Amazon AI Engineer (Mid-Level) Interview Preparation Guide
Amazon's AI Engineer interview process for mid-level candidates consists of a recruiter screening call, two technical phone screens covering coding and ML fundamentals, and four on-site rounds including advanced coding, deep learning and neural networks, ML system design, and behavioral assessment. The process evaluates technical depth in AI/ML domains (neural networks, deep learning, NLP, computer vision, generative AI), system design thinking for scalable ML infrastructure, coding proficiency, and cultural alignment with Amazon's 14 Leadership Principles.
Interview Rounds
Recruiter Screening
What to Expect
Initial conversation with Amazon recruiter to assess your background, experience, and fit for the AI Engineer role. This round covers your career trajectory, current and past AI/ML projects, technical skills, and alignment with Amazon's culture and Leadership Principles. The recruiter will provide information about the role, team structure, and company, and will assess your motivation and availability.
Tips & Advice
Be conversational and authentic. Highlight your most significant AI/ML projects with quantifiable impact (e.g., improved model accuracy by 18%, reduced inference latency by 45%, deployed system serving 1M+ daily requests). Have clear examples demonstrating Amazon Leadership Principles: Ownership (full project responsibility), Customer Obsession (user-centric thinking), Invent and Simplify, and Insist on Highest Standards. Ask thoughtful questions about the team's AI focus, current challenges, and growth opportunities. Use specific metrics from your work rather than generic statements. Discuss your familiarity with deep learning frameworks (PyTorch, TensorFlow) and relevant AI domains from the job description (NLP, computer vision, generative AI). If you have limited AWS experience, express genuine interest in learning cloud-based ML platforms quickly.
Focus Topics
Experience with AWS AI Services and Cloud ML Platforms
Discuss any hands-on experience with AWS SageMaker, Lambda for inference, EC2 GPU instances, S3 for data storage, or other AWS ML services. If limited, mention specific interest in learning Amazon's ML infrastructure and willingness to adopt cloud-first practices.
Practice Interview
Study Questions
Specific Interest in AI at Amazon and Relevant Domains
Research Amazon's AI initiatives (Alexa, recommendation systems, AWS AI services, robotics). Express specific interest in how Amazon advances AI technology. Reference relevant AI domains mentioned in the job description (NLP, computer vision, generative AI) that align with Amazon's business.
Practice Interview
Study Questions
Amazon Leadership Principles Alignment
Prepare concrete examples showing embodiment of Amazon's key Leadership Principles: Ownership (taking full responsibility for complex problems), Customer Obsession (user-centric decision making), Invent and Simplify, Insist on Highest Standards, Learn and Be Curious, and Earn Trust. Use the STAR method for structuring these stories.
Practice Interview
Study Questions
Deep Learning and AI Framework Proficiency
Clearly articulate hands-on experience with PyTorch and/or TensorFlow. Describe specific projects where you implemented neural network architectures (CNNs, RNNs, Transformers). Mention familiarity with specialized tools for NLP (Hugging Face Transformers), computer vision, or generative models.
Practice Interview
Study Questions
End-to-End AI/ML Project Experience with Quantifiable Impact
Discuss 2-3 significant AI projects where you owned the full lifecycle: problem definition, data collection, model architecture design, training, evaluation, and deployment. Focus on projects involving neural networks, deep learning, NLP, computer vision, or generative AI. Quantify impact using metrics (accuracy improvements, latency gains, business impact).
Practice Interview
Study Questions
Technical Phone Screen 1: Coding and Algorithms
What to Expect
This 60-minute technical phone interview focuses on core problem-solving and coding skills using an online code editor (CoderPad, HackerRank, or similar). You'll solve 1-2 algorithmic problems on data structures and algorithms. The interviewer assesses your ability to clarify ambiguous problems, think through multiple approaches, write clean production-quality code, test thoroughly, and optimize for time and space complexity while communicating your reasoning clearly.
