practice a little every day, paws crossed ♡
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 1–30 of 3640 results
- 1PasswordEasySoftware EngineerExplain Role Fit, Conflict Resolution, and Learning
Prepare concise interview answers about role fit, conflict resolution, and learning a new technology or domain. Each response should use verified facts and real experience while demonstrating judgment, collaboration, learning agility, and defensible outcomes.
Behavioral & LeadershipOnsite - AMDMediumSoftware EngineerC++ Code Reading: Bug in a Linked-List insertAfter and Predicting Polymorphic Output
Two C++ code-reading exercises: find the bug in a singly linked list insertAfter function that is missing a pointer update and predict what the program prints, then predict and explain the output of code mixing virtual overriding, name hiding, constructors, default arguments and pass-by-value. It tests precise tracing of pointers and dispatch rules.
Software Engineering FundamentalsOnsite - AMDMediumSoftware EngineerCompare Single-Agent and Multi-Agent Designs
Compare single-agent and multi-agent systems through task independence, specialization, review isolation, coordination cost, shared-state control, and evidence-based integration.
ML System DesignOnsite - AMDMediumSoftware EngineerDesign and Implement a Fixed-Capacity LRU Cache with Constant-Time Operations
Design and implement a fixed-capacity least-recently-used cache whose get and put operations run in constant time, while answering follow-up probes during coding. It tests data structure composition, careful iterator handling, eviction edge cases, and extensions such as thread safety and expiry.
Software Engineering FundamentalsOnsite - AMDMediumSoftware EngineerDevOps Experience, Jenkins Pipelines and Plugins, and Reading Very Large Log Files
Practical DevOps questions: describe your DevOps experience, explain how Jenkins pipelines, agents and plugins are used to build and test software, and show how to fetch and search a log file too large to open or load into memory. It tests hands-on CI/CD knowledge and efficient handling of large files.
Software Engineering FundamentalsOnsite - AMDMediumSoftware EngineerEvaluate Agents That Generate or Optimize Kernels and Compiler Code
Evaluate kernel and compiler agents with bounded transformation spaces, isolated execution, differential correctness checks, numerical tolerances, representative benchmarks, and reviewable evidence.
ML System DesignOnsite - AMDMediumSoftware EngineerExplain Cache Conflicts and Related Memory-Access Problems
Explain cache-set conflicts with address mapping, then distinguish eviction from false sharing and GPU bank conflicts so padding and layout changes target the right cause.
Software Engineering FundamentalsOnsite - AMDMediumSoftware EngineerExplain GPU Register Spills and Their Performance Cost
Explain GPU register spills through live-variable pressure, compiler allocation, spill traffic, occupancy tradeoffs, and measured optimization choices.
Software Engineering FundamentalsOnsite - AMDMediumSoftware EngineerExplain How You Learn New Technical Topics
Explain a practical technical-learning process through a defined knowledge gap, authoritative sources, small experiments, feedback, and verification before real use.
Behavioral & LeadershipOnsite - AMDMediumSoftware EngineerExplain Skills and Progressive Disclosure in an Agent
Explain reusable agent skills, prompt context, progressive disclosure, selection metadata, referenced resources, and the boundaries of permissions and validation.
Software Engineering FundamentalsOnsite - AMDMediumSoftware EngineerExplain the ReAct Agent Loop
Explain a ReAct tool loop through state, action selection, observations, recovery, stopping criteria, and validation of task completion.
Machine LearningOnsite - AMDMediumSoftware EngineerFast Retrieval at Scale: Databases, Indexing vs Sharding, Red-Black Trees and Tries
Conceptual questions on data storage and retrieval: what a database is and how to find data quickly in a very large dataset, how sharding differs from indexing, and how red-black trees and tries work. It tests understanding of access paths, horizontal scaling and core search data structures.
Software Engineering FundamentalsOnsite - AMDMediumSoftware EngineerFind the Largest K Elements with Partitioning
Find the largest k occurrences with partition-based selection, sort only the output, and compare quickselect pivot risks with heap and full-sort alternatives.
Coding & AlgorithmsOnsite - AMDMediumSoftware EngineerHow and How Often You Use AI Tools in Day-to-Day Engineering Work
A behavioral question asking where you use AI tools in your engineering work, how often, and how much you rely on them. It tests judgment about when AI helps or hurts, how you verify generated output, and awareness of confidentiality and code quality risks.
Behavioral & LeadershipOnsite - AMDMediumSoftware EngineerOptimize the Memory Accesses of a GPU Matrix Transpose
Analyze row-major GPU transpose accesses and use tiled shared staging for coalesced reads and writes, with barrier safety, edge handling, and device-specific bank checks.
