Microsoft Senior AI Engineer Interview Preparation Guide
Microsoft's Senior AI Engineer interview process is a rigorous, multi-stage evaluation designed to assess deep technical expertise in artificial intelligence, machine learning systems, neural network architecture, and generative AI. The process begins with a recruiter screen, progresses through an online technical assessment, and culminates in comprehensive onsite rounds covering data structures and algorithms, machine learning fundamentals, deep learning architectures, specialized AI systems (NLP/Computer Vision/Generative AI), AI system design, and behavioral/cultural fit. The entire process spans 4-6 weeks and evaluates both technical mastery and alignment with Microsoft's leadership principles of Create Clarity, Generate Energy, and Deliver Success.
Interview Rounds
Recruiter Screening
What to Expect
Your initial interaction with Microsoft's recruitment team. The recruiter will review your background, motivation for applying to Microsoft, and assess your fit with the AI Engineer role. They'll discuss your experience with AI/ML projects, technical skills (particularly in deep learning, NLP, computer vision), your understanding of the role responsibilities, and your knowledge of Microsoft's AI initiatives. This is also an opportunity to learn about the interview process timeline and ask clarifying questions about the role and team. The recruiter will assess your communication skills, enthusiasm, and cultural alignment with Microsoft's values.
Tips & Advice
Research Microsoft's AI teams, products, and recent AI initiatives before the call. Prepare clear, concise stories about your AI/ML projects that demonstrate impact. Focus on explaining complex technical concepts to a non-technical recruiter clearly. Highlight 2-3 key achievements in AI systems, deep learning, or NLP that showcase your senior-level expertise. Express genuine interest in Microsoft's specific AI challenges and their Azure ML platform. Prepare thoughtful questions about the team, role responsibilities, and growth opportunities. Be authentic about your motivation to join Microsoft and your interest in the specific position.
Focus Topics
Communication of Technical Concepts
Demonstrate ability to explain complex AI/ML concepts (neural networks, generative models, NLP systems) in a way that's accessible to non-technical stakeholders.
Practice Interview
Study Questions
Understanding of the Role & Responsibilities
Show awareness of what the AI Engineer role involves: designing AI architectures, implementing neural networks, developing NLP/vision systems, building generative AI applications, and staying current with AI research.
Practice Interview
Study Questions
Motivation for Microsoft & AI Focus
Articulate why you're interested in Microsoft specifically, what appeals to you about their AI initiatives, and how the AI Engineer role aligns with your career goals.
Practice Interview
Study Questions
Career Trajectory & AI Specialization
Discuss your progression in AI/ML, key projects involving neural networks or deep learning, and how you've grown as an AI engineer. Emphasize your transition from intermediate to senior-level work.
Practice Interview
Study Questions
Online Technical Assessment
What to Expect
A 60-minute timed assessment that tests your foundational skills in coding, data structures, algorithms, and machine learning fundamentals. You'll typically face 2-3 coding challenges (medium difficulty, similar to LeetCode Medium problems) focusing on data structure manipulation and algorithmic problem-solving. Additionally, you'll encounter questions testing your knowledge of machine learning concepts, basic neural network theory, and Python proficiency. This round gauges whether you can handle the core technical demands of senior-level AI engineering and your ability to solve problems efficiently under time constraints.
Tips & Advice
Practice LeetCode and GeeksforGeeks problems focusing on data structures (arrays, linked lists, trees, graphs) and algorithms (searching, sorting, dynamic programming) at medium difficulty. Time yourself to ensure you can complete problems within 15-20 minutes each. For each problem, start with a clear brute-force approach, then optimize for time and space complexity. Use clean, readable Python code with meaningful variable names. For ML questions, review fundamental concepts thoroughly: supervised vs. unsupervised learning, classification vs. regression, model evaluation metrics (accuracy, precision, recall, F1-score), overfitting/underfitting, feature scaling, regularization techniques, and basic neural network concepts. Understand the intuition behind common algorithms rather than memorizing. When stuck on a problem, think out loud about your approach rather than sitting silently.
Focus Topics
Neural Network Basics
Understanding of feedforward neural networks, activation functions (ReLU, sigmoid, tanh), backpropagation, loss functions, and basic training concepts. Ability to explain these intuitively.
Practice Interview
Study Questions
Machine Learning Fundamentals
Core ML concepts: supervised vs. unsupervised learning, classification vs. regression, model evaluation metrics, overfitting/underfitting, cross-validation, regularization, feature scaling, and feature engineering basics.
