Machine Learning interview questions

Showing 301–330 of 572 results

• What features and feature selection would you use?Medium

  This question evaluates feature engineering, feature selection, and production-aware machine learning system design skills for ranking shop ads, within the Machine Learning domain for a Data Scientist role.

  MetaData ScientistMachine LearningTechnical Screen
• Which clustering algorithm would you use and whyMedium

  A Meta Data Scientist machine learning screen on choosing a clustering algorithm for social-product users. It contrasts traditional feature-vector clustering (k-means, GMM, hierarchical, DBSCAN/HDBSCAN) with social-graph community detection (Louvain/Leiden, spectral, SBM, node embeddings), and covers preprocessing, choosing the number of clusters, evaluation, directed/weighted graphs, and scaling to millions of users.

  MetaData ScientistMachine LearningTechnical Screen
• Build and Defend a Baseline Model from a CSVMedium

  Tests end-to-end applied machine learning design on tabular data: framing the prediction target, eliminating data leakage, establishing a leakage-free validation strategy, selecting baselines and evaluation metrics, and planning deployment monitoring and rollback.

  MicrosoftData ScientistMachine LearningOnsite
• Clean OCR data and build an LLM datasetMedium

  This problem tests the ability to design an automated pipeline for cleaning noisy OCR text output and preparing it for supervised fine-tuning of an LLM.

  MicrosoftMachine Learning EngineerMachine LearningOnsite
• Compare CNN/RNN/LSTM and implement K-meansHard

  This question evaluates understanding of deep learning architectures (CNN, RNN, LSTM) and unsupervised clustering implementation (K-means), covering inductive biases, parameter sharing and receptive fields, LSTM gating and vanishing-gradient behavior, tensor shape reasoning, algorithmic complexity, initialization, and cluster handling.

  MicrosoftData ScientistMachine LearningOnsite
• Compare preference alignment methods for LLMsMedium

  This question tests your conceptual understanding of alignment techniques, your ability to evaluate trade-offs between stability and performance, and your approach to measuring model safety and utility.

  MicrosoftMachine Learning EngineerMachine LearningOnsite
• Compute and plot a precision–recall curveEasy

  This question evaluates understanding of binary classification evaluation metrics—precision, recall, precision–recall curves, and related summaries like Average Precision/AUPRC—within the Machine Learning domain and is relevant for Data Scientist roles.

  MicrosoftData ScientistMachine LearningTechnical Screen
• Design a model for imbalanced conversionsHard

  This question evaluates a data scientist's ability to design and validate end-to-end predictive models for imbalanced binary outcomes, encompassing feature engineering, class imbalance handling, probability calibration, thresholding for budgeted targeting, validation, monitoring, and interpretability.

  MicrosoftData ScientistMachine LearningTechnical Screen
• Explain Deep Learning to a 5-Year-Old ChildMedium

  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 Explain Deep Learning to a 5-Year-Old Child states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  MicrosoftData ScientistMachine LearningTechnical Screen
• Explain KNN and PCA and key tradeoffsEasy

  Evaluates understanding of K-Nearest Neighbors (instance-based classification/regression) and Principal Component Analysis (linear dimensionality reduction), highlighting tradeoffs such as distance metric and preprocessing effects, computational and high-dimensional limitations, PCA’s optimization/computation approaches, and the impact of dimensionality reduction on downstream nonparametric methods. Common in the Machine Learning domain for Data Scientist internships at a fundamentals-to-intermediate abstraction level because it probes both theoretical foundations and practical considerations for applying non-parametric algorithms and linear feature extraction to real datasets.

  MicrosoftData ScientistMachine LearningTechnical Screen
• Explain KNN and how to tune itEasy

  This question evaluates understanding of the K-Nearest Neighbors algorithm and related competencies such as hyperparameter selection (K, distance metric, weighting), data preprocessing impacts, failure modes in high-dimensional or imbalanced settings, and model evaluation strategies.

  MicrosoftData ScientistMachine LearningTechnical Screen
• Explain SHAP and build an ML projectEasy

  This question tests your conceptual understanding of SHAP values for model interpretability and your ability to design a robust, end-to-end machine learning pipeline for a real-world business problem.

  MicrosoftData ScientistMachine LearningTechnical Screen
• Explain SHAP in an ML SystemMedium

  This question evaluates a data scientist's competence in designing and operationalizing end-to-end machine learning systems in the Machine Learning domain, covering problem framing, label and feature design, model selection, evaluation, deployment, monitoring, and model interpretability using SHAP.

  MicrosoftData ScientistMachine LearningTechnical Screen
• Explain metrics, regularization, and ablation studiesMedium

  This question evaluates understanding of classification evaluation metrics, regularization methods, and experimental design for component-level analysis in NLP and broader Machine Learning systems, testing competencies in performance measurement, regularization trade-offs, and causal attribution of model components.

