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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 511–540 of 4616 results
- AnthropicMediumMachine Learning EngineerOptimize MapReduce performance
This question evaluates understanding of the MapReduce programming model and distributed batch processing at scale. It tests practical knowledge of parallel efficiency, network bottleneck analysis, and optimization techniques like combiners, partitioning, and speculative execution — core competencies assessed in machine learning and data engineering roles.
System DesignTechnical Screen - AnthropicMediumSoftware EngineerReview and Scale an Inference Service Design
Tests the ability to architect a high-throughput ML inference service under strict latency SLAs, including dynamic batching, hardware capacity estimation, admission control, and multi-model routing.
ML System DesignTechnical Screenml-system-designdistributed-systemsgpu-inference - AnthropicHardSoftware EngineerRoute and Batch Inference with Eight GPUs
Design an inference routing, dynamic batching, and scheduling system across a fixed pool of eight 80GB GPUs serving both large and small language models under strict latency and memory constraints.
ML System DesignTechnical Screenml-systemsllm-inferencegpu-scheduling - AnthropicHardSoftware EngineerScale crawler with thread pool
This question evaluates system design and concurrent programming competencies, focusing on bounded thread-pool architecture, thread-safe frontier and deduplication structures, per-host politeness and in-flight limits, reliability mechanisms (timeouts, retries, backoff), and graceful shutdown and termination semantics.
System DesignTechnical Screen - AnthropicHardMachine Learning EngineerSchedule Large and Small Model Inference on an Eight-GPU Pool
Design an inference scheduler for a shared 8-GPU node that co-locates single-GPU small models and all-reduce 8-GPU large models. The design must address gang scheduling, prefix cache reuse, deadline-aware priorities, and overload mitigation.
ML System DesignTechnical Screenml-system-designgpu-schedulingdistributed-inference - AnthropicMediumSoftware EngineerSolve programming task with follow-ups
This question evaluates algorithmic problem-solving skills, including core algorithm implementation, handling a consecutive-N-element constraint, and designing an optimization that leverages suffix-related information.
Coding & AlgorithmsTechnical Screen - AnthropicMediumSoftware EngineerWhy Anthropic and its values?
This question evaluates a candidate's culture fit, value alignment, integrity under pressure, and critical thinking within the Behavioral & Leadership category for a software engineering role.
Behavioral & LeadershipTechnical Screen - AntraMediumSoftware EngineerCharacter Frequency Count in a String
This problem tests your ability to manipulate strings and utilize hash maps to count character frequencies while maintaining the original order of character appearance.
Coding & AlgorithmsTechnical ScreenStringsHash MapAlgorithms - AntraEasySoftware EngineerExplain core Java and Spring fundamentals
This question evaluates core competencies in Java and Spring, covering data structures and collections, object-oriented design (SOLID), hash-based collections and concurrency primitives, immutability and string handling, exception handling, Spring IoC/DI and AOP, bean lifecycle and annotations, and functional collection processing via the Stream API. Commonly asked in Software Engineering Fundamentals interviews for backend Java/Spring roles, it checks both conceptual understanding and practical application by assessing architectural reasoning, concurrency and thread-safety knowledge, and familiarity with framework conventions and APIs.
Software Engineering FundamentalsTechnical Screen - AntraMediumSoftware EngineerJava and Object-Oriented Design Fundamentals
This problem tests your understanding of interface design, the Liskov Substitution Principle, and proper usage of Java Collections in a Spring-managed context.
Software Engineering FundamentalsTechnical ScreenJavaSpringSOLID - AntraMediumSoftware EngineerLongest Substring Without Repeating Characters
This problem tests your ability to implement an efficient sliding window technique using a hash map to track character indices and maintain a valid substring constraint.
Coding & AlgorithmsTechnical ScreenSliding WindowHash MapString - AppleMediumSoftware EngineerAnswer OoO, caches, and memory-system questions
This question evaluates core computer-architecture and system-level skills—covering out-of-order execution structures, branch prediction (gshare) mechanics, register renaming/RAT tracing, VLIW versus superscalar tradeoffs, cache coherence protocols, load/store subsystem behavior, copy-on-write after fork, cache indexing math, VIPT aliasing, and asynchronous FIFO design—and is categorized under Software Engineering Fundamentals. It is commonly asked because it probes a candidate’s microarchitectural and OS understanding, performance reasoning, and ability to explain low-level (microarchitecture and system-level) concepts and interactions without requiring full ISA-specific implementations.
Software Engineering FundamentalsTechnical Screen - AppleMediumFrontend EngineerBuild an Accessible Recursive Expand-and-Collapse Tree in React
This problem tests recursive component design in React, state management for nested data structures, and the implementation of WAI-ARIA standards for accessible UI components.
Software Engineering FundamentalsTechnical ScreenReactFrontendAccessibility - AppleEasySoftware EngineerCafe Open-Now Check: Split Shifts and Midnight-Crossing Hours
Build an 'is the cafe open right now' check across three stages of growing complexity -- one fixed daily window, then select weekdays, then multiple shifts per day including windows that cross midnight. Tests clean incremental time modeling more than any single clever algorithm.
