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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.
- 9403
- Questions
- 546
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- 11
- Topics
- 8
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Showing 961–990 of 4616 results
- C3 AIHardData ScientistCompare PCA and Autoencoders Under the Curse of Dimensionality
This question tests your conceptual understanding of dimensionality reduction techniques and your ability to weigh their performance in high-dimensional feature spaces prone to the curse of dimensionality.
Machine LearningTechnical ScreenDimensionality ReductionPCAAutoencoders - C3 AIHardData ScientistCompare Transformers and RNNs and Derive Attention Complexity
Evaluate candidate's deep learning foundations by comparing Transformer and recurrent architectures and deriving exact time/memory complexities for standard scaled dot-product attention across training and inference.
Machine LearningTechnical ScreenTransformersRNNAttention Mechanism - C3 AIHardData ScientistConnect Bias-Variance Trade-offs to Bagging and Boosting
This question tests the conceptual understanding of how ensemble methods manipulate model error components through different structural paradigms.
Machine LearningTechnical ScreenBias-VarianceEnsemble LearningBagging - C3 AIHardData ScientistEvaluate a Retrieval-Augmented Generation System
Design an end-to-end evaluation framework for an enterprise RAG system that assesses retrieval quality, generative fidelity, claim-level attribution, security controls, and production telemetry.
ML System DesignTechnical ScreenRAGLLM EvaluationSystem Design - C3 AIMediumData ScientistExplain the Gaussian Distribution and Its Limits
Tests a candidate's mathematical understanding of the Gaussian distribution, key theoretical properties such as affine closure and the Central Limit Theorem, and the ability to diagnose and handle non-normal distributions in real-world modeling.
Statistics & MathTechnical ScreenStatisticsProbabilityAnomaly Detection - C3 AIHardData ScientistFind longest substring with at least k repeats
This interview question evaluates algorithm design, data structures, correctness, complexity, edge cases, and implementation details in a realistic interview setting. A strong answer for Find longest substring with at least k repeats states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Coding & AlgorithmsTechnical Screen - C3 AIHardData ScientistForecast Food Stocking Needs Under Waste and Stockout Costs
Design an end-to-end inventory forecasting and stocking optimization system for perishable food items under asymmetric costs of spoilage versus stockouts. The design covers handling censored sales data, probabilistic demand modeling, cost-optimal stocking policies, and validation strategy.
ML System DesignTechnical Screenml-system-designtime-seriessupply-chain - C3 AIEasyData ScientistIdentify a classic bagging algorithm
This interview question evaluates core ML concepts, assumptions, math intuition, training/evaluation trade-offs, and practical failure modes in a realistic interview setting. A strong answer for Identify a classic bagging algorithm states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Machine LearningTechnical Screen - C3 AIHardData ScientistKeep Gradient Descent from Stalling at Poor Solutions
Design a rigorous diagnostic procedure and remediation plan for gradient descent stalling at suboptimal loss values, differentiating true local minima from saddle points, vanishing gradients, and poor conditioning.
Machine LearningTechnical ScreenOptimizationDeep LearningLoss Landscapes - C3 AIMediumData ScientistLongest Substring Where Every Character Appears at Least K Times
Find the longest substring in which every present character appears at least k times. Practice divide-and-conquer splitting, frequency reasoning, and fixed-alphabet complexity.
Coding & AlgorithmsTechnical Screen - CVS HealthMediumData ScientistAggregate radiology spend and derive fiscal month
This question evaluates a candidate's competency in data manipulation and preprocessing using SQL/Python, covering robust dtype specification, null handling, numeric aggregation and percentage computation, datetime parsing, and fiscal-month derivation.
Data Manipulation (SQL/Python)Technical Screen - CVS HealthMediumData ScientistCalculate annual percentages and YoY by cohorts
This question evaluates SQL and pandas data manipulation skills, specifically joins, conditional aggregation, deduplication, cohort-based percentage computations, and year-over-year change calculations.
Data Manipulation (SQL/Python)Technical Screen - CVS HealthEasyData ScientistCompute specialty spend share and top age band
This question evaluates data manipulation and aggregation skills in SQL and Pandas, including joins/unions across disjoint member tables, computation of specialty spend shares, identification of top age bands by claim counts with tie handling, year-based date filtering, and categorical recoding with monthly grouping.
Data Manipulation (SQL/Python)Technical Screen - CVS HealthHardData ScientistDesign a flu-shot A/B/n campaign experiment
This question evaluates a data scientist's competency in experimental design, causal inference, sample-size and power calculations, compliance-adjusted effect estimation, attribution instrumentation, and reporting for multi-channel marketing experiments.
Analytics & ExperimentationTechnical Screen - CVS HealthHardData ScientistDesign classification under missingness and imbalance
This question evaluates a data scientist's competency in missing data diagnosis and imputation, handling severe class imbalance, choosing and interpreting classification and calibration metrics, and improving logistic regression as part of an end-to-end predictive pipeline on claims and EHR data, and it falls under the Machine Learning domain.
