Engineering roles · Python practice

Python Developer

Idiomatic, well-tested Python for applications and libraries.

24 problems

Python competencies

Core data structures
Functions and closures
Classes and dataclasses
Iterators and generators
Error handling
Testing

Learning path

Strings and collections
Functions and comprehensions
Classes and dataclasses
Generators and decorators
Concurrency basics

Interview scenarios

Refactor a script into a reusable module
Write a decorator that adds retries
Parse and validate configuration

Topics: Strings, Lists and tuples, Dictionaries, Functions, Classes and objects, Dataclasses, Decorators, Generators and laziness, Exceptions, Testing and test data

Libraries: collections, itertools, functools, dataclasses, typing, pytest

Recommended mix: Beginner 25% · Easy 30% · Medium 30% · Hard 10% · Expert 5%

Start a Python Developer session

Mode

Guided practiceNo timer. Hints, solutions, notes and as many runs as you like.Timed assessmentA fixed set of questions against a server-side countdown. Unsubmitted work is finalised when time runs out.Mock interviewQuestions one at a time with structured feedback on correctness, quality, efficiency and testing.Adaptive practiceQuestions chosen from your recorded weak topics, with the reason for each choice.Daily challengeOne curated question per day. Solve it to extend your streak.

Roles

Difficulty (any)

Topics (any)

Number of questions

Include questions I attempted but haven't solved Exclude questions I've already solved

DevOps Engineer

Scripting and automation around builds, deployments, configuration and logs.

28 problems

Python competencies

Python scripting
Filesystem automation
JSON and YAML processing
Log analysis
REST API automation
CI/CD automation

Learning path

Text and config parsing
Log analysis
API automation with retries
Infrastructure automation
Concurrency for automation

Interview scenarios

Summarise a deployment log
Plan log rotation
Detect configuration drift
Parse cron schedules

Topics: Files and configuration, Parsing text, JSON, Log analysis, HTTP and REST APIs, Retries and backoff, CI/CD automation, Infrastructure as code, Threads and concurrency, AWS SDK (Boto3-style), Kubernetes

Libraries: pathlib, json, PyYAML, requests, boto3, kubernetes, subprocess, concurrent.futures

Recommended mix: Beginner 15% · Easy 30% · Medium 35% · Hard 15% · Expert 5%

Start a DevOps Engineer session

Mode

Guided practiceNo timer. Hints, solutions, notes and as many runs as you like.Timed assessmentA fixed set of questions against a server-side countdown. Unsubmitted work is finalised when time runs out.Mock interviewQuestions one at a time with structured feedback on correctness, quality, efficiency and testing.Adaptive practiceQuestions chosen from your recorded weak topics, with the reason for each choice.Daily challengeOne curated question per day. Solve it to extend your streak.

Roles

Difficulty (any)

Topics (any)

Number of questions

Include questions I attempted but haven't solved Exclude questions I've already solved

AI Engineer

Production AI features — APIs around models, retrieval, evaluation and resilient integrations.

17 problems

Python competencies

Async Python
FastAPI and Pydantic
LLM API integration
Embeddings and retrieval
Model evaluation
Retries and fallback

Learning path

Vectors and similarity
Async I/O
Validation and structured output
Retrieval pipelines
Resilient model gateways

Interview scenarios

Fall back to a second model on timeout
Rank chunks for a query
Repair malformed structured output

Topics: Vectors and embeddings, asyncio, Validation and schemas, LLM integration, Retrieval-augmented generation, Model evaluation, Retries and backoff, Streaming responses, AI agents and tools, MCP and JSON-RPC

Libraries: asyncio, FastAPI, Pydantic, httpx, NumPy, pandas, LangChain, LangGraph, Amazon Bedrock

Recommended mix: Beginner 10% · Easy 25% · Medium 35% · Hard 20% · Expert 10%

Start a AI Engineer session

Mode

Guided practiceNo timer. Hints, solutions, notes and as many runs as you like.Timed assessmentA fixed set of questions against a server-side countdown. Unsubmitted work is finalised when time runs out.Mock interviewQuestions one at a time with structured feedback on correctness, quality, efficiency and testing.Adaptive practiceQuestions chosen from your recorded weak topics, with the reason for each choice.Daily challengeOne curated question per day. Solve it to extend your streak.