Tips & Advice
Follow Amazon's 5-step structured approach: (1) Clarify—ask questions about problem scope, input/output format, constraints, and explore edge cases before coding; (2) Plan—discuss 2-3 potential solution approaches, compare their complexity, and select the best one; (3) Implement—write clean, readable code using meaningful variable/function names and helpful comments; (4) Test—start with simple examples, then systematically test edge and corner cases; (5) Optimize—calculate time/space complexity, identify bottlenecks, and discuss optimizations. For mid-level candidates, interviewers expect you to reach optimal or near-optimal solutions with minimal hints. Write code that's maintainable and production-ready, not just functional. Practice in the same online environment you'll use during the interview. At mid-level, demonstrate faster problem-solving and deeper optimization thinking than junior levels, but interviewers still provide some guidance if you're on the right track.
Focus Topics
Complexity Analysis and Optimization Strategies
Proficiency calculating Big O time and space complexity. Identify optimization opportunities: memoization to avoid recomputation, early termination, reducing space usage. Compare trade-offs between approaches (time vs. space, simplicity vs. performance). Know when further optimization has diminishing returns.
Practice Interview
Study Questions
Production-Ready Code Quality
Write clean, readable code with descriptive naming conventions. Include helpful comments explaining non-obvious logic. Handle edge cases gracefully. Structure code logically so others can easily understand and modify it. Avoid overly clever or hard-to-follow approaches even if they're technically correct.
Practice Interview
Study Questions
Core Data Structures Mastery
Deep proficiency with arrays, linked lists, stacks, queues, trees (binary trees, BSTs, balanced trees like AVL), graphs, heaps (min/max), and hash tables. For each structure, understand insertion/deletion/search complexity, practical use cases, and when each is optimal. Know how to implement basic operations and combinations of structures.
Practice Interview
Study Questions
Structured Problem-Solving Methodology
Systematic approach: clarify the problem with questions, discuss multiple solution approaches, pick the best one, implement cleanly, test thoroughly including edge cases, calculate complexity, and optimize. Communicate each step to the interviewer; don't code silently.
Practice Interview
Study Questions
Core Algorithms and Problem-Solving Techniques
Fluency in sorting algorithms (quicksort, mergesort, heapsort), searching (binary search), dynamic programming (memoization and tabulation), recursion, graph algorithms (DFS, BFS, Dijkstra's, topological sort), and string manipulation. Understand time/space complexity of each and their practical applications.
Practice Interview
Study Questions
Technical Phone Screen 2: ML Fundamentals and Neural Networks
What to Expect
This 60-minute technical phone interview assesses understanding of machine learning fundamentals and deep learning concepts through discussion-based questions. Topics include supervised/unsupervised learning algorithms, neural network architectures, activation functions, loss functions, optimization techniques, model evaluation metrics, overfitting/underfitting, regularization strategies, and practical deep learning experience. You may sketch diagrams or pseudocode on a shared whiteboard to illustrate concepts.
Tips & Advice
Ground all explanations in real project examples. When discussing ML concepts, cite specific instances: 'In my NLP project at [company], I implemented a Transformer-based model because...' or 'When building the recommendation system, we faced overfitting and solved it by...'. Use metrics and data to support points (e.g., 'Achieved 92% accuracy on test set after hyperparameter tuning'). For deep learning questions, demonstrate hands-on experience with frameworks and specific architectural choices. If uncertain about a concept, be honest but connect to related knowledge you do have. For mid-level candidates, interviewers expect fluency in neural networks, practical understanding of common architectures (CNNs, RNNs, Transformers), and ability to discuss trade-offs between approaches. Don't memorize answers; instead, understand concepts deeply enough to explain them naturally.
Focus Topics
Model Evaluation, Regularization, and Preventing Overfitting
Comprehensive understanding of evaluation metrics: accuracy, precision, recall, F1-score, ROC-AUC, confusion matrices for classification; MAE, MSE, RMSE, R² for regression. Understanding bias-variance tradeoff. Regularization techniques: L1/L2 regularization, dropout, batch normalization, early stopping, data augmentation. Know when each technique applies and practical implementation.
