Katkat

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Google interview questions

486 practice questions in the style of Google's interviews, covering Behavioral & Leadership, Coding & Algorithms, Machine Learning, Statistics & Math, Data Manipulation (SQL/Python), Analytics & Experimentation, Product / Decision Making, Software Engineering Fundamentals, ML System Design, System Design, Product Design & Strategy.

Questions486

35 easy348 medium103 hard

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Google interview questions by the numbers

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Katkat has 486 Google interview questions. The largest topics are Coding & Algorithms (40%), Behavioral & Leadership (19%) and System Design (8%). By difficulty: 7% easy, 72% medium and 21% hard. The most common round is Technical Screen (52%). Frequent concepts include Data Structures, Greedy, Algorithms and Optimization.

Difficulty

  • Easy7% · 35
  • Medium72% · 348
  • Hard21% · 103

Interview rounds

  • Technical Screen52% · 253
  • Onsite42% · 204
  • Online Assessment2% · 12
  • Other2% · 10
  • HR Screen1% · 7

Roles

  • Software Engineer65% · 317
  • Data Scientist20% · 96
  • Machine Learning Engineer7% · 35
  • Product Manager5% · 25
  • Data Analyst1% · 5

Frequent concepts

  • Data Structures5% · 24
  • Greedy5% · 22
  • Algorithms4% · 21
  • Optimization4% · 19
  • Arrays3% · 17
  • Graph3% · 14

Showing 271–300 of 486 results

  • This question evaluates a candidate's ability to model pairwise relationships and compute connected components in small graphs, testing skills in graph connectivity and grouping based on shared attributes in arrays of two-digit numbers.

    GoogleSoftware EngineerCoding & AlgorithmsOnline Assessment
  • This problem tests your ability to use a sliding window or linear scan approach to identify contiguous subarrays and your capacity to optimize for a single modification to extend an existing sequence.

    GoogleSoftware EngineerCoding & AlgorithmsTechnical Screen
  • This question evaluates a candidate's ability to design memory-efficient streaming algorithms and parallel processing strategies for extremely large files, covering skills in external-memory algorithms, file I/O, concurrency, and correctness under resource constraints.

    GoogleSoftware EngineerCoding & AlgorithmsTechnical Screen
  • This problem tests your ability to find the median of two sorted, run-length encoded arrays without fully decompressing them, focusing on efficiency and pointer manipulation.

    GoogleSoftware EngineerCoding & AlgorithmsTechnical Screen
  • This question evaluates a candidate's understanding of tree data structures and frequency analysis, focusing on handling a trinary search tree variant and determining mode values across possibly large datasets.

    GoogleSoftware EngineerCoding & AlgorithmsTechnical Screen
  • This problem tests knowledge of the sum-of-two-squares theorem and efficient counting of integer partitions into two squares, requiring an understanding of number theory or optimization via circle/grid search.

    GoogleSoftware EngineerCoding & AlgorithmsTechnical Screen
  • This question evaluates algorithmic reasoning and inference from constrained feedback, testing skills in information-theoretic deduction, pattern matching, and search strategy within a bounded-query interactive setting.

    GoogleSoftware EngineerCoding & AlgorithmsTechnical Screen
  • This question evaluates the ability to design efficient algorithms and data-processing strategies for identifying shared object identifiers across large log files, testing skills in streaming processing, set membership handling, and external-memory algorithm reasoning.

    GoogleSoftware EngineerCoding & AlgorithmsTechnical Screen
  • This question evaluates proficiency in graph traversal and shortest-path computation, including handling nodes that are blocked or that incur additional costs. It is commonly asked in the coding and algorithms domain (graph algorithms) to assess both practical implementation skills and conceptual understanding of complexity, edge cases, and trade-offs between unweighted and weighted path models.

    GoogleSoftware EngineerCoding & AlgorithmsOnsite
  • This problem tests your ability to model a relationship network as a graph and implement Breadth-First Search (BFS) to find the shortest path length between two nodes.

    GoogleSoftware EngineerCoding & AlgorithmsTechnical Screen
  • This problem tests your ability to use the sliding window (two-pointer) technique combined with hash maps to find an optimal subarray satisfying a frequency constraint.

    GoogleSoftware EngineerCoding & AlgorithmsTechnical Screen
  • This question tests the ability to solve subarray problems using the sliding window (two-pointer) technique and hash maps to track element frequencies.

    GoogleSoftware EngineerCoding & AlgorithmsTechnical Screen
  • Tests greedy algorithms, digit manipulation, and boundary handling to find the largest number less than or equal to n with non-decreasing decimal digits.

    GoogleSoftware EngineerCoding & AlgorithmsTechnical Screen
  • Practice a Google coding interview problem focused on find the longest consecutive path in a binary tree. The prompt emphasizes edge cases, clean implementation, and verifiable test behavior without revealing the solution.

    GoogleSoftware EngineerCoding & AlgorithmsOnsite
  • Tests array traversal, state tracking, and boundary handling to find the maximum length of a contiguous, strictly increasing run in linear time.

    GoogleSoftware EngineerCoding & AlgorithmsOnsite
  • This problem tests tree traversal techniques and dynamic programming on trees, specifically how to track path lengths across subtree boundaries using a recursive helper function.

    GoogleSoftware EngineerCoding & AlgorithmsOnsite
  • Tests grid traversal and dynamic programming to find the maximum consecutive sequence of identical elements along horizontal, vertical, and diagonal lines.

    GoogleSoftware EngineerCoding & AlgorithmsTechnical Screen
  • Find the ten most frequent normalized IP addresses with cutoff ties preserved, first in memory and then exactly through disk partitioning, per-partition aggregation, global thresholding, and skew handling.