Roles

Difficulty (any)

Topics (any)

Number of questions

Include questions I attempted but haven't solved Exclude questions I've already solved

Generative AI Engineer

LLM applications end to end — prompts, context windows, RAG, agents and tool calling.

17 problems

Python competencies

Prompt templating
Token budgets
Chunking and retrieval
Streaming responses
Tool calling and agents
MCP servers

Learning path

Tokens and templates
Chunking
Retrieval and ranking
Streaming
Agents and MCP

Interview scenarios

Pack a conversation into a token budget
Parse a server-sent event stream
Route MCP tool calls

Topics: LLM integration, Retrieval-augmented generation, Vectors and embeddings, Streaming responses, AI agents and tools, MCP and JSON-RPC, Retries and backoff, Resilience patterns, Validation and schemas

Libraries: OpenAI SDK, Amazon Bedrock, LangChain, LangGraph, FastMCP, tiktoken, Pydantic

Recommended mix: Beginner 10% · Easy 25% · Medium 35% · Hard 20% · Expert 10%

Start a Generative AI Engineer session

Mode

Guided practiceNo timer. Hints, solutions, notes and as many runs as you like.Timed assessmentA fixed set of questions against a server-side countdown. Unsubmitted work is finalised when time runs out.Mock interviewQuestions one at a time with structured feedback on correctness, quality, efficiency and testing.Adaptive practiceQuestions chosen from your recorded weak topics, with the reason for each choice.Daily challengeOne curated question per day. Solve it to extend your streak.

Roles

Difficulty (any)

Topics (any)

Number of questions

Include questions I attempted but haven't solved Exclude questions I've already solved

Forward Deployed Engineer

Customer-specific integrations built fast and debugged in production.

15 problems

Python competencies

API integration
Data transformation
ETL pipelines
Debugging
Authentication flows
Rapid prototyping

Learning path

JSON and CSV wrangling
Paginated APIs
ETL and validation
Rate-limited sync jobs
Production troubleshooting

Interview scenarios

Sync a customer's records through a rate-limited API
Clean a messy export
Map fields between systems

Topics: JSON, ETL pipelines, HTTP and REST APIs, Pagination, Validation and schemas, SQL patterns in Python, Rate limiting, Retries and backoff, LLM integration

Libraries: requests, httpx, pandas, csv, json, SQLAlchemy, Pydantic

Recommended mix: Beginner 10% · Easy 30% · Medium 35% · Hard 20% · Expert 5%

Start a Forward Deployed Engineer session

Mode

Guided practiceNo timer. Hints, solutions, notes and as many runs as you like.Timed assessmentA fixed set of questions against a server-side countdown. Unsubmitted work is finalised when time runs out.Mock interviewQuestions one at a time with structured feedback on correctness, quality, efficiency and testing.Adaptive practiceQuestions chosen from your recorded weak topics, with the reason for each choice.Daily challengeOne curated question per day. Solve it to extend your streak.

Roles

Difficulty (any)

Topics (any)

Number of questions

Include questions I attempted but haven't solved Exclude questions I've already solved

Platform Engineer

Internal platforms — deployment ordering, capacity, rollouts and self-service tooling.