Practice Interview
Study Questions
Python Coding Proficiency
Fluent Python implementation including list comprehensions, efficient string manipulation, built-in functions, and writing clean, readable code that follows Python conventions.
Practice Interview
Study Questions
Data Structures Fundamentals
Mastery of arrays, linked lists, stacks, queues, trees (binary search trees, balanced trees), graphs, and hash tables. Understanding when to use each structure and their time/space complexity.
Practice Interview
Study Questions
Algorithmic Problem Solving
Proficiency with searching algorithms (binary search), sorting (quicksort, mergesort), dynamic programming, graph traversal (BFS, DFS), and other common algorithmic paradigms.
Practice Interview
Study Questions
Technical Interview Round 1: Data Structures & Algorithms
What to Expect
An in-depth technical interview focusing on your data structures and algorithms problem-solving abilities. You'll face 1-2 complex coding problems that require not just solving them, but optimizing them for efficiency. An interviewer will assess how you approach problems, communicate your thinking, ask clarifying questions, and iteratively improve your solutions. For senior-level candidates, expect problems that require sophisticated algorithmic techniques like dynamic programming, graph algorithms, or complex data structure design. You'll be expected to analyze time and space complexity rigorously and discuss trade-offs in your approach. The interviewer also assesses your ability to write production-quality code and handle edge cases thoroughly.
Tips & Advice
For senior-level AI engineers, DSA remains critical. Practice hard-difficulty LeetCode problems, particularly those involving dynamic programming, advanced graph algorithms, and complex data structure manipulation. When approaching a problem: (1) clarify requirements and constraints with the interviewer, (2) discuss your approach before coding, (3) start with a clear brute-force solution, (4) optimize step-by-step while narrating your thinking, (5) write clean code with proper error handling, (6) thoroughly test edge cases. Discuss time and space complexity trade-offs explicitly. If you get stuck, articulate what you're struggling with—interviewers appreciate this more than silence. For senior level, also think about how your algorithm scales and potential real-world applications. Practice explaining your optimization reasoning, not just the final solution.
Focus Topics
Code Quality & Edge Case Handling
Writing production-quality code with proper variable naming, modular structure, comprehensive error handling, and thorough testing of edge cases including empty inputs, single elements, and boundary conditions.
Practice Interview
Study Questions
Complexity Analysis & Trade-offs
Rigorous analysis of time and space complexity using Big-O notation, understanding practical implications of complexity, and articulating trade-offs between different approaches.
Practice Interview
Study Questions
Binary Search & Optimization Techniques
Mastery of binary search variants, two-pointer techniques, sliding window, and other optimization patterns for reducing algorithm complexity from brute force.
Practice Interview
Study Questions
Graph Algorithms & Complexity
Deep understanding of graph representations, DFS/BFS, topological sorting, shortest path algorithms (Dijkstra, Bellman-Ford), minimum spanning trees, and detecting cycles. Analyzing time/space complexity for each.
Practice Interview
Study Questions
Advanced Dynamic Programming
Complex DP problems requiring state definition, memoization vs. tabulation, space optimization, and multi-dimensional DP. Understanding when and how to apply DP to unfamiliar problems.
Practice Interview
Study Questions
Technical Interview Round 2: Machine Learning & Neural Networks
What to Expect
A comprehensive technical interview focusing on your deep understanding of machine learning theory, neural network architectures, and practical ML engineering. The interviewer will ask questions spanning supervised learning models (linear regression, logistic regression, decision trees, random forests, SVMs), unsupervised learning (clustering, dimensionality reduction), model evaluation and selection, and neural network fundamentals. You'll face questions about feature engineering, handling class imbalance, regularization techniques, hyperparameter tuning, and cross-validation. For a senior AI engineer, expect deeper questions about why certain techniques work, when to apply them, and how to diagnose and fix common ML problems. You may need to write code to implement or explain algorithms, and discuss real-world scenarios where you've applied these concepts at scale.
Tips & Advice
Review ML fundamentals thoroughly but focus on deep understanding rather than memorization. For each concept, understand: the intuition, mathematical foundation, when to use it, limitations, and how to implement it. Be prepared to explain why certain algorithms work and diagnose when models fail. Practice explaining complex concepts clearly—ability to communicate is crucial. Review common ML pitfalls: data leakage, imbalanced datasets, feature scaling importance, overfitting indicators. For senior level, discuss how you'd approach ML problems in production: monitoring, retraining, handling data drift, and scaling. Have specific examples from your experience ready: projects where you tuned models, handled challenging datasets, improved model performance. Understand trade-offs (accuracy vs. interpretability, bias vs. variance, speed vs. accuracy). Be ready to code simple implementations (logistic regression, decision tree basics, or gradient descent). For each question, start by clarifying what the interviewer is asking, discuss your approach, and provide concrete examples.