Software Engineering FundamentalsTechnical Screentimemodelingintervals - AppleMediumSoftware EngineerCapture Enclosed O Regions in an X/O Grid Using Union-Find Instead of Flood Fill
Given a grid of X and O cells, capture every region of edge-connected O cells that does not touch the border by turning it into X, using a disjoint-set (union-find) structure instead of flood fill. Tests union-find implementation, mapping grid cells to set indices and correct border handling.
Coding & AlgorithmsTechnical Screen - AppleMediumMachine Learning EngineerChoose File Storage Tiers Using Access Predictions
Design an end-to-end machine learning and decision system that predicts file access likelihood to automatically transition cloud-stored user files across storage tiers to minimize operational cost while meeting retrieval latency SLAs.
ML System DesignTechnical Screenml-system-designprobability-calibrationcloud-storage - AppleMediumSoftware EngineerCloud Provider Design for Provisioning Resources Through Terraform
A cloud infrastructure design question asking what a cloud provider must build so customers can create, update and destroy its resources with Terraform. It tests mapping the Terraform resource lifecycle to provider APIs, handling long-running operations and their status, idempotent retries, and drift detection.
System DesignTechnical Screen - AppleMediumMachine Learning EngineerCompare DCN v1 vs v2 and A/B test
This question tests your understanding of architectural improvements in Deep & Cross Networks (DCN) and your ability to design a rigorous A/B testing strategy for model deployment in a high-stakes production environment.
Machine LearningTechnical ScreenDeep LearningCTR PredictionRecommendation Systems - AppleMediumData ScientistCompare Normal and Poisson Distributions in Statistics
Evaluates statistical understanding of Normal and Poisson distributions for counts and continuous measurements. Strong answers compare support, parameters, use cases, Normal approximation conditions, and mean-variance derivations.
Statistics & MathTechnical Screen - AppleMediumData ScientistCompare Normal vs Poisson; test dispersion and approximate tails
This question evaluates a data scientist's competency in statistical inference for count data, covering MLE and asymptotic variance estimation, Wald confidence intervals, tests for equidispersion versus overdispersion, Normal approximation with continuity correction for Poisson tails, and model selection diagnostics.
Statistics & MathTechnical Screen - AppleMediumMachine Learning EngineerCompare audio preprocessing and training
This question evaluates understanding of audio data preprocessing and end-to-end model training, assessing competencies in feature representation choices, normalization, segmentation, augmentation, and pipeline orchestration within machine learning for audio.
Machine LearningTechnical Screen - AppleHardSoftware EngineerCompute conflicts and minimum meeting rooms
This question evaluates the ability to reason about interval overlaps and resource allocation, testing algorithmic thinking and efficient handling of time-interval data in scheduling contexts and belongs to the Coding & Algorithms domain.
Coding & AlgorithmsTechnical Screen - AppleMediumSoftware EngineerCompute island size in grid
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 island size in grid states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Coding & AlgorithmsTechnical Screen - AppleMediumSoftware EngineerCompute minimum required meeting rooms
This question evaluates understanding of interval scheduling and resource-allocation concepts, proficiency with data structures for detecting temporal overlaps (heap and sweep-line paradigms), and the ability to analyze time and space complexity while handling edge cases.
Coding & AlgorithmsTechnical Screen - AppleMediumSoftware EngineerCompute top three scores per student
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 top three scores per student states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Coding & AlgorithmsTechnical Screen - AppleMediumData ScientistConstruct a Churn-Prediction Pipeline Using Scikit-Learn
This question tests practical machine learning engineering skills, specifically the ability to construct an end-to-end classification pipeline for imbalanced tabular data. It evaluates knowledge of preprocessing, model validation, probability calibration, and production packaging — core competencies assessed in data scientist interviews.
Machine LearningTechnical Screen - AppleMediumSoftware EngineerCount Islands in a Binary Grid
This problem tests your ability to perform graph traversal on a 2D grid while adhering to strict memory constraints and preserving the integrity of the original data structure.
Coding & AlgorithmsTechnical ScreenGraphsBFSIterative Traversal - AppleHardSoftware EngineerCount subarrays with sum equals k
This problem tests your ability to optimize subarray sum calculations using prefix sums and hash maps to achieve linear time complexity.
Coding & AlgorithmsTechnical ScreenArraysHash MapPrefix Sum - AppleMediumSoftware EngineerCount the Nodes in a Chain Reachable Only Through Left and Right Links
Starting from one node of a chain in which every node links only to its left and right neighbors, count the nodes in the whole chain. It tests traversal in both directions from an arbitrary node, handling of chain ends and single-node chains, and the design of your own test cases.
Coding & AlgorithmsTechnical Screen - AppleMediumMachine Learning EngineerDebug a PyTorch U-Net shape mismatch
This question tests your ability to calculate the output dimensions of convolutional layers in a U-Net architecture and resolve shape mismatches caused by cropping or padding requirements during feature map concatenation.
Coding & AlgorithmsTechnical ScreenPyTorchComputer VisionU-Net
Anthropic
Apple