24 problems

Python competencies

Dependency graphs
Kubernetes resources
Rollout strategies
Rate limiting
Consistent hashing
Scheduling

Learning path

Resource parsing
Dependency ordering
Rollouts and canaries
Distributed primitives
Fair scheduling

Interview scenarios

Order service deployments
Simulate a rolling update
Share capacity fairly between tenants

Topics: Kubernetes, Graphs, Simulation and state machines, Rate limiting, Infrastructure as code, Hashing, Monitoring and alerting

Libraries: kubernetes, PyYAML, networkx, hashlib

Recommended mix: Beginner 10% · Easy 25% · Medium 35% · Hard 20% · Expert 10%

Start a Platform Engineer session

Mode

Guided practiceNo timer. Hints, solutions, notes and as many runs as you like.Timed assessmentA fixed set of questions against a server-side countdown. Unsubmitted work is finalised when time runs out.Mock interviewQuestions one at a time with structured feedback on correctness, quality, efficiency and testing.Adaptive practiceQuestions chosen from your recorded weak topics, with the reason for each choice.Daily challengeOne curated question per day. Solve it to extend your streak.

Roles

Difficulty (any)

Topics (any)

Number of questions

Include questions I attempted but haven't solved Exclude questions I've already solved

Site Reliability Engineer

Reliability through measurement — SLOs, alerting, incident tooling and resilience patterns.

27 problems

Python competencies

Log and metric analysis
Percentiles and error rates
Alerting logic
Retries and circuit breakers
Capacity
Failover

Learning path

Log parsing
Percentiles and histograms
Alert windows
Resilience patterns
Failover planning

Interview scenarios

Compute p95 latency per route
Alert on error-rate windows
Implement a circuit breaker

Topics: Log analysis, Monitoring and alerting, Statistics, Retries and backoff, Resilience patterns, Simulation and state machines, Kubernetes, Intervals

Libraries: prometheus_client, statistics, datetime, asyncio

Recommended mix: Beginner 10% · Easy 25% · Medium 35% · Hard 20% · Expert 10%

Start a Site Reliability Engineer session

Mode

Guided practiceNo timer. Hints, solutions, notes and as many runs as you like.Timed assessmentA fixed set of questions against a server-side countdown. Unsubmitted work is finalised when time runs out.Mock interviewQuestions one at a time with structured feedback on correctness, quality, efficiency and testing.Adaptive practiceQuestions chosen from your recorded weak topics, with the reason for each choice.Daily challengeOne curated question per day. Solve it to extend your streak.

Roles

Difficulty (any)

Topics (any)

Number of questions

Include questions I attempted but haven't solved Exclude questions I've already solved

Cloud Engineer

Cloud resources as code — tagging, cost, regions, policies and automation.

10 problems

Python competencies

Cloud SDK usage
Resource tagging
Cost analysis
Policy documents
Multi-region design

Learning path

Tags and ARNs
Paginated SDK calls
Cost and idle resources
Policies
Multi-region planning

Interview scenarios

Find idle instances
Summarise a Terraform plan
Plan regional failover

Topics: AWS SDK (Boto3-style), IAM and policies, Pagination, Infrastructure as code, Monitoring and alerting, Dictionaries

Libraries: boto3, botocore, google-cloud, azure-sdk

Recommended mix: Beginner 15% · Easy 30% · Medium 30% · Hard 20% · Expert 5%

Start a Cloud Engineer session

Mode

Guided practiceNo timer. Hints, solutions, notes and as many runs as you like.Timed assessmentA fixed set of questions against a server-side countdown. Unsubmitted work is finalised when time runs out.Mock interviewQuestions one at a time with structured feedback on correctness, quality, efficiency and testing.Adaptive practiceQuestions chosen from your recorded weak topics, with the reason for each choice.Daily challengeOne curated question per day. Solve it to extend your streak.

Roles

Difficulty (any)

Topics (any)

Number of questions

Include questions I attempted but haven't solved Exclude questions I've already solved

AWS Engineer

AWS with Boto3 — paginators, S3, EC2, CloudWatch and IAM, practised against mocked clients.

