Amazon AI Engineer Interview Preparation Guide - Staff Level
Amazon's AI Engineer interview process for Staff level candidates is comprehensive and rigorous, spanning 9 rounds over approximately 4-6 weeks. The process begins with recruiter screening, progresses through two technical phone screens focusing on coding and ML fundamentals, and culminates in six intensive onsite rounds covering system design, deep learning, specialized AI domains, large-scale systems, behavioral assessment, and research-driven innovation. Amazon evaluates candidates on technical depth, system design expertise, AWS proficiency, specialized AI knowledge (NLP, Computer Vision, Generative AI), and strong alignment with Amazon Leadership Principles.
Interview Rounds
Recruiter Screening
What to Expect
The initial recruiter screening is a phone conversation with Amazon's HR department. This is an informal discussion to assess your background, experience level, skills alignment with the Staff-level AI Engineer role, current compensation, and motivation for joining Amazon. The recruiter will also discuss your availability, relocation willingness, and expectations. They will validate that your experience meets the Staff-level bar (12+ years) and assess cultural fit based on Amazon's Leadership Principles.
Tips & Advice
Be concise but specific about your experience with AI systems, deep learning, and large-scale ML projects. Highlight your Staff-level impact: leading architecture decisions, mentoring senior engineers, driving innovation, and working across multiple teams. Mention specific AI domains you're expert in (NLP, Vision, Generative AI). Research Amazon's AI initiatives and express genuine interest. Align your career goals with Amazon's business needs and culture. Be clear about compensation expectations but remain flexible.
Focus Topics
Specialized AI Domain Expertise
Discuss your deep expertise in NLP, Computer Vision, Generative AI, or other specialized AI domains relevant to the role.
Practice Interview
Study Questions
Leadership & Mentoring Experience
Share examples of mentoring senior engineers, driving architectural decisions, and leading cross-functional AI initiatives.
Practice Interview
Study Questions
Motivation for Amazon & AI Role
Explain why you're interested in the AI Engineer role at Amazon, what appeals to you about the company, and how this role aligns with your career goals.
Practice Interview
Study Questions
Professional Background & AI Expertise
Articulate your 12+ years of experience with emphasis on AI engineering, deep learning systems, and increasing scope of responsibility. Highlight major AI projects, technologies, and domains.
Practice Interview
Study Questions
Technical Phone Screen Round 1 - Coding & Algorithms
What to Expect
This technical phone screen focuses on your problem-solving and algorithmic skills through live coding challenges. You'll be asked to solve one or two coding problems involving data structures and algorithms, similar to LeetCode Medium level problems. The interviewer will observe your approach: how you clarify the problem, plan your solution, implement clean code, test edge cases, and optimize for time and space complexity. You'll code in a shared online editor or document, and the interviewer may ask follow-up questions to understand your analytical thinking. This round evaluates your foundational coding ability and problem-solving methodology.
Tips & Advice
Follow Amazon's step-by-step approach: Clarify (ask questions and explore edge cases), Plan (discuss potential approaches), Implement (write clean, production-ready code with meaningful variable names), Test (test with simple and edge cases), Optimize (calculate complexity and discuss optimizations). Present multiple possible solutions when applicable, explaining your reasoning for choosing the optimal one. Write organized, testable code with comments as you go. Prepare to explain your Time and Space Complexity analysis. At Staff level, interviewers expect confident, efficient problem-solving with attention to code quality.
Focus Topics
Code Quality & Production-Ready Implementation
Write well-organized, readable code with meaningful function and variable names, comments, proper error handling, and structure that could be tested.
Practice Interview
Study Questions
Amazon Coding Approach (Clarify-Plan-Implement-Test-Optimize)
Master Amazon's structured problem-solving approach: clarifying requirements, discussing potential approaches, implementing clean code, testing edge cases, and optimizing solutions.
Practice Interview
Study Questions
Core Data Structures (Arrays, Hash Maps, Trees, Graphs)
Mastery of fundamental data structures, their properties, time/space complexities, and appropriate use cases. Include hash maps, binary trees, binary search trees, graphs, and advanced structures.
Practice Interview
Study Questions
Algorithm Complexity Analysis (Time & Space)
Deep understanding of Big O notation, calculating time and space complexity, identifying optimization opportunities, and explaining trade-offs between approaches.