Practice Interview
Study Questions
Computer Vision and Image-Based Deep Learning
Understanding of convolutional neural networks and their application to computer vision tasks: image classification, object detection, semantic segmentation. Knowledge of popular architectures (ResNet, EfficientNet, YOLO, etc.). Practical considerations: image preprocessing, augmentation, handling different image sizes, deployment of vision models.
Practice Interview
Study Questions
Model Training, Optimization, and Hyperparameter Tuning
Understanding of training dynamics: learning rate effects, batch size selection, momentum and adaptive methods (Adam, RMSprop). Techniques for efficient training: mixed precision, gradient checkpointing, learning rate scheduling. Hyperparameter tuning approaches: grid search, random search, Bayesian optimization. Strategies for managing training instability and convergence issues.
Practice Interview
Study Questions
Natural Language Processing and Generative AI Models
Hands-on knowledge of NLP applications: text classification, sequence-to-sequence models, language models, attention mechanisms. Understanding of transformer-based language models and pre-trained models (BERT, GPT, etc.). Familiarity with fine-tuning techniques, embeddings, and practical NLP challenges. Knowledge of generative AI: how LLMs work, prompt engineering basics, applications of generative models.
Practice Interview
Study Questions
Deep Learning Architectures: CNNs, RNNs, Transformers
Detailed knowledge of Convolutional Neural Networks (for vision tasks), Recurrent Neural Networks and LSTMs (for sequences), and Transformer architecture (for NLP/vision). For each, understand: architectural design, why it suits specific problem types, practical considerations, and common variants (ResNet, GRU, etc.). Know when to use each architecture.
Practice Interview
Study Questions
Neural Network Fundamentals and Backpropagation
Deep understanding of feedforward neural networks, how backpropagation works, and how gradients flow through layers. Understand activation functions (ReLU, sigmoid, tanh) and their properties. Know loss functions (cross-entropy, MSE) and why specific ones match problem types. Understand how neural networks learn through gradient descent and optimization.
Practice Interview
Study Questions
On-Site Interview Round 1: Advanced Coding and Algorithm Optimization
What to Expect
This 60-minute on-site technical interview assesses coding ability under in-person pressure, similar to but potentially slightly more challenging than the phone screen. You'll solve 1-2 algorithmic problems on a whiteboard or laptop. The interviewer evaluates your problem-solving approach, code quality, ability to optimize, handling of edge cases, and communication skills. This round ensures you can think clearly and code well in a formal setting and potentially with slightly harder problem variants.
Tips & Advice
Treat this as a comprehensive technical assessment. On whiteboard: write clearly and large; use visual representations where helpful. Walk through your logic verbally while coding—avoid silent coding. Start by asking clarifying questions to demonstrate analytical thinking. Present multiple solution approaches before committing to one, discussing trade-offs. For mid-level candidates, interviewers expect faster problem recognition and fewer hints than junior levels. Be comfortable with silence while thinking; don't fill it with unnecessary chatter. Test your solution thoroughly against simple and edge cases before declaring completion. If stuck, explain your thought process and work collaboratively to resolve issues. Practice writing code on whiteboards beforehand; it's cognitively different from IDE coding. Time yourself during practice to ensure you can solve and test a problem in ~45 minutes, leaving time for optimization discussion.
Focus Topics
Production-Grade Code Under Pressure
Write clean, readable code even while solving complex problems. Handle edge cases gracefully. Structure code logically. Demonstrate that coding speed doesn't compromise quality.
Practice Interview
Study Questions
Time and Space Complexity Mastery
Proficiency calculating Big O complexity for all solutions, including recursive algorithms. Comparative analysis of approaches. Optimization strategies targeting specific complexity bottlenecks. Understanding practical implications of theoretical complexity.
Practice Interview
Study Questions
Advanced Data Structures and Specialized Applications
Deeper knowledge of advanced structures: segment trees, Fenwick trees, tries, union-find, and specialized trees. Understand when each is necessary and how to apply them to optimization problems. Know combinations of structures for complex solutions.