6 problems

Python competencies

Boto3 paginators
S3 operations
EC2 and CloudWatch
IAM policies
ARNs
Batching and throttling

Learning path

ARNs and tags
S3 listings
EC2 and CloudWatch
IAM policy analysis
Lifecycle automation

Interview scenarios

Plan an S3 cleanup in batches
Parse IAM policies
Size S3 prefixes

Topics: AWS SDK (Boto3-style), IAM and policies, Pagination, Retries and backoff, Security automation

Libraries: boto3, botocore, moto

Recommended mix: Beginner 15% · Easy 30% · Medium 35% · Hard 15% · Expert 5%

Start a AWS Engineer session

Mode

Guided practiceNo timer. Hints, solutions, notes and as many runs as you like.Timed assessmentA fixed set of questions against a server-side countdown. Unsubmitted work is finalised when time runs out.Mock interviewQuestions one at a time with structured feedback on correctness, quality, efficiency and testing.Adaptive practiceQuestions chosen from your recorded weak topics, with the reason for each choice.Daily challengeOne curated question per day. Solve it to extend your streak.

Roles

Difficulty (any)

Topics (any)

Number of questions

Include questions I attempted but haven't solved Exclude questions I've already solved

Backend Engineer

Services and APIs — caching, validation, idempotency, rate limiting and data integrity.

29 problems

Python competencies

API design
Validation
Caching
Rate limiting
Idempotency
Concurrency

Learning path

Request data handling
Validation
Caching
Rate limits and retries
Idempotent processing

Interview scenarios

Build an LRU cache
Process events exactly once
Validate request bodies

Topics: HTTP and REST APIs, Validation and schemas, Caching, Rate limiting, Retries and backoff, asyncio, JSON, Streaming responses

Libraries: FastAPI, Django, Flask, Pydantic, SQLAlchemy, Redis

Recommended mix: Beginner 10% · Easy 30% · Medium 35% · Hard 20% · Expert 5%

Start a Backend Engineer session

Mode

Guided practiceNo timer. Hints, solutions, notes and as many runs as you like.Timed assessmentA fixed set of questions against a server-side countdown. Unsubmitted work is finalised when time runs out.Mock interviewQuestions one at a time with structured feedback on correctness, quality, efficiency and testing.Adaptive practiceQuestions chosen from your recorded weak topics, with the reason for each choice.Daily challengeOne curated question per day. Solve it to extend your streak.

Roles

Difficulty (any)

Topics (any)

Number of questions

Include questions I attempted but haven't solved Exclude questions I've already solved

Data Engineer

Reliable data pipelines — parsing, cleaning, deduplication, windowing and aggregation.

18 problems

Python competencies

Parsing and cleaning
Aggregation
Deduplication
Window functions
Streaming data
Idempotent loads

Learning path

Records and CSV
Group-by aggregation
Deduplication
Window functions
Idempotent pipelines

Interview scenarios

Clean a JSON Lines export
Compute running totals
Keep the latest record per key

Topics: ETL pipelines, SQL patterns in Python, JSON, Parsing text, Generators and laziness, Dictionaries, Statistics

Libraries: pandas, PyArrow, Polars, csv, SQLAlchemy, Apache Spark

Recommended mix: Beginner 15% · Easy 30% · Medium 30% · Hard 20% · Expert 5%

Start a Data Engineer session

Mode

Guided practiceNo timer. Hints, solutions, notes and as many runs as you like.Timed assessmentA fixed set of questions against a server-side countdown. Unsubmitted work is finalised when time runs out.Mock interviewQuestions one at a time with structured feedback on correctness, quality, efficiency and testing.Adaptive practiceQuestions chosen from your recorded weak topics, with the reason for each choice.Daily challengeOne curated question per day. Solve it to extend your streak.

Roles

Difficulty (any)

Topics (any)

Number of questions

Include questions I attempted but haven't solved Exclude questions I've already solved

Machine Learning Engineer

The Python around models — features, metrics, similarity and anomaly detection.