Focus Topics
Feature Engineering & Data Preprocessing
Techniques for handling missing data, feature scaling/normalization, categorical encoding, feature selection, dimensionality reduction (PCA), handling imbalanced datasets, and detecting/handling outliers.
Practice Interview
Study Questions
Regularization & Overfitting Prevention
Deep understanding of L1/L2 regularization, dropout, early stopping, ensemble methods, and other techniques to prevent overfitting. When to apply each and why they work.
Practice Interview
Study Questions
Model Evaluation, Selection & Diagnostics
Mastery of model evaluation metrics (accuracy, precision, recall, F1-score, ROC-AUC, confusion matrix), cross-validation strategies, hyperparameter tuning, and diagnosing overfitting/underfitting. Understanding train/validation/test splits and the implications of each.
Practice Interview
Study Questions
Backpropagation & Training Dynamics
Understanding backpropagation algorithm mechanics, gradient flow, vanishing/exploding gradients problem, learning rate impact, and training dynamics. Ability to explain and address training challenges.
Practice Interview
Study Questions
Neural Network Architecture & Components
Comprehensive knowledge of neural network layers, activation functions (ReLU, sigmoid, tanh, softmax), loss functions for different tasks, optimization algorithms (SGD, Adam, momentum), and batch normalization. Understanding how these components interact.
Practice Interview
Study Questions
Supervised Learning Models & Theory
Deep understanding of linear regression, logistic regression, decision trees, random forests, gradient boosting, SVMs, and k-NN. Know assumptions, when to use each, advantages, limitations, and how to optimize them.
Practice Interview
Study Questions
Technical Interview Round 3: Deep Learning & Advanced Architectures
What to Expect
An advanced technical interview focused specifically on deep learning, modern neural network architectures, and your ability to design and implement sophisticated AI systems. You'll discuss convolutional neural networks (CNNs) for computer vision, recurrent neural networks (RNNs), long short-term memory (LSTM) networks, attention mechanisms, and transformer architectures. The interview may cover specific applications: image classification, object detection, sequence modeling, machine translation. For senior-level candidates, expect questions about why certain architectures are chosen for specific problems, how to modify architectures for novel applications, training challenges at scale (distributed training, GPU optimization), and staying current with research. You may need to discuss recent papers, novel architectures, or explain how you'd implement a complex model. The interviewer assesses your ability to think from first principles about architecture design and your research depth.
Tips & Advice
Study foundational deep learning architectures thoroughly: CNNs (convolution, pooling, classic architectures like ResNet, VGG), RNNs, LSTMs, GRUs, and transformers (self-attention, multi-head attention, positional encoding, the transformer stack). For each architecture, understand: how it works, why it's effective for its domain, how to modify it, and its limitations. Review recent AI research papers, particularly in computer vision and NLP. Be prepared to discuss: how you'd approach adapting an architecture for a new problem, training strategies for large models, distributed training considerations, and optimization techniques. For a senior-level role, have concrete examples of complex architectures you've implemented or problems you've solved. Understand practical concerns: memory constraints, computational efficiency, inference latency. Be ready to code or pseudocode key components. Discuss transfer learning, fine-tuning pre-trained models, and when to train from scratch. For each architectural choice, articulate the trade-offs and design reasoning. Stay current with the latest developments (recent transformer variants, vision transformers, large language models).
Focus Topics
Research Depth & Architecture Innovation
Familiarity with recent research papers, novel architectures, and ability to understand architectural innovations. Capacity to think from first principles about designing new architectures for novel problems.
Practice Interview
Study Questions
Distributed Training & Optimization at Scale
Understanding distributed training approaches (data parallelism, model parallelism), mixed precision training, gradient accumulation, communication optimization, and scaling challenges for large models.
Practice Interview
Study Questions
Transfer Learning & Fine-tuning
Strategic use of pre-trained models (ImageNet, BERT, GPT), understanding when and how to fine-tune, layer freezing strategies, domain adaptation, and when to train from scratch vs. transfer.
Practice Interview
Study Questions
Convolutional Neural Networks & Computer Vision
In-depth understanding of convolution operation, filters, pooling, stride, padding, CNN architectures (LeNet, AlexNet, VGG, ResNet, Inception), and how to design networks for image classification, object detection, and segmentation tasks.