Practice Interview
Study Questions
Algorithmic Pattern Recognition and Optimization
Ability to quickly recognize problem patterns (dynamic programming, greedy, graph problem, string manipulation, etc.). Techniques like memoization, tabulation, bit manipulation, two-pointers, sliding windows, and divide-and-conquer. Intuition for which technique applies to each problem type.
Practice Interview
Study Questions
On-Site Interview Round 2: Deep Learning, Neural Networks, and AI Model Development
What to Expect
This 60-minute on-site technical interview dives deep into your expertise in deep learning, neural network architecture design, and practical AI model development. You'll discuss specific projects you've built, explain architecture decisions, handle real challenges (vanishing gradients, training instability, inference optimization), and demonstrate hands-on familiarity with frameworks and modern AI techniques. Questions may involve sketching architectures, discussing training strategies, or solving practical ML engineering problems specific to neural networks and AI systems.
Tips & Advice
Come prepared with detailed technical discussions of 2-3 deep learning projects you've personally built or significantly contributed to. Walk through: problem definition, data collection, architecture design decisions, training approach, challenges encountered, and solutions implemented. Use specific technical details—don't generalize. For example: 'I implemented a Transformer-based NLP model using PyTorch. I used a 12-layer architecture with 768 hidden dimensions because of computational constraints. I tackled vanishing gradients by using layer normalization and gradient clipping.' Discuss practical challenges: computational constraints, data limitations, inference latency requirements, and how you addressed them. Use concrete numbers: training time, model size, inference latency, accuracy metrics. At mid-level, you're not expected to be a researcher but should be deeply familiar with modern architectures and able to implement or fine-tune them. If asked about cutting-edge topics (diffusion models, advanced transformer variants), acknowledge your knowledge level honestly and discuss how you'd learn new techniques. Interviewers appreciate candor more than false expertise.
Focus Topics
Model Optimization, Debugging, and Performance Tuning
Debugging neural network training: diagnosing vanishing/exploding gradients, poor convergence, overfitting. Optimization strategies beyond basic hyperparameter tuning. Inference optimization: quantization, pruning, distillation, model compression. Profiling and identifying bottlenecks in training and inference pipelines. Understanding trade-offs: accuracy vs. latency, model size vs. performance.
Practice Interview
Study Questions
Computer Vision and Convolutional Neural Networks
Understanding CNN architectures and their application to vision tasks: image classification, object detection, semantic segmentation. Knowledge of popular models and when to use each. Practical experience with image preprocessing, augmentation, handling different input sizes. Deployment considerations for vision models (latency, model size).
Practice Interview
Study Questions
Deep Learning Frameworks and Practical Implementation
Production-level proficiency with PyTorch and/or TensorFlow. Understanding custom layers, custom training loops, checkpointing, and reproducibility. Knowledge of specialized frameworks (Hugging Face Transformers for NLP, torchvision for vision). Practical deployment considerations: model serialization, version control, reproducibility.
Practice Interview
Study Questions
Generative AI and Modern AI Applications
Understanding how generative AI models work: diffusion models, generative adversarial networks, autoregressive language models. Knowledge of practical applications: text generation, image generation, code generation. Understanding of fine-tuning and prompt engineering for generative models. Awareness of emerging techniques and recent research in generative AI.
Practice Interview
Study Questions
Deep Learning Architecture Design and Selection
Deep understanding of convolutional neural networks (ResNet, EfficientNet, DenseNet variants), recurrent architectures (LSTM, GRU, bidirectional), and Transformers with attention mechanisms. For each: architectural innovations, design rationale, practical trade-offs (depth vs. width, parameter count vs. accuracy), and when to use each. Experience implementing or fine-tuning these architectures.
Practice Interview
Study Questions
Training Large-Scale Neural Networks at Scale
Practical techniques for efficient large-scale training: distributed training (data parallelism, model parallelism), mixed-precision training, gradient checkpointing, learning rate scheduling, batch normalization. Understanding GPU/hardware optimization for training. Strategies for managing training failures, instability, divergence, and convergence issues.