    GoogleSoftware EngineerSoftware Engineering FundamentalsTechnical Screen
  • Implement a multi-source breadth-first search to find the minimum time needed for signals emitted from multiple sources to reach all target cells on a grid with obstacles.

    GoogleSoftware EngineerCoding & AlgorithmsOnsite
  • Given a company reporting tree in which every employee except the top one has a single direct manager, find the nearest manager shared by a list of K employees, counting each employee as part of their own chain. Tests tree reasoning, ancestor chains and very deep hierarchies.

    GoogleSoftware EngineerCoding & AlgorithmsOnsite
  • This question tests the ability to combine frequency counting with heap-based selection to extract the k most frequent distinct elements from an input stream.

    GoogleSoftware EngineerCoding & AlgorithmsOnsite
  • This problem tests your ability to model a geometric area as a function and solve for a specific threshold using a binary search or linear sweep approach.

    GoogleSoftware EngineerCoding & AlgorithmsTechnical Screen
  • Tests backtracking and recursion to generate all valid ordered compositions of a target integer using only digits 1 and 2 in lexicographical order.

    GoogleSoftware EngineerCoding & AlgorithmsOnsite
  • Evaluates truncated normal distribution fundamentals and sampling methods for lower-tail truncation. Strong answers name the lower-truncated normal, provide the PDF and CDF, and compare inverse-CDF, rejection, and tail-efficient samplers.

    GoogleData ScientistStatistics & MathTechnical Screen
  • This problem tests the ability to generate uniform spatial samples using standard random functions and the mathematical application of linear interpolation for percentile estimation from binned data.

    GoogleData ScientistStatistics & MathTechnical Screen
  • This question evaluates understanding of probabilistic sampling, numerical stability, input validation, and algorithmic trade-offs for streaming weighted random draws, including generator determinism via seeding and methods such as CDF/binary-search versus alias for performance.

    GoogleMachine Learning EngineerStatistics & MathOnsite
  • Practice a Google product strategy case on five-year threats and technology trends. The solution frames Google's moats, evaluates AI-native search, regulation, distribution shifts, multimodal and privacy-preserving trends, and deep-dives on AI assistants with execution challenges, product applications, monetization, and guardrails.

    GoogleProduct ManagerProduct / Decision MakingOnsite
  • A Google product sense question: propose a product that helps new parents. It tests how a PM narrows an open-ended prompt to one segment, ranks real pain points, uses Google's existing strengths to shape a focused first version, and defines success metrics plus the safety and privacy risks of serving families.

    GoogleProduct ManagerProduct Design & StrategyOther
  • Practice PM onsite prompts covering product critique, going above and beyond for users, and resolving disagreement with metrics. The guide shows how to analyze a poorly designed product, define improvements and trade-offs, tell STARL stories, and use trusted metrics to align stakeholders.

    GoogleProduct ManagerBehavioral & LeadershipOnsite
  • Decide whether a start cell can reach a target cell in a square grid when some cells are water and moves are limited to up, down, left and right. It tests modeling a grid as a graph, choosing a traversal, and handling walled-off regions and grid edges correctly.

    GoogleSoftware EngineerCoding & AlgorithmsOnsite

Google interview experiences

All 103 →
  • A Huge YAML Merge Problem and a Knapsack That Wants QuickselectGoogle · Software Engineer · Intern · Technical Screen · Oct 2026

    The first-round interviewer was really nice, but the problem was super long. Reading and understanding the problem plus the examples can take maybe 20 minutes. Roughly, it is merging two trees (YAML files), where every node has merge rules, and there are a lot of rules. The approach is DFS plus rule matching. The second-round interviewer gave me a knapsack problem, but for this one DP or DFS with…

  • Back-to-Back Phone Screens: Behavioral, Then an HTTP RouterGoogle · Software Engineer · Technical Screen · Oct 2026

    After I finished the psychological test, they scheduled my phone interviews right away. It was two rounds back to back, 45 minutes each, and I don't know if that's normal. The first round was a behavioral round with a really nice interviewer. All the questions were the usual ones: what do you do if you disagree with a coworker, what do you do if you disagree with another team, what do you do if a…

  • In-Person Onsite with a Design Round and a Train Schedule QuestionGoogle · Software Engineer · Onsite · Oct 2026

    The onsite was in-person (but virtual was an option) and only two rounds. I only had 4 interviews total with Google. Onsite was solutions design and Leetcode question. Solutions design question was design a review ingestion platform for an e-commerce site that takes reviews and summarizes them with an LLM for an internal dashboard. Focus on trust and safety, prompt injection, data sanitization, P…

  • 10-Minute Resume Grill, Then First Missing Positive in O(1) SpaceGoogle · Software Engineer · Intern · Technical Screen · Sep 2026

    The interview started right on time and went straight into an intense 10-minute resume grill. The interviewer homed in on my recent backend work, specifically asking detailed questions about the Kafka streaming architecture and Redis caching implementations from my previous internship in the Platform Cloud org. They also dug into the technical trade-offs I made when building Bastion, focusing hea…

  • An Unexpected Forecasting Question and a Fast Offer TimelineGoogle · Data Scientist · Onsite · Sep 2026
    Offer

    I signed Google's offer a couple of days ago, though I still have a few other issues to resolve. I'm sharing my interview experience and preparation package, and hoping I can start smoothly. First round Stats: Questions about p-values, simple linear regression, confidence intervals, and similar topics. Data: Compare two vendors' models. There are similar questions in other interview reports. Seco…

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