Practice Interview
Study Questions
Recurrent Neural Networks & Sequence Modeling
Detailed understanding of RNNs, LSTMs, GRUs, bidirectional RNNs, and sequence-to-sequence architectures. Understanding the vanishing gradient problem, why LSTMs solve it, and when to use each architecture for sequence tasks.
Practice Interview
Study Questions
Transformer Architectures & Attention Mechanisms
Comprehensive understanding of self-attention mechanism, multi-head attention, positional encoding, transformer encoder-decoder architecture, and modern transformer variants. Understanding how transformers revolutionized NLP and vision.
Practice Interview
Study Questions
Technical Interview Round 4: Specialized AI Systems (NLP, Computer Vision & Generative AI)
What to Expect
A specialized technical interview focusing on your expertise in developing specific AI applications: natural language processing, computer vision systems, and generative AI models. You'll discuss building end-to-end NLP applications (text classification, sentiment analysis, named entity recognition, machine translation, question answering), computer vision systems (image classification, object detection, image segmentation), and generative AI applications (text generation, image generation, language models). The interviewer will explore your understanding of domain-specific techniques, datasets commonly used, evaluation metrics for each domain, and challenges specific to these applications. For a senior AI engineer, expect discussions about architecting complex systems combining multiple components (e.g., vision + language models), handling real-world constraints (latency, compute), and deploying these systems to production. You'll likely discuss specific architectures used in your experience and design decisions you made.
Tips & Advice
Focus on depth in at least one specialized domain (ideally matching your background) while having solid knowledge of others. For NLP: understand tokenization, embeddings (Word2Vec, GloVe, subword tokenization), language models, BERT/GPT architectures, and specific tasks (classification, NER, translation). For Computer Vision: understand image representations, classic CV techniques, modern CNN architectures, detection/segmentation approaches. For Generative AI: understand diffusion models, VAEs, GANs, and large language models (LLMs), including recent advances in generative AI. Be ready to discuss: architectural choices for specific problems, common challenges (class imbalance in CV, context length limits in NLP), domain-specific evaluation metrics, and production considerations. Have concrete examples from your experience—specific projects, challenges you solved, and how you optimized for specific constraints. Discuss how you'd approach building a complex system combining multiple AI techniques. Stay current with generative AI advances (prompt engineering, fine-tuning LLMs, chain-of-thought prompting). For each technical concept, explain not just what works but why, and discuss alternatives and trade-offs.
Focus Topics
Building End-to-End AI Systems & Integration
Designing complex systems combining multiple AI techniques and components. Understanding data pipelines, model orchestration, handling model composition, and deploying multi-stage systems efficiently.
Practice Interview
Study Questions
Computer Vision Systems & Architectures
Comprehensive knowledge of image classification, object detection, semantic/instance segmentation, and pose estimation. Understanding architectures (ResNet, YOLO, Faster R-CNN, Mask R-CNN), dataset preparation, and computer vision evaluation metrics.
Practice Interview
Study Questions
Domain-Specific Evaluation & Metrics
Understanding appropriate evaluation approaches for each domain: NLP metrics (BLEU, ROUGE, perplexity), computer vision metrics (mAP, IoU, F1), and generative model evaluation (human evaluation, automated metrics, bias and fairness assessment).
Practice Interview
Study Questions
Generative AI & Large Language Models
Understanding generative models: diffusion models, VAEs, GANs, language model generation mechanisms, decoding strategies (beam search, top-k sampling, nucleus sampling), and recent advances in large language models, prompt engineering, and instruction fine-tuning.
Practice Interview
Study Questions
Natural Language Processing & Language Models
Deep understanding of NLP fundamentals: tokenization, embeddings, language models, transformers (BERT, GPT), and NLP tasks (classification, NER, machine translation, question answering, sentiment analysis). Understanding pre-trained language models and fine-tuning strategies.
Practice Interview
Study Questions
Technical Interview Round 5: AI System Design & Architecture
What to Expect
A technical interview focused on your ability to design large-scale, production-ready AI systems from first principles. You'll be presented with open-ended problems like building a recommendation system, designing an NLP pipeline for content moderation, architecting a real-time computer vision system, or building a scalable machine translation service. The interviewer expects you to ask clarifying questions, define requirements, propose a complete architecture, discuss trade-offs, address scalability and latency constraints, and consider practical concerns like monitoring and model updates. For senior-level candidates, this round separates experienced engineers from others. You're expected to think systematically: data pipeline design, model serving architecture, caching strategies, handling edge cases, observability, and deployment considerations. The interviewer assesses your architectural thinking, systems knowledge, and ability to make informed engineering trade-offs. You should discuss real examples from your experience designing complex AI systems.
