Company
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.
35 easy348 medium103 hard
Google interview questions by the numbers
Last updated
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.
Topics
- Coding & Algorithms40% · 192
- Behavioral & Leadership19% · 94
- System Design8% · 40
- Statistics & Math7% · 32
- Machine Learning6% · 28
- Analytics & Experimentation5% · 23
- ML System Design3% · 15
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
Most mentioned in interview experiences
- 1.Choose Your Workplace Style3 reports
- 2.Select MOST/LEAST appropriate actions (SJT)3 reports
- 3.Choose best/worst actions in workplace ethics scenarios2 reports
- 4.Choose Non-Overlapping Weighted Jobs to Maximize Total Profit2 reports
- 5.Decide string transform with directional tokens and walls2 reports
- 6.Describe a conflict with a colleague2 reports
- 7.Describe a major challenge you faced2 reports
- 8.Design a Spam Review Workflow with Classifiers, AI Agents, and Human Labelers2 reports
- 9.Explain handling a tight project deadline2 reports
- 10.Grid Reachability From Start to Target With Impassable Water Cells2 reports
Counted from 74 candidate interview experiences on Katkat. How we collect and count
Showing 31–60 of 486 results
Evaluates classifier design for detecting dead links in imbalanced URL data with leakage-safe validation. Strong answers cover leakage-safe features, grouped validation, AUROC versus accuracy, PR metrics, thresholding, deployment, and monitoring.
GoogleData ScientistMachine LearningTechnical ScreenEvaluates renewal prediction modeling for enterprise Google Meet contracts. Strong answers define leakage-safe timelines, engineer usage, reliability, support, contract, and customer-health features, compare logistic regression with complex models, and evaluate calibration, lift, and business intervention value.
GoogleData ScientistMachine LearningTechnical ScreenTests implementation of a Trie (prefix tree) data structure to support word insertion, exact word search, prefix existence checks, and lexicographical autocomplete suggestions with a capacity limit.
GoogleSoftware EngineerCoding & AlgorithmsTechnical ScreenImplement a command dispatcher for a chat ops bot that resolves slash-separated commands with named arguments to a registered handler. Tests trie-based matching, precedence between literal and parameter segments, and handling of ambiguous or dead-end routes.
GoogleSoftware EngineerSoftware Engineering FundamentalsTechnical ScreenImplement a thread-safe asynchronous promise/future primitive with chaining and an aggregate fan-in barrier (`all_of`) for parallel processing.
GoogleSoftware EngineerSoftware Engineering FundamentalsTechnical ScreenThis problem tests your ability to process sequential text data, efficiently store word adjacency frequencies, and retrieve the most likely successor.
GoogleSoftware EngineerCoding & AlgorithmsOnsiteThis question evaluates proficiency in applied machine learning classification, including feature design, training a logistic regression, handling severe class imbalance, selecting evaluation metrics and calibration, choosing thresholds under asymmetric costs, and planning offline-to-online validation and monitoring.
GoogleData ScientistMachine LearningTechnical ScreenThis question tests the ability to architect an end-to-end classification pipeline for high-stakes, multimodal content moderation while accounting for class imbalance, adversarial evasion, and operational safety.
GoogleData ScientistMachine LearningTechnical ScreenThis problem tests your ability to design a memory-efficient data structure for storing n-gram probabilities and performing fast lookups for the most likely next word.
GoogleData ScientistCoding & AlgorithmsOnline AssessmentPractice a structured build-versus-buy product decision framework. The solution compares build, buy, and hybrid options across strategic differentiation, total cost of ownership, time to market, reliability, maintenance, vendor lock-in, data portability, pilots, stakeholder review, and risk mitigation.
GoogleProduct ManagerProduct / Decision MakingTechnical ScreenThis problem tests your ability to manage state and apply custom sorting logic to simulate resource allocation across time intervals.
GoogleSoftware EngineerCoding & AlgorithmsOnsiteTests tracking vehicle state across interleaved chronological event streams and computing distance-based segment tolls regardless of travel direction or checkpoint repetition.
GoogleSoftware EngineerCoding & AlgorithmsOnsiteThis question evaluates proficiency in SQL data aggregation, time-based grouping, and analytical window functions for computing month-over-month percentage changes in event volumes.