Practice Interview
Study Questions
LeetCode Medium Problems - Pattern Recognition
Practice 30-50 LeetCode Medium problems focusing on patterns: two-pointers, sliding window, depth-first search, breadth-first search, dynamic programming, and binary search.
Practice Interview
Study Questions
Technical Phone Screen Round 2 - ML Fundamentals & System Design
What to Expect
This technical phone screen dives deeper into machine learning knowledge and system design thinking. You'll discuss core ML concepts including regularization, model drift, hyperparameter tuning, cross-validation, and evaluation metrics. The interviewer may ask you to design an end-to-end ML pipeline, discuss how to structure machine learning workflows using AWS services like SageMaker, and explain model deployment strategies. You might also face coding challenges related to ML, such as data preprocessing, feature engineering, or implementing ML algorithms. Additionally, behavioral questions aligned with Amazon's Leadership Principles (like 'Dive Deep' or 'Bias for Action') are introduced here. The focus is on your ability to think systematically about ML systems and your practical experience with production ML.
Tips & Advice
Demonstrate deep understanding of ML fundamentals with concrete examples from your work. When discussing system design, explicitly state your assumptions and check if they align with the interviewer's expectations. Present multiple possible solutions for system design problems, explaining trade-offs. For ML-specific coding (data preprocessing, feature engineering), explain your approach and consider edge cases. Be prepared to discuss how you'd implement solutions using AWS services, SageMaker, and other tools. When answering behavioral questions, use the STAR method with specific metrics and measurable outcomes. At Staff level, show that you've owned complex ML systems end-to-end and can mentor others.
Focus Topics
ML-Specific Coding (Data Preprocessing & Feature Engineering)
Write production-quality code for data preprocessing, feature engineering, handling missing data, dealing with imbalanced datasets, and feature scaling. Understand code organization for ML pipelines.
Practice Interview
Study Questions
Amazon Leadership Principles Behavioral Questions
Prepare behavioral answers aligned with Amazon's Leadership Principles: Dive Deep, Bias for Action, Customer Obsession, Ownership, etc. Use STAR method with specific examples and metrics.
Practice Interview
Study Questions
Model Deployment & Production Considerations
Strategies for model serving, inference optimization, versioning, A/B testing, canary deployments, monitoring model performance in production, and handling model staleness.
Practice Interview
Study Questions
AWS & SageMaker for ML at Scale
Proficiency with AWS services: SageMaker for training and hosting, data versioning, experiment tracking, distributed training, model registry, and deployment options. Understand how to scale ML workflows on AWS.
Practice Interview
Study Questions
Core ML Concepts (Regularization, Model Drift, Hyperparameter Tuning)
Deep understanding of regularization techniques (L1, L2, dropout), handling model drift in production, hyperparameter tuning strategies, cross-validation approaches, and common evaluation metrics for different problem types.
Practice Interview
Study Questions
End-to-End ML System Design & Architecture
Design complete ML pipelines from data ingestion to model deployment. Include data pipeline architecture, feature engineering, model training, validation, monitoring, and serving strategies.
Practice Interview
Study Questions
Onsite Round 1 - System Design for Large-Scale AI Systems
What to Expect
This intensive onsite round focuses on your ability to design large-scale AI systems that operate in production environments. You'll be given a scenario such as: 'Design a system for real-time NLP-based content recommendation' or 'Build an architecture for training and serving computer vision models at scale.' You'll need to clarify requirements, make reasonable assumptions about scale and constraints, and design a complete system covering data pipeline, model architecture, training infrastructure, serving layer, monitoring, and scalability. The interviewer will probe your understanding of trade-offs: latency vs. accuracy, cost vs. performance, real-time vs. batch processing. Expect deep questions about distributed systems, infrastructure decisions, and how your design would evolve as the system scales.