Tips & Advice
Prepare for open-ended system design questions by following a structured approach: (1) Ask clarifying questions about requirements, constraints, scale, and latency; (2) Define a high-level architecture with major components; (3) Dive deep into critical components; (4) Discuss data pipeline, model training infrastructure, and serving; (5) Address scalability, latency, and reliability; (6) Discuss monitoring, updating, and A/B testing; (7) Cover edge cases and failure modes. For AI systems specifically: think about feature engineering at scale, model versioning, deployment strategies (canary, blue-green), fallback mechanisms, and how to handle model drift. Discuss trade-offs explicitly: latency vs. accuracy, computational cost vs. model performance, simplicity vs. sophistication. Have real examples from your experience: systems you designed, scaling challenges you solved, and architectural decisions. For Microsoft specifically, leverage Azure ML and cloud-based solutions where relevant. Discuss practical concerns: data freshness, model update frequency, A/B testing setup, and monitoring strategies. Be prepared to modify your design based on constraints the interviewer introduces. Draw diagrams and component interactions clearly.
Focus Topics
Model Training Infrastructure & Orchestration
Designing training pipelines: data loading, preprocessing, distributed training setup, hyperparameter optimization, experiment tracking, model versioning, and checkpointing strategies for efficient and reproducible training.
Practice Interview
Study Questions
Model Deployment & Iteration Strategies
Deployment approaches: canary deployments, A/B testing framework, shadow mode testing, rollback strategies. Model update frequency, versioning, and safely transitioning between models without service disruption.
Practice Interview
Study Questions
Scalability, Reliability & Monitoring
Addressing scalability challenges: handling increased load, distributed systems thinking, fault tolerance. Designing monitoring and alerting: model performance tracking, data drift detection, system health metrics, and incident response procedures.
Practice Interview
Study Questions
Data Pipeline & Feature Engineering at Scale
Designing scalable data pipelines, feature engineering infrastructure, feature stores, data quality monitoring, and efficient feature serving. Understanding batch vs. real-time feature processing trade-offs.
Practice Interview
Study Questions
Model Serving & Inference Optimization
Strategies for deploying models: batch vs. real-time serving, model quantization, distillation, caching, latency optimization, and serving multiple model versions. Understanding containerization and orchestration platforms.
Practice Interview
Study Questions
End-to-End AI System Architecture
Designing complete AI systems including data ingestion and pipelines, feature engineering and storage, model training infrastructure, model serving and inference, and post-serving components. Understanding interactions between all components.
Practice Interview
Study Questions
Behavioral & Hiring Manager Interview
What to Expect
The final round consists of two parts. First, a behavioral interview assessing your alignment with Microsoft's leadership principles and cultural values. You'll be asked about past experiences using the STAR method (Situation, Task, Action, Result): How you've overcome technical challenges, collaborated with teams, handled disagreements, managed complex projects, mentored others, and demonstrated growth mindset. The interviewer looks for evidence of: Create Clarity (setting goals, communicating effectively), Generate Energy (inspiring collaboration, driving momentum), and Deliver Success (achieving measurable impact). Second, you'll meet with the hiring manager to discuss team fit, specific role responsibilities, expectations, and your long-term career goals. The hiring manager assesses whether you'll thrive on their specific team, your understanding of current projects, and your motivation.
Tips & Advice
Prepare 6-8 strong STAR stories demonstrating: (1) Technical leadership and deep expertise, (2) Solving complex AI/ML problems, (3) Mentoring junior engineers, (4) Collaborating across teams, (5) Handling disagreement or difficult situations, (6) Learning from failure, (7) Creating clarity in ambiguous situations, (8) Delivering measurable impact. For each story, clearly articulate the situation, your specific actions and thinking, and quantifiable results. Practice telling stories concisely (2-3 minutes each). Align stories with Microsoft's leadership principles. Research Microsoft's current AI initiatives, products, and culture before the interview. Prepare thoughtful questions about the team, current projects, and growth opportunities—this demonstrates genuine interest. For the hiring manager call, understand the specific team's charter, ongoing projects, and challenges. Ask about the role's day-to-day responsibilities and how success is measured. Express enthusiasm aligned with specific team needs. Be authentic: discuss your career trajectory, motivation to join Microsoft, and how the role fits your goals. Close by reaffirming your interest and asking what next steps look like.