Practice Interview
Study Questions
Natural Language Processing and Large Language Models
The job description explicitly requires NLP expertise. Understand transformer-based language models (BERT, GPT architectures). Experience with fine-tuning pre-trained models for specific tasks. Knowledge of tokenization, embeddings, sequence-to-sequence models. Understanding of practical NLP challenges: context length limitations, training efficiency, inference optimization for LLMs. Familiarity with prompt engineering and generative AI applications.
Practice Interview
Study Questions
On-Site Interview Round 3: ML System Design and Scalable AI Architecture
What to Expect
This 75-minute on-site interview assesses your ability to design end-to-end, production-grade machine learning systems at scale. You'll receive a high-level problem (e.g., 'Design a recommendation system for Amazon products', 'Design a real-time fraud detection system', 'Design a computer vision system for quality assurance') and must propose a complete, architecturally sound solution. The interviewer evaluates your ability to scope the problem, define success metrics, design data pipelines, select model architectures, design serving infrastructure, plan monitoring, and handle real-world constraints like latency, cost, and scalability.
Tips & Advice
Start by clarifying the problem thoroughly: What are we optimizing for (accuracy, latency, cost)? What's the scale (users, requests/second, data volume)? What are hard constraints? Then structure your solution: (1) Define success metrics (business and technical), (2) Outline the ML pipeline (data collection → preprocessing → feature engineering → training → serving → monitoring), (3) Discuss data infrastructure and scalability, (4) Propose model architecture(s) with justification, (5) Design training strategy and evaluation, (6) Design serving infrastructure with latency/throughput considerations, (7) Plan monitoring and iteration. For mid-level candidates, interviewers expect practical details: How will you handle model updates? What's your latency budget? How do you A/B test? How do you monitor for data drift or model degradation? Consider AWS services (SageMaker, Lambda, S3, DynamoDB, Kinesis). Present multiple options with trade-offs. Don't assume unlimited resources; discuss realistic constraints and cost considerations. Be prepared to deep-dive into any component if asked.
Focus Topics
Trade-off Analysis and Constraint-Driven Design
Systematically analyzing trade-offs: accuracy vs. latency, complexity vs. maintainability, cost vs. performance, timeliness vs. data quality. Making principled decisions based on stated constraints. Proposing multiple solution approaches when appropriate and justifying final choices. Understanding that different use cases have different optimization targets.
Practice Interview
Study Questions
AWS Services for ML Systems (SageMaker, Lambda, S3, DynamoDB, Kinesis)
Practical knowledge of AWS services: SageMaker for training and hosting, Lambda for serverless inference, S3 for data storage, DynamoDB for low-latency feature lookups, Kinesis for real-time streaming, Glue for ETL. Understanding when to use each service, their limitations, and costs. Designing systems that leverage AWS managed services effectively.
Practice Interview
Study Questions
Metrics, Monitoring, and Production ML Observability
Defining both business metrics (revenue, engagement) and ML metrics (accuracy, latency, throughput). Designing monitoring systems for model performance, data quality, system health, and business metrics. Alerting strategies for production incidents. Detecting model degradation, data drift, and feature staleness. Planning evaluation strategies: offline evaluation, online testing, A/B testing frameworks.
Practice Interview
Study Questions
Model Serving Infrastructure and Inference Optimization
Designing inference infrastructure: batch serving vs. real-time serving. Model serving frameworks (TensorFlow Serving, TorchServe, SageMaker, Lambda for serverless). Optimizing for latency: caching strategies, model compression (quantization, distillation, pruning), batching. Handling model updates and versioning. A/B testing framework for model improvements.
Practice Interview
Study Questions
Model Architecture Selection and Training Strategy for Scale
Selecting appropriate model architectures based on problem requirements (accuracy, latency, complexity). Distributed training strategies for large models. Training pipelines: experiment tracking, hyperparameter tuning at scale, managing training failures. Planning model update cadence and retraining strategies. Monitoring model performance during and after training.
Practice Interview
Study Questions
End-to-End ML System Design and Architecture
Comprehensive understanding of the complete ML pipeline: problem framing and success metrics, data collection and labeling, data preprocessing, feature engineering, model training, model evaluation, deployment, and monitoring. Ability to map requirements to architectural components and infrastructure. Understanding how each component impacts end-to-end system performance.