Tips & Advice
Start by clarifying requirements and understanding the scale (data volume, QPS, latency requirements, accuracy targets). Explicitly state your assumptions about scale, infrastructure, and business constraints. Design for distributed, scalable systems from the start. Consider the full pipeline: data collection, preprocessing, feature engineering, model training, hyperparameter tuning, model validation, serving, and monitoring. Discuss trade-offs explicitly (accuracy vs. latency, cost vs. performance). When questioned, be ready to justify your architectural choices and discuss how the system would handle growth. For Staff level, show that you've designed and managed large-scale systems and can mentor others through such designs. Draw diagrams to illustrate your architecture.
Focus Topics
Data Pipeline Architecture & Feature Management
Design scalable data pipelines for processing large volumes of data. Address data versioning, feature stores, handling data quality issues, and feature engineering at scale.
Practice Interview
Study Questions
Monitoring, Logging & System Observability for ML
Design monitoring strategies for ML systems including model performance monitoring, data drift detection, inference latency tracking, and alerting. Discuss observability best practices.
Practice Interview
Study Questions
End-to-End ML Pipeline Architecture
Design complete ML pipelines including data ingestion, preprocessing, feature engineering, model training, validation, evaluation, and deployment. Address scalability, fault tolerance, and monitoring.
Practice Interview
Study Questions
Distributed Systems for ML (Scalability & Parallelism)
Understand distributed training, data parallelism, model parallelism, distributed inference, and handling distributed state. Design systems that scale horizontally.
Practice Interview
Study Questions
Model Serving & Inference Optimization at Scale
Design inference systems for low-latency serving. Discuss batching, model quantization, caching, load balancing, and scaling inference infrastructure. Handle real-time vs. batch tradeoffs.
Practice Interview
Study Questions
Onsite Round 2 - Deep Learning & Neural Network Architecture
What to Expect
This round evaluates your deep expertise in deep learning and neural network design. You'll discuss various neural network architectures including Convolutional Neural Networks (CNNs) for computer vision, Recurrent Neural Networks (RNNs) for sequence processing, Transformers and attention mechanisms for NLP, and hybrid architectures. The interviewer will probe your understanding of how these architectures work, when to use each, and how to design custom architectures for specific problems. You might be asked to implement a simplified neural network layer, explain backpropagation, discuss optimization techniques (SGD, Adam, etc.), and address practical concerns like initialization, regularization, and batch normalization. Questions about transfer learning, fine-tuning pre-trained models, and computational efficiency on GPUs are likely.
Tips & Advice
Demonstrate thorough understanding of neural network internals: forward pass, backpropagation, gradient descent, and various optimization algorithms. Be able to explain why certain architectures work better for specific tasks (CNNs for vision due to convolution and pooling, Transformers for NLP due to attention). Discuss trade-offs: model size vs. accuracy, training time vs. inference speed, computational requirements vs. performance. Be prepared to implement or pseudocode neural network components. Understand practical considerations: learning rate schedules, batch size effects, handling overfitting, and computational requirements on GPUs. For Staff level, show that you've designed and trained large models, understand scaling challenges, and can guide others in architecture selection.
Focus Topics
GPU Optimization & Computational Efficiency
Understand GPU memory constraints, model parallelism, data parallelism, mixed precision training, quantization, and optimization techniques for training and inference on GPUs.
Practice Interview
Study Questions
Transfer Learning & Fine-Tuning Pre-trained Models
Strategies for leveraging pre-trained models (BERT, GPT, Vision Transformers, ResNet, etc.), fine-tuning approaches, domain adaptation, and when to train from scratch vs. transfer.
Practice Interview
Study Questions
Neural Network Architectures (CNNs, RNNs, Transformers)
Deep understanding of Convolutional Neural Networks for vision, Recurrent Neural Networks for sequences, Transformer architectures and attention mechanisms for NLP. Know when each is appropriate.
Practice Interview
Study Questions
Training Optimization & Backpropagation
Deep understanding of backpropagation, gradient descent variants (SGD, Momentum, Adam, RMSprop), learning rate schedules, batch normalization, and handling vanishing/exploding gradients.
Practice Interview
Study Questions
Deep Learning Frameworks (TensorFlow, PyTorch)
Proficiency in TensorFlow and PyTorch. Know how to build, train, and optimize models using these frameworks. Understand computational graphs, autograd, and distributed training support.
