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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 2191–2220 of 4616 results
- LinkedInHardSoftware EngineerExplain a Technical Project with an Architecture Diagram
This question evaluates your ability to clearly explain a complex software architecture, articulate your personal technical ownership, justify design trade-offs, and quantify business and operational impact.
Behavioral & LeadershipTechnical Screenbehavioralsystem architecturetechnical leadership - LinkedInMediumSoftware EngineerExplain a past project and critique a prior team
This question evaluates communication of technical ownership, system architecture reasoning, and leadership competencies such as collaboration, conflict resolution, and influencing change within a Software Engineer role.
Behavioral & LeadershipTechnical Screen - LinkedInMediumSoftware EngineerExplain database transactions and ACID
This question evaluates a candidate's understanding of database transactions including ACID properties, isolation levels (read uncommitted, read committed, repeatable read, serializable), common anomalies (dirty reads, non-repeatable reads, phantom reads), MVCC, and commit/rollback semantics, testing competency in transactional correctness and concurrency control. Commonly asked in System Design and database contexts because it probes reasoning about data integrity, visibility, and durability under concurrent workloads, and it tests both conceptual understanding of transactional guarantees and practical application of isolation behavior in relational database systems.
System DesignTechnical Screen - LinkedInMediumMachine Learning EngineerExplain gradient descent, learning rate, backpropagation, overfitting and batch size
A machine learning fundamentals screen on how neural networks are trained: how gradient descent updates parameters, what the learning rate and batch size change, how backpropagation computes gradients, and how to diagnose and fix underfitting and overfitting. Tests mechanics and practical training judgment, not definitions alone.
Machine LearningTechnical Screen - LinkedInEasyMachine Learning EngineerExplain overfitting vs underfitting and fixes
This question tests the conceptual understanding of the bias-variance tradeoff and the practical strategies used to improve model generalization.
Machine LearningTechnical ScreenModel EvaluationBias-Variance TradeoffRegularization - LinkedInMediumData ScientistExplain variance reduction in random forests
This question tests your conceptual understanding of how Random Forests reduce the variance of base decision trees through bagging and feature randomness.
Machine LearningTechnical ScreenEnsemble MethodsRandom ForestBias-Variance Trade-off - LinkedInEasySoftware EngineerFind dictionary words that match a phone keypad digit sequence
Coding problem: given a list of known words and a digit string typed on a standard phone keypad, return every known word whose letters map to exactly that digit sequence, in sorted order. It tests string encoding, choosing a lookup structure for the dictionary, and analyzing time complexity.
Coding & AlgorithmsTechnical Screen - LinkedInMediumData ScientistFind index with positive suffix sums
This problem tests your ability to optimize array traversals using prefix or suffix sums, specifically evaluating your skill in calculating cumulative values efficiently in O(n) time.
Coding & AlgorithmsTechnical ScreenArraysSuffix SumOptimization - LinkedInMediumMachine Learning EngineerFind shortest word transformation with caching
Find shortest word transformation with caching 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 - LinkedInMediumSoftware EngineerFind the Node Where Two Singly Linked Lists Merge
Two singly linked lists stored in one successor array may merge at some node and share every node after it. Given both heads, return the first shared node, or -1 if the lists never meet. It tests linked-list traversal and careful handling of cases such as one list starting inside the other.
Coding & AlgorithmsTechnical Screen - LinkedInMediumSoftware EngineerGenerate All Distinct Permutations of a List with Duplicate Values
Given a list of up to eight integers that may contain duplicates, return every distinct permutation exactly once, sorted in lexicographic order. It tests systematic enumeration, avoiding duplicate results caused by equal values, and producing a deterministic output order.
Coding & AlgorithmsTechnical Screen - LinkedInEasyData ScientistHandle imbalance, validate samples, and avoid overfitting
This question tests your ability to design a robust machine learning pipeline for a highly imbalanced classification task, focusing on sampling strategies, metric selection, and regularization techniques to prevent overfitting.
Machine LearningTechnical ScreenImbalanceOverfittingRegularization - LinkedInEasyData ScientistHow do you sample uniformly from an infinite stream?
This question tests the implementation of Reservoir Sampling to maintain a uniform random sample of size k from a stream of unknown or infinite length.
Coding & AlgorithmsTechnical ScreenAlgorithmsProbabilityStreaming - LinkedInMediumSoftware EngineerHow do you win project buy-in?
This question evaluates leadership, stakeholder management, and team resiliency competencies by probing experience in securing project buy-in and in preventing single-point-of-failure through knowledge sharing, documentation, and ownership distribution.
Behavioral & LeadershipTechnical Screen - LinkedInMediumData ScientistHow to analyze Simpson's paradox
This question evaluates competency in causal reasoning, statistical inference, experimental design, and detection of Simpson's paradox within the Analytics & Experimentation domain.