13 problems

Python competencies

Feature scaling
Evaluation metrics
Vector math
Statistics
Data preparation

Learning path

Vector math
Feature scaling
Classification metrics
Anomaly detection
Retrieval

Interview scenarios

Compute macro F1
Scale features
Flag anomalies with z-scores

Topics: Vectors and embeddings, Statistics, Model evaluation, ETL pipelines, Lists and tuples

Libraries: NumPy, pandas, scikit-learn, PyTorch

Recommended mix: Beginner 15% · Easy 25% · Medium 35% · Hard 20% · Expert 5%

Start a Machine Learning Engineer session

Mode

Guided practiceNo timer. Hints, solutions, notes and as many runs as you like.Timed assessmentA fixed set of questions against a server-side countdown. Unsubmitted work is finalised when time runs out.Mock interviewQuestions one at a time with structured feedback on correctness, quality, efficiency and testing.Adaptive practiceQuestions chosen from your recorded weak topics, with the reason for each choice.Daily challengeOne curated question per day. Solve it to extend your streak.

Roles

Difficulty (any)

Topics (any)

Number of questions

Include questions I attempted but haven't solved Exclude questions I've already solved

MLOps Engineer

Shipping and operating models — monitoring, canaries, evaluation gates and scheduling.

9 problems

Python competencies

Model monitoring
Canary analysis
Evaluation gates
Metrics
GPU and job scheduling

Learning path

Metrics and histograms
Evaluation
Canary analysis
Anomaly detection
Fair scheduling

Interview scenarios

Decide whether to promote a canary model
Bucket latency into histograms
Schedule training jobs fairly

Topics: Monitoring and alerting, Model evaluation, Statistics, Simulation and state machines, CI/CD automation

Libraries: MLflow, Kubeflow, prometheus_client, NumPy

Recommended mix: Beginner 10% · Easy 25% · Medium 35% · Hard 20% · Expert 10%

Start a MLOps Engineer session

Mode

Guided practiceNo timer. Hints, solutions, notes and as many runs as you like.Timed assessmentA fixed set of questions against a server-side countdown. Unsubmitted work is finalised when time runs out.Mock interviewQuestions one at a time with structured feedback on correctness, quality, efficiency and testing.Adaptive practiceQuestions chosen from your recorded weak topics, with the reason for each choice.Daily challengeOne curated question per day. Solve it to extend your streak.

Roles

Difficulty (any)

Topics (any)

Number of questions

Include questions I attempted but haven't solved Exclude questions I've already solved

Kubernetes Engineer

Kubernetes from Python — resource quantities, pod health, rollouts and autoscaling.

5 problems

Python competencies

Kubernetes Python client
Resource quantities
Pod status
Rollout strategy
Autoscaling

Learning path

Pod status
Resource quantities
Unhealthy workloads
Rolling updates
Autoscaling

Interview scenarios

List unhealthy pods with reasons
Sum container requests
Simulate the HPA

Topics: Kubernetes, Simulation and state machines, Monitoring and alerting, Parsing text

Libraries: kubernetes, PyYAML, kopf

Recommended mix: Beginner 15% · Easy 25% · Medium 35% · Hard 15% · Expert 10%

Start a Kubernetes Engineer session

Mode

Guided practiceNo timer. Hints, solutions, notes and as many runs as you like.Timed assessmentA fixed set of questions against a server-side countdown. Unsubmitted work is finalised when time runs out.Mock interviewQuestions one at a time with structured feedback on correctness, quality, efficiency and testing.Adaptive practiceQuestions chosen from your recorded weak topics, with the reason for each choice.Daily challengeOne curated question per day. Solve it to extend your streak.

Roles

Difficulty (any)

Topics (any)

Number of questions

Include questions I attempted but haven't solved Exclude questions I've already solved

Infrastructure Automation Engineer

Desired-state automation — plans, drift, configuration and scheduling.