  MicrosoftMachine Learning EngineerMachine LearningTechnical Screen
• Greedy and Top-k Beam Search Decoding That Returns the Sentence ProbabilityMedium

  A coding question on decoding a sequence from a next-token probability distribution: implement greedy decoding, extend it to beam search that keeps the top k partial sequences, and return the chosen sequence with its overall probability. It also probes why long sequences tend to receive low probability and how decoding should account for that.

  MicrosoftSoftware EngineerMachine LearningTechnical Screen
• How would you build and evaluate a classifier?Medium

  This problem tests your ability to design a binary classification pipeline from metric selection to production monitoring, with a focus on aligning technical decisions with business objectives.

  MicrosoftData ScientistMachine LearningTechnical Screen
• Measure LLM Quality and Detect Hallucinations, Then Implement an Evaluation ComponentMedium

  A hands-on LLM evaluation exercise on measuring model quality, comparing prompt or model versions, and detecting hallucinations against supplied context. It also asks for a small Python groundedness scorer that flags unsupported sentences and aggregates a hallucination rate, testing evaluation judgment and clean, well-tested code.

  MicrosoftMachine Learning EngineerMachine LearningOnsite
• How would you model stock price prediction?Medium

  This problem tests the candidate's ability to design an end-to-end machine learning pipeline for financial time-series forecasting, specifically focusing on the nuances of feature engineering and evaluation in a non-stationary environment.

  MillenniumSoftware EngineerMachine LearningTechnical Screen
• Answer an LLM Systems Quick-Fire InterviewMedium

  This question tests your breadth of knowledge across the LLM stack, specifically focusing on inference optimization, evaluation strategies, and operational trade-offs.

  Mistral AIMachine Learning EngineerMachine LearningOnsite
• Compare RMSNorm and LayerNorm in Transformer BlocksMedium

  Compare LayerNorm and RMSNorm equations, centering, shift behavior, hidden-feature axes, numerical precision, and the tradeoffs behind simpler normalization.

  Mistral AISoftware EngineerMachine LearningTechnical Screen
• Compare Reward-Model RLHF and Direct Preference OptimizationMedium

  Compare reward-model RLHF and DPO through preference data, policy objectives, reference regularization, training stages, and evaluation under distribution shift.

  Mistral AISoftware EngineerMachine LearningTechnical Screen
• Compare Tensor Parallelism and Pipeline ParallelismMedium

  Compare transformer tensor and pipeline parallelism through parameter partitioning, collectives, activation transfers, microbatch bubbles, memory, and inference latency.

  Mistral AISoftware EngineerMachine LearningTechnical Screen
• Explain Adam, AdamW, and Decoupled Weight DecayMedium

  Understand Adam moment estimates and bias correction, then compare gradient-based L2 regularization with AdamW’s decoupled weight decay and learning-rate interaction.

  Mistral AISoftware EngineerMachine LearningTechnical Screen
• Explain Mixture of Experts and Expert ParallelismMedium

  Explain sparse mixture-of-experts routing and expert parallelism, including token dispatch, weighted combination, load balancing, overflow policies, and communication costs.

  Mistral AISoftware EngineerMachine LearningTechnical Screen
• Explain Transformer Internals and Implement Top-p DecodingHard

  Assess understanding of causal transformer decoding mechanics, including attention and KV caching, paired with an implementation of nucleus (top-p) sampling with temperature scaling.

  Mistral AIMachine Learning EngineerMachine LearningTechnical Screen
• Explain the Forward and Reverse Processes of Diffusion ModelsMedium

  Explain Gaussian diffusion corruption, timestep-conditioned denoising, noise-prediction training, iterative reverse sampling, latent representations, and guidance tradeoffs.

  Mistral AISoftware EngineerMachine LearningTechnical Screen
• Optimize Nearest-Center Assignment Without Materializing NxKxD DistancesMedium

  This problem tests the ability to optimize memory usage in distance computations by leveraging algebraic expansion to avoid creating an intermediate (N, K, D) tensor.

  Mistral AIMachine Learning EngineerMachine LearningOnsite
• Validate Features That Predict User RetentionMedium

  Learn to evaluate retention features with leakage-safe time windows, cohort validation, baselines, ablations, and calibrated predictions.

  MixpanelData ScientistMachine LearningTechnical Screen
• Answer basic probability and statistics questionsMedium

  This problem tests your conceptual understanding of probability distributions, statistical bias in data collection, and the relationship between covariance and linear correlation.

  Morgan StanleyData ScientistMachine LearningOnline Assessment
• Compare deep learning framework trendsMedium

  Compare deep learning framework trends evaluates core ML concepts, assumptions, math intuition, training/evaluation trade-offs, and practical failure modes in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  NVIDIASoftware EngineerMachine LearningTechnical Screen