Google interview questions

Showing 331–360 of 486 results

• Implement a Transformer Block with SwiGLUMedium

  This question evaluates implementation and reasoning skills for Transformer internals, testing competency in multi-head self-attention, SwiGLU gated feed-forward layers, residual connections, and layer normalization within the Machine Learning / deep learning domain.

  GoogleMachine Learning EngineerCoding & AlgorithmsTechnical Screen
• Implement a URL router that matches request paths against patterns with placeholdersHard

  Implement a URL router that registers path patterns with placeholder segments such as {userId} and returns the registered pattern that matches a concrete request path. It tests choosing a segment-level data structure, defining precedence between literal and placeholder matches, and analyzing registration and lookup complexity.

  GoogleSoftware EngineerSoftware Engineering FundamentalsOnsite
• Implement an HTTP Router That Resolves Handlers for Paths with Named ParametersMedium

  Implement an HTTP router that registers handlers for path patterns, some with named parameters and some without, and looks up the handler and parameter values for a request path. It tests data structure choice, precedence between fixed and parameterized segments, conflict handling and lookup complexity.

  GoogleSoftware EngineerSoftware Engineering FundamentalsTechnical Screen
• Implement an LRU cacheMedium

  This question evaluates competency in data structure design and algorithmic complexity by requiring a bounded key-value cache with O(1) average-time get and put operations.

  GoogleSoftware EngineerCoding & AlgorithmsOnsite
• Implement anagram check and stable deduplicationMedium

  This question evaluates streaming algorithm design, Unicode normalization and locale-aware text processing, multiset frequency counting under tight time and memory bounds for an anagram checker, and stable, order-preserving deduplication with custom keys and NaN handling; it is in the Coding & Algorithms domain for a Data Scientist position.

  GoogleData ScientistCoding & AlgorithmsTechnical Screen
• Implement frequency + distance top‑K queriesMedium

  This pair of problems evaluates algorithmic and data-structure skills including frequency counting, multi-criteria ordering and selection for top-K results, geometric distance computation for tie-breaking, and time-indexed storage with nearest-timestamp retrieval.

  GoogleSoftware EngineerCoding & AlgorithmsTechnical Screen
• Implement iterator merging lists with filteringMedium

  This problem tests your ability to design a stateful iterator that performs a multi-way merge on sorted streams while applying filtering and deduplication logic on the fly.

  GoogleSoftware EngineerCoding & AlgorithmsTechnical Screen
• Implement longest subarray summing to kMedium

  This question evaluates proficiency in array algorithms, prefix-sum reasoning, hash-based lookup patterns, and careful handling of edge cases and deterministic tie-breaking.

  GoogleData ScientistCoding & AlgorithmsOnsite
• Implement matrix transforms and discuss eigenvaluesMedium

  This problem tests your ability to perform in-place 2D matrix transformations and your conceptual understanding of how linear algebraic properties like eigenvalues describe matrix behavior.

  GoogleSoftware EngineerSoftware Engineering FundamentalsOnsite
• Implement percentage RMSE and bootstrap its CIMedium

  This question evaluates implementation and statistical reasoning skills, focusing on robust numerical computation of percentage RMSE (including handling zeros/negatives, optional country weights, and numerical stability) and nonparametric bootstrap methods for confidence intervals.

  GoogleData ScientistCoding & AlgorithmsTechnical Screen
• Implement piecewise linear interpolation for time-to-emptyMedium

  This question evaluates numerical interpolation, time-series data cleaning, monotonicity preservation, extrapolation, and numerical stability skills applied to battery discharge curves, and falls under coding and algorithms within a data science domain.

  GoogleData ScientistCoding & AlgorithmsTechnical Screen
• Implement quicksort and a binary search tree, and explain how each works internallyMedium

  Implement quicksort and a binary search tree from scratch and explain how each works internally, including partitioning, pivot choice, insertion, search and deletion. Tests correctness invariants, best, average and worst-case complexity, duplicate and sorted inputs, alternative approaches, and how library implementations differ.

  GoogleSoftware EngineerSoftware Engineering FundamentalsTechnical Screen
• Implement sampling and subarray scanMedium

  This question evaluates probabilistic sampling and random-number transformation concepts for generating uniform points in a continuous domain, alongside array-processing and algorithmic analysis skills for identifying the longest strictly increasing contiguous subarray.

  GoogleData ScientistCoding & AlgorithmsTechnical Screen
• Implement sampling, subarray scan, and percentile estimateEasy

  This problem tests your ability to generate random uniform points within a geometric shape, process contiguous array properties using linear scans, and perform linear interpolation on histogram data to approximate percentiles.