Practice Interview
Study Questions
Scalable Data Pipeline and Feature Engineering Infrastructure
Designing data pipelines handling large-scale data ingestion, processing, and storage. Trade-offs between batch vs. real-time processing. Feature engineering at scale using frameworks (Spark, Beam, Flink) or AWS services (Kinesis, Glue). Designing feature stores for efficient feature management and serving. Data quality and monitoring in pipelines.
Practice Interview
Study Questions
On-Site Interview Round 4: Behavioral and Amazon Leadership Principles
What to Expect
This 45-60 minute on-site interview assesses behavioral fit, leadership qualities, and deep alignment with Amazon's 14 Leadership Principles. The interviewer asks about past experiences, challenges you've overcome, collaboration style, decision-making approach, and career growth trajectory. For mid-level candidates, the interviewer particularly evaluates your readiness to take on larger projects, mentor junior colleagues, and potential for growth into senior roles.
Tips & Advice
Prepare detailed STAR (Situation, Task, Action, Result) stories demonstrating each major Amazon Leadership Principle, with emphasis on Ownership, Customer Obsession, Invent and Simplify, Are Right a Lot, and Insist on Highest Standards. For mid-level candidates, focus stories on: (1) Taking complete ownership of complex projects end-to-end, overcoming obstacles, and delivering results; (2) Mentoring or developing junior colleagues, demonstrating growth mindset; (3) Making good decisions with incomplete information and learning from mistakes; (4) Driving improvements or innovation that had measurable impact; (5) Collaborating effectively across teams to achieve ambitious goals. Use specific metrics and data in your results. Tell stories authentically—don't memorize answers. If discussing failures, emphasize what you learned and how it changed your approach. Be candid about challenges. Practice telling stories concisely (2-3 minutes each). Research Amazon's recent AI initiatives and tie your experiences to how you'd contribute similarly. Ask thoughtful questions about the team and role to show genuine interest.
Focus Topics
Collaboration, Communication, and Cross-Functional Impact
Discuss successful collaborations with other teams (data, infrastructure, product), managing conflicting priorities, or achieving better outcomes through teamwork. Demonstrate clear communication of technical concepts to non-technical stakeholders. Show how you've built trust and influence across teams.
Practice Interview
Study Questions
Decision-Making with Incomplete Information
Provide examples of making important decisions without complete information. Explain how you gathered available data, consulted relevant people, made the decision, and evaluated outcomes. Show good judgment, comfort with ambiguity, and learning from results.
Practice Interview
Study Questions
Mentoring and Developing Others
At mid-level, you're expected to mentor junior colleagues. Share examples of helping others grow, providing constructive feedback, or leading projects that developed someone else's skills. Show investment in others' success, not just your own.
Practice Interview
Study Questions
Learning from Failure and Growth Mindset
Discuss a significant project setback or personal mistake. Explain what went wrong, how you recovered, what you learned, and how it changed your approach. Show curiosity about learning new things, staying current with AI/ML advances, and actively seeking feedback to improve.
Practice Interview
Study Questions
Amazon Leadership Principle: Ownership
Demonstrate taking full, personal responsibility for projects from conception through completion and beyond. Show you identify problems proactively, take initiative beyond immediate scope, and are accountable for both successes and failures. Mid-level focus: owning medium-to-large scope projects end-to-end and driving them to completion despite obstacles.
Practice Interview
Study Questions
Driving Results and Execution Excellence
Share stories about projects you delivered successfully, particularly when facing obstacles, tight deadlines, or competing priorities. Show how you balanced quality with delivery speed. Use metrics to quantify impact (e.g., delivered feature 2 weeks early, improved system latency by 40%, deployed model serving 10M daily predictions).
Practice Interview
Study Questions
Amazon Leadership Principle: Customer Obsession
Show decisions and work are centered on customer/user needs and impact. Provide examples of incorporating customer feedback, understanding pain points, or making decisions benefiting users even if it meant more work. In technical context: designing systems with user experience in mind, not just technical optimization.