Practice Interview
Study Questions
Onsite Round 3 - Specialized AI Domain (NLP, Computer Vision, or Generative AI)
What to Expect
This round focuses on your deep expertise in a specialized AI domain: Natural Language Processing (NLP), Computer Vision, or Generative AI. Your interviewer will assess mastery in your area of specialization. For NLP: expect questions about tokenization, embeddings (Word2Vec, GloVe, contextual embeddings), language models, machine translation, question-answering, and text classification architectures. For Computer Vision: expect questions about image classification, object detection, segmentation architectures, and visual understanding. For Generative AI: expect deep questions about generative models (diffusion models, GANs, autoregressive models), large language models, prompt engineering, fine-tuning large models, and evaluation methods for generative outputs. The interviewer will expect you to discuss state-of-the-art techniques, recent research, and practical implementation considerations for your chosen domain.
Tips & Advice
Be prepared to discuss your specialized domain in depth. If NLP: understand modern transformer-based models (BERT, GPT, T5), discuss tokenization strategies, explain how embeddings work, and share experiences fine-tuning large models. If Computer Vision: explain convolutional architectures, discuss different task types (classification, detection, segmentation), and share experience with large-scale vision datasets. If Generative AI: understand diffusion models, attention mechanisms in generative models, discuss prompt engineering and in-context learning, and explain evaluation challenges for generative outputs. Be ready to discuss limitations, failure modes, and practical challenges of your domain. Reference recent papers and research. For Staff level, show that you've led cutting-edge projects in your domain and published or contributed to industry-leading work.
Focus Topics
Domain-Specific Techniques & Recent Research
Stay current with cutting-edge research in your domain. Discuss recent papers, techniques, and architectural innovations. Be ready to discuss limitations and future directions.
Practice Interview
Study Questions
Production Challenges & Practical Implementation
Discuss real-world challenges when deploying specialized AI systems: handling domain-specific data, addressing edge cases, maintaining model quality, and scaling systems in production.
Practice Interview
Study Questions
Computer Vision Architectures & Applications (Object Detection, Segmentation, Classification)
For Computer Vision specialization: CNN architectures (ResNet, EfficientNet, Vision Transformers), object detection frameworks (YOLO, Faster R-CNN), semantic and instance segmentation, and visual understanding.
Practice Interview
Study Questions
Natural Language Processing Fundamentals (Tokenization, Embeddings, Language Models)
For NLP specialization: tokenization approaches, word embeddings (Word2Vec, GloVe), contextual embeddings (ELMo, BERT), transformer-based language models, and attention mechanisms in NLP.
Practice Interview
Study Questions
Generative AI & Large Language Models (Diffusion, GANs, Autoregressive Models)
For Generative AI specialization: generative model architectures (diffusion models, GANs, autoregressive models), large language model training, prompt engineering, fine-tuning strategies, and generation quality evaluation.
Practice Interview
Study Questions
Onsite Round 4 - Large-Scale ML Systems, Training & Optimization
What to Expect
This round evaluates your experience building and optimizing AI systems at massive scale. You'll discuss distributed training approaches for very large models: data parallelism, model parallelism, pipeline parallelism, and mixture-of-experts approaches. The interviewer will ask about handling training failures, checkpointing strategies, and data efficiency. You'll discuss large-scale data processing using tools like Apache Spark or Ray, and strategies for data movement and caching. Topics include optimization techniques for training massive models (gradient compression, gradient accumulation, mixed precision training), resource utilization, cost optimization, and training time reduction. You might face questions about training cutting-edge large models (like LLMs) in practice, handling dataset scale, convergence issues at scale, and infrastructure orchestration.
Tips & Advice
Demonstrate hands-on experience with large-scale training. Discuss specific challenges you've solved: training models with billions of parameters, handling distributed failures, optimizing communication overhead, managing GPU/TPU utilization, and reducing training costs. Understand various distributed training strategies and when to apply each. Be prepared to discuss infrastructure: how models are distributed across devices, how gradients are synchronized, and how to handle stragglers. Talk about practical optimizations you've implemented and their impact. For Staff level, share strategic thinking about resource allocation, trade-offs between speed and cost, and how to approach new large-scale challenges. Be specific about numbers: model size, training time, resource utilization, and cost savings achieved.