  GoogleData ScientistCoding & AlgorithmsTechnical Screen
• Implement string formatting and request tracking tasksEasy

  This question tests string manipulation, character mapping logic for rotational symmetry, and the ability to manage time-ordered event tracking using appropriate data structures.

  GoogleSoftware EngineerCoding & AlgorithmsTechnical Screen
• Implement substring search and weighted samplingMedium

  This question tests the ability to implement a fast substring matching algorithm and a data structure that supports weighted random sampling from a dynamic collection of items.

  GoogleMachine Learning EngineerCoding & AlgorithmsTechnical Screen
• Implement text wrapping, waitlist, and intervalsMedium

  This problem tests your ability to implement a greedy word-wrapping algorithm and a custom queue data structure that supports efficient deletions based on time intervals.

  GoogleSoftware EngineerCoding & AlgorithmsOnsite
• Infection Spread on a Grid (Cellular Automaton)Hard

  Implement an optimized discrete-time cellular automaton simulating epidemic spread on a 2D grid with recovery durations, barriers, and multi-neighbor infection thresholds.

  GoogleSoftware EngineerCoding & AlgorithmsTechnical Screen
• Infer distribution and choose robust statisticsMedium

  This problem tests the ability to diagnose non-normal data distributions (specifically zero-inflated, heavy-tailed data) and select appropriate statistical metrics for hypothesis testing.

  GoogleData ScientistStatistics & MathTechnical Screen
• Interpret Language-Model PerplexityHard

  Learn the exact likelihood basis of perplexity, correct evaluation details, tokenizer caveats, and limits as an LLM quality metric.

  GoogleMachine Learning EngineerMachine LearningTechnical Screen
• Interpreting a Dosage-Recovery Regression: P-values, CIs, CausalityMedium

  Given a fitted dosage-vs-recovery-time regression, explain exactly what the reported p-value and confidence interval mean, what assumptions back that inference, how a confidence interval differs from a prediction interval, and why the result alone cannot establish causality. Tests statistical precision under pressure.

  GoogleData ScientistStatistics & MathOnsite
• Intervals with Exactly X Concurrent MeetingsMedium

  Implement an algorithm to identify all maximal time intervals during which exactly X meetings overlap, using a sweep-line technique over half-open intervals.

  GoogleSoftware EngineerCoding & AlgorithmsOnsite
• Investigate a Decrease in Webpage WarningsHard

  Diagnose falling malicious-webpage warnings by decomposing traffic, scoring, flags, rendering, and labels to separate improvement from failure.

  GoogleData AnalystAnalytics & ExperimentationOnsite
• Investigate a Drop in SalesHard

  Use a structured sales-drop investigation covering metric definitions, data quality, funnel decomposition, segmentation, and causal evidence.

  GoogleData AnalystAnalytics & ExperimentationOnsite
• Largest-Area Rectangle From a Point Set When Sides Need Not Be Axis-AlignedMedium

  Given distinct integer points in the plane, return the largest area of a rectangle whose four corners are input points, where the rectangle may be rotated rather than aligned with the axes. It tests the geometric characterization of rectangles, grouping point pairs efficiently, and exact integer arithmetic.

  GoogleSoftware EngineerCoding & AlgorithmsOnsite
• Layer Config Trees with Pluggable Per-Path Merge StrategiesMedium

  Combine a default configuration tree with an environment overlay where each path can pick a merge strategy such as deep, override, preserve, concat, union, or keyed list matching, and new strategies plug in without touching the traversal. Tests simultaneous tree recursion, registry-based extensibility, ordering rules and type-conflict handling.

  GoogleSoftware EngineerSoftware Engineering FundamentalsTechnical Screen
• Lead a Project Through Ambiguous RequirementsMedium

  This question evaluates your ability to take ownership of ambiguous, loosely defined technical initiatives, structure unknown variables, validate assumptions through experiments, and pivot effectively based on emerging data.

  GoogleSoftware EngineerBehavioral & LeadershipOnsite
• Lead a deep dive on your most complex projectMedium

  This question evaluates a candidate's systems architecture and technical leadership skills, including design reasoning, trade-off analysis, incident management, and reflective learning.

  GoogleSoftware EngineerBehavioral & LeadershipOnsite
• Leading an Engineering Team Back From Low MoraleMedium

  A behavioral question for an engineering manager candidate about leading a team whose morale had dropped. It tests how the candidate reads the signals, diagnoses causes through one-on-ones and data, acts on causes inside and outside their control, communicates honestly, and shows that the team recovered.

  GoogleSoftware EngineerBehavioral & LeadershipOnsite
• Learning from Failure & ConflictMedium

  Google PM behavioral screen covering failure or conflict, ownership, resilience, stakeholder communication, learning, and mission fit using STAR or STAR-L structured answers.

  GoogleProduct ManagerBehavioral & LeadershipHR Screen