Focus Topics
Technical Mentorship & Leadership
Specific examples of mentoring junior engineers, growing their capabilities, and leveraging your expertise to elevate team technical standards. Demonstrates ability to scale impact through people.
Practice Interview
Study Questions
Growth Mindset & Learning from Failure
Sharing experiences of facing challenges, learning from setbacks, adapting approaches, and continuous improvement. Demonstrating curiosity and commitment to staying current with AI research and technologies.
Practice Interview
Study Questions
Cross-Functional Collaboration & Communication
Examples of working effectively with product managers, other engineers, researchers, and stakeholders. Communicating complex AI concepts to diverse audiences and aligning on solutions.
Practice Interview
Study Questions
Microsoft Leadership Principles & Generate Energy
Sharing examples of how you've inspired collaboration, motivated teams on challenging projects, driven momentum through obstacles, and fostered positive team dynamics while tackling difficult AI/ML problems.
Practice Interview
Study Questions
Microsoft Leadership Principles & Deliver Success
Demonstrating track record of delivering measurable impact: shipping AI/ML projects that moved business metrics, achieving performance improvements, scaling systems, and consistently executing on commitments.
Practice Interview
Study Questions
Microsoft Leadership Principles & Create Clarity
Demonstrating how you set clear goals, communicate effectively across technical and non-technical audiences, reduce ambiguity in complex AI problems, and ensure team alignment on approaches and outcomes.
Practice Interview
Study Questions
Frequently Asked AI Engineer Interview Questions
Sample Answer
Sample Answer
Sample Answer
def cherry_pickup(grid):
n = len(grid)
INF = -10**9
K = 2*(n-1)
dp = [[INF]*n for _ in range(n)] # dp for k-1
dp[0][0] = grid[0][0]
for k in range(1, K+1):
ndp = [[INF]*n for _ in range(n)]
for i1 in range(max(0,k-(n-1)), min(n-1,k)+1):
j1 = k - i1
if j1<0 or j1>=n or grid[i1][j1]==-1: continue
for i2 in range(max(0,k-(n-1)), min(n-1,k)+1):
j2 = k - i2
if j2<0 or j2>=n or grid[i2][j2]==-1: continue
best = INF
for pi1 in (i1, i1-1):
for pi2 in (i2, i2-1):
if 0<=pi1<n and 0<=pi2<n:
best = max(best, dp[pi1][pi2])
if best==INF: continue
add = grid[i1][j1]
if (i1,j1)!=(i2,j2):
add += grid[i2][j2]
ndp[i1][i2] = max(ndp[i1][i2], best + add)
dp = ndp
return max(0, dp[n-1][n-1])Sample Answer
Sample Answer
# bellman_ford.py
from math import inf
def bellman_ford(edges, n, source):
"""
edges: list of tuples (u, v, w)
n: number of vertices (0..n-1)
source: source vertex index
Returns: (dist, parent) or raises ValueError on negative cycle
"""
dist = [inf] * n
parent = [None] * n
dist[source] = 0
# Relax edges |V|-1 times
for i in range(n - 1):
updated = False
for u, v, w in edges:
if dist[u] != inf and dist[u] + w < dist[v]:
dist[v] = dist[u] + w
parent[v] = u
updated = True
if not updated:
break # early stop if no change
# One more pass to detect negative cycles
for u, v, w in edges:
if dist[u] != inf and dist[u] + w < dist[v]:
raise ValueError("Graph contains a negative-weight cycle")
return dist, parentSample Answer
Sample Answer
Sample Answer
def climb_steps(n):
"""
Return number of distinct ways to climb n steps using 1 or 2 steps.
Time: O(n), Extra Space: O(1)
"""
if n < 0:
return 0
if n == 0 or n == 1:
return 1
prev2, prev1 = 1, 1 # prev2 = dp[i-2], prev1 = dp[i-1]
for _ in range(2, n + 1):
curr = prev1 + prev2 # dp[i] = dp[i-1] + dp[i-2]
prev2, prev1 = prev1, curr
return prev1
# Sample I/O
print(climb_steps(5)) # -> 8Sample Answer
Sample Answer
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Senior Engineer's Guide to Microsoft Interviews + Questions
We interviewed dozens of Microsoft interviewers to get the inside track on their interview process, questions, and how they make hiring decisions.
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