
This question tests the ability to traverse an undirected graph using graph search algorithms to identify and count disjoint subgraphs.
Coding & AlgorithmsTechnical ScreenGraphsBFSDFS - AmazonMediumSoftware EngineerCount Customers Who Visited on Multiple Dates
Tests data manipulation, hash set/map aggregation, and date deduplication to identify users with repeat visits across distinct calendar days.
Coding & AlgorithmsTechnical ScreenHash TableCountingString - AmazonMediumSoftware EngineerCount regions with DFS
This question evaluates proficiency in graph traversal and grid-based connected-component detection using depth-first search on a binary m x n grid, along with algorithmic complexity analysis and techniques to avoid revisiting cells.
Coding & AlgorithmsTechnical Screen - AmazonHardSoftware EngineerCount unique Morse-code word transformations
This question evaluates a candidate's ability to implement string transformations based on a fixed character-to-code mapping and to identify distinct results, testing competencies in encoding, string manipulation, and deduplication.
Coding & AlgorithmsTechnical Screen - AmazonMediumSoftware EngineerCourse Prerequisite Feasibility
This question tests the ability to detect cycles in a directed graph to determine if a set of academic courses with prerequisite constraints can be completed.
Coding & AlgorithmsTechnical ScreenGraphDFSTopological Sort
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