Practice Interview
Study Questions
Frequently Asked AI Engineer Interview Questions
Sample Answer
Sample Answer
import numpy as np
def nms(boxes, scores, iou_threshold=0.5):
# boxes: (N,4) x1,y1,x2,y2 ; scores: (N,)
if len(boxes) == 0:
return np.array([], dtype=int)
idxs = np.argsort(scores)[::-1] # sort by score desc
keep = []
while idxs.size:
i = idxs[0]
keep.append(i)
# compute IoU between boxes[i] and boxes[idxs[1:]] vectorized
xx1 = np.maximum(boxes[i,0], boxes[idxs[1:],0])
yy1 = np.maximum(boxes[i,1], boxes[idxs[1:],1])
xx2 = np.minimum(boxes[i,2], boxes[idxs[1:],2])
yy2 = np.minimum(boxes[i,3], boxes[idxs[1:],3])
w = np.maximum(0.0, xx2 - xx1)
h = np.maximum(0.0, yy2 - yy1)
inter = w * h
area_i = (boxes[i,2]-boxes[i,0]) * (boxes[i,3]-boxes[i,1])
areas = (boxes[idxs[1:],2]-boxes[idxs[1:],0]) * (boxes[idxs[1:],3]-boxes[idxs[1:],1])
iou = inter / (area_i + areas - inter)
# keep those with IoU <= threshold
remaining = np.where(iou <= iou_threshold)[0]
idxs = idxs[remaining + 1] # +1 to account for skipped idxs[0]
return np.array(keep, dtype=int)Sample Answer
Sample Answer
#include <bits/stdc++.h>
using namespace std;
struct SegTree {
int n;
vector<long long> t;
SegTree(int _n=0){ init(_n); }
void init(int _n){
n = _n;
t.assign(4*n+5, 0);
}
void build(int v, int tl, int tr, const vector<long long>& a){
if(tl==tr){ t[v] = a[tl]; return; }
int tm=(tl+tr)/2;
build(v*2, tl, tm, a);
build(v*2+1, tm+1, tr, a);
t[v] = t[v*2] + t[v*2+1];
}
long long range_sum(int v, int tl, int tr, int l, int r){
if(l>r) return 0;
if(l<=tl && tr<=r) return t[v];
int tm=(tl+tr)/2;
return range_sum(v*2, tl, tm, l, min(r,tm))
+ range_sum(v*2+1, tm+1, tr, max(l,tm+1), r);
}
void point_update(int v, int tl, int tr, int pos, long long val){
if(tl==tr){ t[v] = val; return; }
int tm=(tl+tr)/2;
if(pos<=tm) point_update(v*2, tl, tm, pos, val);
else point_update(v*2+1, tm+1, tr, pos, val);
t[v] = t[v*2] + t[v*2+1];
}
// convenient wrappers (0-indexed array)
void build(const vector<long long>& a){ build(1,0,n-1,a); }
long long range_sum(int l,int r){ return range_sum(1,0,n-1,l,r); }
void point_update(int idx,long long val){ point_update(1,0,n-1,idx,val); }
};
int main(){
ios::sync_with_stdio(false);
cin.tie(nullptr);
int N,Q;
if(!(cin>>N>>Q)) return 0;
vector<long long> a(N);
for(int i=0;i<N;i++) cin>>a[i];
SegTree st(N);
st.build(a);
while(Q--){
string type; cin>>type;
if(type=="sum"){
int l,r; cin>>l>>r; // 0-indexed expected
cout<<st.range_sum(l,r)<<"\n";
} else if(type=="set"){
int idx; long long val; cin>>idx>>val;
st.point_update(idx,val);
}
}
return 0;
}Sample Answer
def max_subarray_k(nums, k):
"""
Return max sum of any contiguous subarray of size k.
Time: O(n), Space: O(1) additional.