Focus Topics
Handling Training at Extreme Scale (Fault Tolerance, Convergence)
Challenges of training at extreme scale: handling distributed failures, checkpointing and recovery strategies, convergence issues at scale, and debugging distributed training.
Practice Interview
Study Questions
Resource Management & Cost Optimization
Strategies for optimal resource utilization, cost management in cloud training, spot instances, preemption handling, and trade-offs between training time and cost.
Practice Interview
Study Questions
Distributed Training (Data Parallelism, Model Parallelism, Pipeline Parallelism)
Deep understanding of distributed training approaches: data parallelism for embarrassingly parallel training, model parallelism for large models, pipeline parallelism for efficient resource usage, and hybrid approaches.
Practice Interview
Study Questions
Large-Scale Data Processing & Management
Handling massive datasets for training: data ingestion, preprocessing at scale, storage strategies, data loading pipelines, and addressing data movement bottlenecks.
Practice Interview
Study Questions
Training Optimization & Efficiency (Gradient Compression, Mixed Precision, Accumulation)
Techniques to accelerate training and reduce computational requirements: gradient compression, mixed precision training, gradient accumulation, loss scaling, and communication optimization.
Practice Interview
Study Questions
Onsite Round 5 - Amazon Leadership Principles & Behavioral Competencies
What to Expect
This dedicated behavioral round assesses your alignment with Amazon's Leadership Principles and your ability to lead and influence at the Staff level. You'll be asked multiple behavioral questions designed to evaluate principles such as: Customer Obsession (how you drive customer value), Ownership (how you take responsibility for outcomes), Invent and Simplify (how you innovate), Bias for Action (your decision-making pace), Learn and Be Curious (your commitment to growth), and others. At Staff level, questions will probe how you've mentored and developed team members, influenced technical strategy across multiple teams, navigated ambiguous situations, made difficult trade-off decisions, and driven large-scale initiatives. The interviewer will assess your leadership presence, communication clarity, emotional intelligence, and ability to advocate for your team.
Tips & Advice
Prepare 6-8 detailed stories that demonstrate Amazon's Leadership Principles, especially those most relevant to Staff-level impact. Use the STAR method (Situation, Task, Action, Result) consistently. For each story, prepare follow-up details in case the interviewer asks probing questions. Quantify your results with specific metrics and business impact. For Staff-level questions, focus on: mentoring and developing senior engineers, driving architectural decisions across multiple teams, owning large initiatives end-to-end, making decisions with incomplete information, and handling interpersonal conflicts. Be specific about scale: how many people did you influence, what was the business impact, how did you measure success. Show that you don't just execute—you drive strategy and develop others. Speak about failures as learning opportunities.
Focus Topics
Handling Failures & Learning
Share honest stories about failures, what you learned, and how you've applied those lessons. Show growth mindset and accountability for setbacks.
Practice Interview
Study Questions
Decision-Making with Ambiguity & Incomplete Information
Discuss how you've made important decisions when information was incomplete, how you've validated assumptions, and how you've moved forward despite uncertainty.
Practice Interview
Study Questions
Mentorship & Team Development
Discuss how you've mentored junior and senior engineers, developed technical capabilities in your team, and created growth opportunities for team members. Include specific examples of people you've developed.
Practice Interview
Study Questions
Amazon Leadership Principle: Ownership
Share examples of taking full ownership of outcomes, including failures. Show how you've driven accountability across teams and ensured results happened.
Practice Interview
Study Questions
Amazon Leadership Principle: Customer Obsession
Demonstrate how you've driven customer value, understood customer needs, and made decisions prioritizing customer benefit. Show understanding that internal customers (engineers, teams) are also customers.
Practice Interview
Study Questions
Technical Leadership & Influencing Across Teams
Share examples of driving technical strategy across multiple teams, making architectural decisions that affected many people, and influencing without direct authority.
Practice Interview
Study Questions
Onsite Round 6 - Bar Raiser / Advanced AI Research & Innovation
What to Expect
This final onsite round features Amazon's 'Bar Raiser'—a senior person not on your immediate team whose role is to ensure hiring standards are maintained. This round assesses whether you will raise the bar at Amazon and typically combines technical depth with behavioral evaluation. Expect advanced technical questions that test the boundaries of your knowledge: novel AI architectures you haven't implemented, cutting-edge research challenges, or unusual combinations of ML techniques. The Bar Raiser will probe how you approach unsolved problems, your research mindset, and ability to innovate. They'll also assess your communication ability to explain complex concepts clearly and your ability to challenge assumptions constructively. This round often serves as the final decision point, so interviewers are evaluating whether you'll be a positive influence on the team and organization long-term.