Software Engineering FundamentalsOnsite - AMDMediumSoftware EngineerProfile a GPU Workload and Choose a Parallelism Strategy
Profile a GPU workload from its critical path to kernel counters, then choose parallelism based on independence, memory capacity, communication, and measured end-to-end gains.
Software Engineering FundamentalsOnsite - AMDMediumSoftware EngineerTrace an LLM Request Through a Paged-KV Inference Engine
Trace LLM inference from request admission through prefill and decoding, explaining paged KV block tables, continuous batching, cache lifetime, and latency tradeoffs.
ML System DesignOnsite - AMDMediumSoftware EngineerUse the Roofline Model to Analyze Prefill and Decoding
Apply roofline analysis to LLM prefill and decoding using arithmetic intensity, sustainable bandwidth, batching, KV-cache traffic, and measured kernel behavior.
Software Engineering FundamentalsOnsite - AdobeMediumSoftware EngineerCollaborate Effectively with a Product Manager
Give a concrete example of partnering with a product manager on a difficult decision involving scope, timing, quality, or measurement. Explain shared ownership, ambiguity, disagreement, alignment between product and engineering, and the measured result.
Behavioral & LeadershipOnsite - AdobeEasyData ScientistDescribe Your Background and Alignment with This Role
This interview question evaluates behavioral evidence, ownership, communication, trade-offs, and measurable outcomes in a realistic interview setting. A strong answer for Describe Your Background and Alignment with This Role states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Behavioral & LeadershipOnsite - AdobeMediumSoftware EngineerDescribe Your Two-to-Three-Year Growth Direction
Explain how you want your capabilities, scope, and problem ownership to grow over the next two to three years. Ground the direction in prior evidence, connect it to the role, and show how you plan to learn while remaining adaptable.
Behavioral & LeadershipOnsite - AdobeMediumData ScientistDesign Metrics Framework for Adobe Express Performance Evaluation
This interview question evaluates metric design, causal reasoning, experiment setup, diagnostics, SQL/statistical checks, and recommendations in a realistic interview setting. A strong answer for Design Metrics Framework for Adobe Express Performance Evaluation states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Analytics & ExperimentationOnsite - AdobeMediumSoftware EngineerDesign a Runtime Localization System
Design a runtime localization system for dozens of countries with locale and regional resolution, variables, plural rules, safe fallback, and offline-capable bundles. Address live copy updates, approvals, rollbacks, formatting, missing translations, cache behavior, and longer-text layout failures.
System DesignOnsite - AdobeHardMachine Learning EngineerDesign a natural-language AEP Q&A assistant
This interview question evaluates ML product requirements, data/labeling, modeling, serving architecture, evaluation, monitoring, and trade-offs in a realistic interview setting. A strong answer for Design a natural-language AEP Q&A assistant states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
ML System DesignOnsite - AdobeMediumMachine Learning EngineerDetermine if a multiple-sum subarray exists
This interview question evaluates algorithm design, data structures, correctness, complexity, edge cases, and implementation details in a realistic interview setting. A strong answer for Determine if a multiple-sum subarray exists states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Coding & AlgorithmsOnsite - AdobeMediumMachine Learning EngineerDiscuss resume and behavioral scenarios
This interview question evaluates behavioral evidence, ownership, communication, trade-offs, and measurable outcomes in a realistic interview setting. A strong answer for Discuss resume and behavioral scenarios states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Behavioral & LeadershipOnsite - AdobeHardMachine Learning EngineerExplain Transformer Attention Fundamentals
This question tests the candidate's deep conceptual knowledge of the Transformer architecture, focusing on the mathematical mechanics of scaled dot-product attention, memory optimization, and hardware-aware training techniques.
Machine LearningOnsiteTransformersAttentionDeep Learning - AdobeMediumSoftware EngineerFind the Lowest Common Ancestor Only When Both Nodes Exist
Return the lowest common ancestor of two target values only when both are present in a potentially skewed binary tree. Handle absent targets, equal targets, empty input, and cases where one target is the ancestor.
Coding & AlgorithmsOnsite - AdobeMediumSoftware EngineerLearn from a Major Work Setback
Describe a consequential professional setback that left you frustrated or disappointed. Focus on what was under your control, your response, the perspectives of others, and a lasting change in process or judgment.
Behavioral & LeadershipOnsite - AdobeHardMachine Learning EngineerOptimize LLM Training and Serving
This question tests the ability to diagnose hardware utilization bottlenecks in LLM workloads and design architectural optimizations to improve throughput and latency under constrained GPU memory resources.
ML System DesignOnsiteLLMGPU-ArchitecturePerformance-Engineering