Machine LearningTechnical Screen - CVS HealthMediumData ScientistExplain p-value and choose correct test
This question evaluates a candidate's understanding of statistical inference, hypothesis testing, and robust test selection, focusing on p-value interpretation and choosing between paired vs independent and parametric vs rank-based tests.
Statistics & MathTechnical Screen - CVS HealthHardData ScientistHandle challenges in MMM/MMX
This question evaluates a data scientist's competency in applied Machine Learning and causal inference for marketing-mix modeling, focusing on diagnosing model fragility from multicollinearity, endogeneity, non‑stationarity, measurement error, and decisions around adstock, saturation and promotion effects.
Machine LearningTechnical Screen - CVS HealthHardData ScientistLaunch and measure a TV campaign
This question evaluates a data scientist's competency in experimental design and causal inference, including geo-level matching, KPI definition, media measurement (GRP/TRP, reach/frequency, adstock and saturation modeling), power analysis, and operational risk management for measuring incremental vaccinations.
Analytics & ExperimentationTechnical Screen - CVS HealthHardData ScientistLead structured response to accuracy incident
This question evaluates incident leadership, operational decision-making, and data-driven troubleshooting skills for a data scientist, focusing on triage, hypothesis formation, mitigation choice, cross-functional communication, and recurrence prevention.
Behavioral & LeadershipTechnical Screen - CVS HealthEasyData ScientistReduce LLM hallucination and handle class imbalance
This question tests your ability to design a robust pipeline for detecting clinical inaccuracies in LLM outputs while managing highly imbalanced datasets where missing a critical diagnosis is costlier than a false alarm.
Machine LearningTechnical ScreenLLMRAGImbalanced Learning - CVS HealthMediumData ScientistTune classifier and compute key metrics
This question tests your ability to optimize a classification threshold based on business costs and your understanding of how to bridge classification and regression tasks.
Machine LearningTechnical ScreenClassificationCost-sensitive LearningThresholding - CVS HealthMediumData ScientistUse pandas to aggregate, pivot, and label
This question evaluates competency in pandas-based tabular data manipulation—specifically DataFrame merging, groupby aggregation, pivot table construction, counting unique values, and conditional flagging with numpy—within the domain of Data Manipulation (SQL/Python) and is primarily at the practical application level.
Data Manipulation (SQL/Python)Technical Screen - CVS HealthMediumData ScientistWrite SQL for dedup and purchase shares
This question evaluates proficiency in SQL data manipulation skills including entity deduplication, canonicalization rules, window functions, common table expressions, joins, and aggregation to derive per-user purchase counts and distributions.
Data Manipulation (SQL/Python)Technical Screen - CadenceMediumMachine Learning EngineerExplain Transformer Positional Encoding
This question tests your conceptual and technical understanding of why Transformers require positional encodings and the mathematical mechanics of different encoding strategies.
Machine LearningTechnical ScreenTransformerAttentionNLP - CadenceMediumMachine Learning EngineerImplement Depth-First Search Traversal
This problem tests your ability to implement a recursive or iterative depth-first search (DFS) while managing state to handle cycles in a graph represented as an adjacency list.
Coding & AlgorithmsTechnical ScreenGraphsDFSRecursion - Candid HealthEasySoftware EngineerImplement a Callback Signaling Registry with Register, Unregister and Signal
Implement a minimal in-process signaling system with register_callback, unregister_callback and signal functions that all return None. Tests requirement clarification, callback storage and hashability, execution order, isolation of failing callbacks, and prevention of reentrant signal loops.
Software Engineering FundamentalsTechnical Screen - Capital OneMediumData ScientistAnalyze ad watch-time with Excel pivots
This question evaluates a data scientist's proficiency with Excel PivotTables, date and ISO-week handling, aggregation and ratio metrics (clicks per watch-minute), and deterministic tie-breaking logic for reproducible results.
Analytics & ExperimentationTechnical Screen - Capital OneMediumData EngineerAnswer conflict and ambiguity with STAR stories
This question evaluates a candidate's conflict-resolution, cross-functional communication, technical troubleshooting, ambiguity-management, and reflective learning competencies within a data engineering context.
Behavioral & LeadershipTechnical Screen - Capital OneMediumSoftware EngineerAnswer learning and challenge behavioral prompts
This question evaluates learning agility, problem-solving, influence, and leadership competencies by probing quick learning, process-challenging, and trade-off management in real work scenarios.
Behavioral & LeadershipTechnical Screen - Capital OneMediumSoftware EngineerAnswer motivation and teamwork questions
The question evaluates motivation, cultural fit, communication, teamwork, conflict-resolution, and the ability to describe concrete impact from collaborative work.
Behavioral & LeadershipTechnical Screen
C3 AI
CVS Health
Capital One