Tips & Advice
Be prepared for questions at the cutting edge of AI research. Familiarize yourself with recent papers from top conferences (NeurIPS, ICML, ICLR, ACL, CVPR, EMNLP). Show curiosity about unsolved problems in your domain. When asked about novel topics, demonstrate your thinking process: how you'd approach learning about new areas, how you'd design experiments, and how you'd validate hypotheses. Be ready to discuss trade-offs and limitations of current approaches. At Staff level, show that you contribute to advancing the field, not just applying known techniques. Communicate complex ideas clearly—this is a marker of true expertise. Be genuine about what you don't know and express interest in learning. If the question challenges your previous answer, engage intellectually and adjust your thinking rather than defending your initial response.
Focus Topics
Research Methodology & Experimental Validation
Approach to novel research challenges: hypothesis formation, experimental design, validation strategies, and how to learn from experiments. Show rigor in your scientific thinking.
Practice Interview
Study Questions
Intellectual Humility & Growth Mindset
Demonstrate openness to new ideas, willingness to challenge your own assumptions, and genuine interest in learning from others. Show comfort with not knowing everything.
Practice Interview
Study Questions
Communication of Complex AI Concepts
Ability to explain complex AI concepts clearly to different audiences. Show that you can break down sophisticated ideas into understandable components.
Practice Interview
Study Questions
Advanced AI System Architecture & Design
Design novel AI system architectures for complex, unfamiliar problems. Show ability to reason about systems you haven't built before and break down novel challenges.
Practice Interview
Study Questions
Cutting-Edge AI Research & State-of-the-Art Techniques
Deep familiarity with recent research in AI and your specialized domain. Understand novel architectures, training methods, and evaluation approaches published in top-tier venues.
Practice Interview
Study Questions
Frequently Asked AI Engineer Interview Questions
Sample Answer
import re, itertools
from collections import defaultdict
from concurrent.futures import ProcessPoolExecutor
from rapidfuzz import fuzz
def normalize(s):
return re.sub(r'\W+','',s.lower() or '')
def blocking_keys(rec):
keys=[]
email=rec.get('email') or ''
local,_,domain=email.partition('@')
keys.append('email_local:'+normalize(local))
keys.append('email_domain:'+normalize(domain))
name=normalize(rec.get('name',''))
keys.append('name_key:'+name.split()[-1][:6]+(name[:1] if name else ''))
return keys
def block_and_emit(records):
inv=defaultdict(list)
for i,rec in records:
for k in blocking_keys(rec):
inv[k].append((i,rec))
pairs=[]
for bucket in inv.values():
# only compare within bucket; for large buckets sample or shard further
for (i,a),(j,b) in itertools.combinations(bucket,2):
pairs.append((i,a,j,b))
return pairs
def score_pair(a,b):
# fast short-circuit exact email
if a.get('email') and a.get('email')==b.get('email'):
return 0.99
name_score=fuzz.token_set_ratio(a.get('name',''), b.get('name',''))/100
email_local_a=normalize((a.get('email') or '').split('@')[0])
email_local_b=normalize((b.get('email') or '').split('@')[0])
email_score = 1.0 if email_local_a and email_local_a==email_local_b else 0.0
return 0.6*name_score + 0.4*email_score
def process_block(records):
pairs=block_and_emit(records)
return [(i,j,score_pair(a,b)) for i,a,j,b in pairs if score_pair(a,b)>0.75]
# Parallel execution: partition dataset into chunks and run process_block on each worker
with ProcessPoolExecutor(max_workers=8) as ex:
futures=[ex.submit(process_block, chunk) for chunk in partitioned_data]
results=[f.result() for f in futures]
# merge results, build union-find to cluster duplicatesSample Answer
from typing import Any, Dict, Protocol, Sequence
import random
class Transform(Protocol):
def apply(self, example: Any, rng: random.Random = None) -> Any: ...
def get_config(self) -> Dict: ...