"""
n = len(nums)
if k <= 0 or k > n:
raise ValueError("k must be between 1 and len(nums)")
# initial window sum
current_sum = sum(nums[:k])
max_sum = current_sum
# slide the window: for i from 1 to n-k, window is nums[i:i+k]
for i in range(1, n - k + 1):
# subtract the element leaving (nums[i-1]) and add entering (nums[i+k-1])
current_sum += nums[i + k - 1] - nums[i - 1]
if current_sum > max_sum:
max_sum = current_sum
return max_sumSample Answer
Sample Answer
Sample Answer
from pydantic import BaseModel, ValidationError
import logging, prometheus_client
logger = logging.getLogger("ingest")
dq_bad = prometheus_client.Counter("dq_invalid_rows", "Invalid rows")
dq_total = prometheus_client.Counter("dq_total_rows", "Total rows")
class Row(BaseModel):
user_id: int
event_type: str
ts: int
def ingest_row(raw, mode="fail-fast"):
dq_total.inc()
try:
row = Row.parse_obj(raw)
return row
except ValidationError as e:
dq_bad.inc()
logger.warning("Row validation failed: %s; sample=%s", e.errors(), raw)
if mode == "fail-fast":
logger.error("Fail-fast mode: aborting ingestion")
raise
return None # permissive: drop or mark rowSample Answer
class Node:
def __init__(self, key, priority):
self.key = key
self.priority = priority
self.prev = self.next = None
class BucketPQ:
def __init__(self, min_p, max_p):
self.min_p = min_p
self.max_p = max_p
self.R = max_p - min_p + 1
self.buckets = [None] * self.R # heads of doubly-linked lists
self.pos = {} # key -> (priority, node)
self.cur_min = 0
def _idx(self, p): return p - self.min_p
def push(self, key, priority):
idx = self._idx(priority)
node = Node(key, priority)
head = self.buckets[idx]
node.next = head
if head: head.prev = node
self.buckets[idx] = node
self.pos[key] = (priority, node)
if idx < self.cur_min: self.cur_min = idx
def pop_min(self):
for i in range(self.cur_min, self.R):
head = self.buckets[i]
if head:
# remove head
self.buckets[i] = head.next
if head.next: head.next.prev = None
key = head.key
del self.pos[key]
self.cur_min = i
return key, head.priority
raise KeyError("empty")
def decrease_key(self, key, new_p):
if key not in self.pos: raise KeyError
old_p, node = self.pos[key]
if new_p >= old_p: return # only support decrease efficiently
# unlink node
if node.prev: node.prev.next = node.next
else: self.buckets[self._idx(old_p)] = node.next
if node.next: node.next.prev = node.prev
# insert into new bucket
node.prev = None; node.next = self.buckets[self._idx(new_p)]
if node.next: node.next.prev = node
self.buckets[self._idx(new_p)] = node
node.priority = new_p
self.pos[key] = (new_p, node)
if self._idx(new_p) < self.cur_min: self.cur_min = self._idx(new_p)Sample Answer
Search Results
Amazon Machine Learning Engineer Interview (questions, process ...
Complete guide to Amazon machine learning engineer interviews. Learn more about the role, the interview process, practice with example questions, ...
Mastering Amazon's Machine Learning Interview: A Comprehensive ...
This comprehensive guide will walk you through everything you need to know to ace the Amazon ML interview, covering the interview process, technical and ...
Applied Scientist Interview Prep - Amazon.jobs
The applied scientist interview is designed to identify candidates who have the technical proficiency, behavioral skills, and cultural fit required to help us ...
Your complete guide to the Amazon interview process
This guide will walk you through each step, from application to interview, highlighting what makes Amazon's approach different and how to prepare effectively.
Amazon Machine Learning Engineer (MLE) Interview Guide
In this guide, we'll explore Amazon's AI/ML opportunities, their unique interview loop, and tips to strengthen your application for this highly competitive role ...
Amazon Artificial Intelligence Engineer Interview Process
The Amazon AI interview process consists of three main stages. The process is usually similar for most tech interviews at Amazon.
This interview preparation guide was generated using AI-powered research from the sources listed above. While we strive for accuracy, we recommend verifying critical information from official company sources.
Want to create your own tailored preparation guide using our deep research?
Get Started for FreeInterview-Ready Courses
Visual-first, interactive, structured learning paths