Analytics & ExperimentationTechnical Screen - LinkedInHardData ScientistHow to diagnose traffic and measure relevance?
This problem tests your ability to diagnose an unexplained drop in engagement metrics by decomposing a complex user funnel, isolating root causes using multidimensional data, and designing a robust validation framework.
Analytics & ExperimentationTechnical ScreenRoot Cause AnalysisMetrics StrategyFunnel Analysis - LinkedInEasyData ScientistHow would you lead a team to improve quality?
This question evaluates leadership and technical execution competencies for a data scientist in a tech lead role, including work planning, delegation, accountability, stakeholder communication, root-cause diagnosis of product quality issues, metric definition, and delivering projects with measurable impact.
Behavioral & LeadershipTechnical Screen - LinkedInMediumMachine Learning EngineerImplement K-Means and Explain Convergence
This problem tests the implementation of the K-means clustering algorithm from scratch, requiring an understanding of the iterative refinement process, distance metrics, and the underlying mathematical objective.
Coding & AlgorithmsTechnical ScreenClusteringUnsupervised LearningAlgorithms - LinkedInHardMachine Learning EngineerImplement a Text-Embedding Recommender Training Pipeline
Tests the ability to structure an end-to-end, leakage-resistant training pipeline for a text-embedding recommender, covering profile flattening, embedding caching, temporal splits, binary classification, and artifact serialization.
Machine LearningTechnical Screenrecommender-systemsfeature-engineeringnlp-embeddings - LinkedInMediumData EngineerImplement a stack supporting max and popMax
This question evaluates understanding of data structures and algorithmic complexity, focusing on designing a stack variant that supports efficient retrieval and removal of the current maximum while preserving stack behavior.
Coding & AlgorithmsTechnical Screen - LinkedInMediumData ScientistImplement fast sampling for weighted k-sided die
This question evaluates understanding of probabilistic algorithm design, numerical stability, and efficient data-structure implementation for constant-time sampling from categorical distributions.
Coding & AlgorithmsTechnical Screen - LinkedInMediumData ScientistImplement stream random sampling in Python
This question evaluates understanding of streaming algorithms, uniform random sampling, and probabilistic guarantees for maintaining a fixed-size sample from an unbounded data stream (reservoir-sampling concepts).
Coding & AlgorithmsTechnical Screen - LinkedInMediumSoftware EngineerLongest Run of Ones After One Flip
This problem tests your ability to manage a sliding window with a flexible constraint, specifically counting zeros to determine the maximum length of a sequence after a single modification.
Coding & AlgorithmsTechnical ScreenSliding WindowTwo PointersArray - LinkedInHardData ScientistMeasure Relevant Feed Success
This question tests the ability to define North Star metrics for a social feed and design an experimentation framework that accounts for long-term ecosystem health and cross-side network effects.
Analytics & ExperimentationTechnical ScreenProduct AnalyticsA/B TestingMetrics - LinkedInEasySoftware EngineerMerge Two N-ary Trees by Matching Node Keys
Merge two rooted n-ary trees in which every node has an integer key, combining nodes only when their keys match under matching parents. Tests recursive tree traversal, matching children by key, handling roots with different keys, and producing a canonical preorder encoding of the merged tree.
Coding & AlgorithmsTechnical Screen - LinkedInHardSoftware EngineerMerge and Serialize N-ary Trees
Tests recursive tree manipulation, immutable merging semantics, and robust string serialization/deserialization with input validation.
Software Engineering FundamentalsTechnical Screentreerecursionparsing - LinkedInMediumSoftware EngineerMinimize adjacent-color assignment cost
This interview question evaluates algorithm design, data structures, correctness, complexity, edge cases, and implementation details in a realistic interview setting. A strong answer for Minimize adjacent-color assignment cost states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Coding & AlgorithmsTechnical Screen - LinkedInEasySoftware EngineerMinimum index distance between two given words in a word list
Coding problem: given a list of words and two different target words that both appear in it, return the smallest distance between an index of one word and an index of the other. It tests single-pass scanning with position tracking, handling of repeated occurrences, and time complexity analysis.
Coding & AlgorithmsTechnical Screen - LinkedInMediumSoftware EngineerRandomized String Multiset With Average O(1) Insert, Remove and Weighted Random Pick
Design a collection of strings that allows duplicates and supports insert, remove and a random pick in average constant time, where each stored occurrence is equally likely to be returned. It tests hash map and array layout, index bookkeeping on removal, and how string keys change the cost analysis.
Software Engineering FundamentalsTechnical Screen - LinkedInHardSoftware EngineerReduce Stale Listings in a Job Marketplace
Design an auditable staleness-detection and remediation engine for a global job marketplace that aggregates heterogeneous signals, enforces graduated interventions, and supports low-latency employer appeals.
System DesignTechnical ScreenDistributed SystemsEvent-Driven ArchitectureAudit Logging
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