@classmethod
def from_config(cls, config: Dict): ...
class Pipeline:
def __init__(self, transforms: Sequence[Transform]):
self.transforms = tuple(transforms) # immutable
def apply(self, example: Any, seed: int = None) -> Any:
rng = random.Random(seed)
out = example
for t in self.transforms:
out = t.apply(out, rng)
return out
def get_config(self):
return {"pipeline": [ {"class": t.__class__.__name__, "config": t.get_config()} for t in self.transforms ]}
@classmethod
def from_config(cls, cfg, registry):
return cls([ registry[name].from_config(c) for name,c in ... ])Sample Answer
Sample Answer
Sample Answer
Sample Answer
Sample Answer
import time
import logging
from typing import Optional, Type, Any
class Timer:
"""Context manager that times a block and logs duration. Does not swallow exceptions."""
def __init__(self, name: str, logger: Optional[logging.Logger] = None) -> None:
self.name = name
self.logger = logger or logging.getLogger(__name__)
self._start: float = 0.0
def __enter__(self) -> "Timer":
self._start = time.perf_counter()
return self
def __exit__(self, exc_type: Optional[Type[BaseException]], exc: Optional[BaseException], tb: Optional[Any]) -> bool:
elapsed = time.perf_counter() - self._start
self.logger.info("%s finished in %.6f seconds", self.name, elapsed)
# Returning False (or None) ensures exceptions are not suppressed.
return Falselogging.basicConfig(level=logging.INFO)
with Timer("heavy_task"):
do_work()Sample Answer
from datetime import datetime, timedelta
import heapq
from typing import List, Tuple
ISO = "%Y-%m-%dT%H:%M:%S"
def parse_iso(s):
return datetime.strptime(s, ISO)
def iso(dt):
return dt.strftime(ISO)
def tumbling_counts_with_watermark(events: List[Tuple[str, str]],
window_size_sec: int,
allowed_lateness_sec: int):
"""
events: list of (event_ts_iso, event_id) in arbitrary order
window_size_sec: tumbling window length in seconds
allowed_lateness_sec: allowed lateness in seconds for watermark
Returns list of emitted windows: (window_start_iso, window_end_iso, count, emit_watermark_iso)
"""
# state
counts = {} # window_start_dt -> count
heap = [] # (window_end_dt, window_start_dt)
max_event_time = None
results = []
def window_bounds(event_time):
offset = (event_time - datetime.min).total_seconds() // window_size_sec
start = datetime.min + timedelta(seconds=offset * window_size_sec)
end = start + timedelta(seconds=window_size_sec)
return start, end
def advance_watermark_and_emit():
nonlocal max_event_time
if max_event_time is None:
return
watermark = max_event_time - timedelta(seconds=allowed_lateness_sec)
# emit all windows with end <= watermark
while heap and heap[0][0] <= watermark:
end, start = heapq.heappop(heap)
cnt = counts.pop(start, 0)
results.append((iso(start), iso(end), cnt, iso(watermark)))
return
for ts_iso, _eid in events:
et = parse_iso(ts_iso)
if (max_event_time is None) or (et > max_event_time):
max_event_time = et
start, end = window_bounds(et)
# If window already emitted (end <= current watermark), treat as late and drop
current_watermark = max_event_time - timedelta(seconds=allowed_lateness_sec)
if end <= current_watermark:
# late event; drop or handle separately
continue
# count and push to heap if first seen
if start not in counts:
counts[start] = 0
heapq.heappush(heap, (end, start))
counts[start] += 1
# try emitting windows now that watermark may have advanced
advance_watermark_and_emit()
# final watermark advancement (e.g., on stream end, set max_event_time to infinity or emit remaining if policy permits)
# here we assume on stream close we advance watermark to +inf so remaining windows are emitted
if counts:
# emit all remaining windows
remaining = sorted([(end, start) for (end, start) in heap])
final_wm = max_event_time + timedelta(days=365*100) # effectively infinite
for end, start in remaining:
cnt = counts.pop(start, 0)
results.append((iso(start), iso(end), cnt, iso(final_wm)))
return resultsSample 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
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