GoogleData ScientistData Manipulation (SQL/Python)OnsiteThis question evaluates proficiency with pandas group-wise computations, broadcasting of group aggregates, and DataFrame manipulation without renaming columns.
GoogleData ScientistData Manipulation (SQL/Python)Technical ScreenThis problem tests your ability to determine if one board state can be transformed into another using reversible movement rules, requiring the identification of invariants to avoid exhaustive searching.
GoogleSoftware EngineerCoding & AlgorithmsTechnical ScreenThis question evaluates understanding of bootstrap resampling, the distinction between estimating an estimator’s variance and methods that actually reduce estimator variance, and related ensemble reasoning for a Data Scientist role in the Statistics & Math domain.
GoogleData ScientistStatistics & MathOnsiteDetermine if a non-negative integer has digits that are monotonically non-decreasing from left to right, handling edge cases without unnecessary memory allocation.
GoogleSoftware EngineerCoding & AlgorithmsTechnical ScreenDetermine whether a proposed half-open interval overlaps with any interval in an unsorted list. This tests basic interval overlap logic and boundary condition handling.
GoogleSoftware EngineerCoding & AlgorithmsTechnical ScreenThis question evaluates string-processing skills and combinatorial reasoning about character multisets, specifically anagram recognition, substring enumeration, and the use of efficient representations for repeated checks.
GoogleSoftware EngineerCoding & AlgorithmsTechnical ScreenThis question evaluates string-processing skills such as substring enumeration and membership testing against a dictionary, along with algorithmic reasoning about correctness and complexity.
GoogleSoftware EngineerCoding & AlgorithmsTechnical ScreenThis question evaluates a candidate's competency in ML system design, focusing on balancing cost-versus-latency trade-offs for inference routing, operational metrics, workload segmentation, and reliability.
GoogleSoftware EngineerML System DesignTechnical ScreenChoose a set of jobs, each with a start time, end time and profit, so that no two chosen jobs overlap and the total profit is as large as possible. It tests sorting intervals, defining an optimal-substructure recurrence, and finding the latest compatible earlier job efficiently for large inputs.
GoogleSoftware EngineerCoding & AlgorithmsOnsiteDetermine at most one obstacle to remove in a grid to minimize the maximum time for signals to reach all target cells from multiple sources.
GoogleSoftware EngineerCoding & AlgorithmsOnsiteThis item evaluates workplace-style competencies such as self-awareness, prioritization, consistency, communication, adaptability, and time-management in a software engineering context.
GoogleSoftware EngineerBehavioral & LeadershipOnline AssessmentChoose and measure a malicious-webpage classifier metric using exposure-weighted recall, false-warning guardrails, and unbiased label audits.
GoogleData AnalystAnalytics & ExperimentationOnsiteThis question evaluates workplace ethics, confidentiality handling, conflict-of-interest recognition, anti-harassment awareness, integrity, and escalation judgment for software engineers.
GoogleSoftware EngineerBehavioral & LeadershipOnline AssessmentThis problem tests your ability to model uncertainty and implement a greedy strategy to minimize the expected search space size when provided with restricted (green-only) feedback.
GoogleSoftware EngineerCoding & AlgorithmsTechnical ScreenThis question evaluates a data scientist's competency in problem understanding and stakeholder clarification, covering active listening, assumption management, requirement elicitation, and cross-functional communication within the Behavioral & Leadership category.
GoogleData ScientistBehavioral & LeadershipTechnical ScreenA code review exercise built around a token bucket rate limiter written in Go and used as HTTP middleware. It tests finding the defects that matter in a production web service, deciding what blocks a merge, writing actionable review comments, and proposing a corrected limiter with tests that control time instead of sleeping.
GoogleSoftware EngineerSoftware Engineering FundamentalsOnsiteThis question tests your conceptual depth in LLM alignment by asking you to differentiate between supervised fine-tuning (SFT), reinforcement learning from human feedback (RLHF), direct preference optimization (DPO), and group-relative optimization (e.g., GRPO) based on their mathematical objectives and operational characteristics.
GoogleMachine Learning EngineerMachine LearningTechnical Screen
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 2026Offer
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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