9 problems

Python competencies

Desired state
Drift detection
Plan analysis
Configuration parsing
Scheduling

Learning path

Config parsing
Config diffs
Drift and plans
Interpolation
Scheduling

Interview scenarios

Plan create/update/delete from desired state
Flag destructive Terraform changes
Resolve variable references

Topics: Infrastructure as code, Files and configuration, Parsing text, Dictionaries, Graphs, Dates and times

Libraries: python-terraform, Ansible, Pulumi, PyYAML, Jinja2

Recommended mix: Beginner 15% · Easy 30% · Medium 35% · Hard 15% · Expert 5%

Start a Infrastructure Automation Engineer session

Mode

Guided practiceNo timer. Hints, solutions, notes and as many runs as you like.Timed assessmentA fixed set of questions against a server-side countdown. Unsubmitted work is finalised when time runs out.Mock interviewQuestions one at a time with structured feedback on correctness, quality, efficiency and testing.Adaptive practiceQuestions chosen from your recorded weak topics, with the reason for each choice.Daily challengeOne curated question per day. Solve it to extend your streak.

Roles

Difficulty (any)

Topics (any)

Number of questions

Include questions I attempted but haven't solved Exclude questions I've already solved

Security Engineer / DevSecOps

Security automation — secrets detection, policy analysis, dependency risk and masking.

7 problems

Python competencies

Secrets detection
Policy analysis
Dependency vulnerabilities
Input validation
Log redaction

Learning path

Validation
Redaction
Policy analysis
Secrets scanning
Dependency risk

Interview scenarios

Mask secrets in logs
Find wildcard IAM permissions
Report vulnerable dependencies

Topics: Security automation, IAM and policies, Regular expressions, Validation and schemas, Log analysis

Libraries: re, hashlib, bandit, safety, cryptography

Recommended mix: Beginner 15% · Easy 30% · Medium 30% · Hard 20% · Expert 5%

Start a Security Engineer / DevSecOps session

Mode

Guided practiceNo timer. Hints, solutions, notes and as many runs as you like.Timed assessmentA fixed set of questions against a server-side countdown. Unsubmitted work is finalised when time runs out.Mock interviewQuestions one at a time with structured feedback on correctness, quality, efficiency and testing.Adaptive practiceQuestions chosen from your recorded weak topics, with the reason for each choice.Daily challengeOne curated question per day. Solve it to extend your streak.

Roles

Difficulty (any)

Topics (any)

Number of questions

Include questions I attempted but haven't solved Exclude questions I've already solved

Full Stack Engineer

Python on the server side of a product — formatting, validation, search and streaming.

7 problems

Python competencies

Strings and formatting
Validation
Autocomplete and search
Streaming
API integration

Learning path

Strings and slugs
Validation
Search
Pagination
Streaming

Interview scenarios

Generate URL slugs
Build autocomplete
Parse a streamed response

Topics: Strings, Formatting output, Validation and schemas, Tries, Streaming responses, HTTP and REST APIs

Libraries: FastAPI, Django, Flask, Jinja2

Recommended mix: Beginner 20% · Easy 30% · Medium 30% · Hard 15% · Expert 5%

Start a Full Stack Engineer session

Mode

Guided practiceNo timer. Hints, solutions, notes and as many runs as you like.Timed assessmentA fixed set of questions against a server-side countdown. Unsubmitted work is finalised when time runs out.Mock interviewQuestions one at a time with structured feedback on correctness, quality, efficiency and testing.Adaptive practiceQuestions chosen from your recorded weak topics, with the reason for each choice.Daily challengeOne curated question per day. Solve it to extend your streak.

Roles

Difficulty (any)

Topics (any)

Number of questions

Include questions I attempted but haven't solved Exclude questions I've already solved

Software Development Engineer

Data structures and algorithms with practical framing, as in SDE interviews.

13 problems

Python competencies

Hashing
Sorting
Graphs
Heaps
Binary search
Dynamic programming

Learning path

Arrays and strings
Hashing and sorting
Stacks and heaps
Graphs
Dynamic programming

Interview scenarios

Find the first bad build
Merge maintenance windows
Route by lowest latency

Topics: Hashing, Sorting, Stacks and queues, Heaps, Graphs, Binary search, Dynamic programming, Tries, Intervals, Time and space complexity

Libraries: collections, heapq, bisect, functools

Recommended mix: Beginner 15% · Easy 25% · Medium 35% · Hard 20% · Expert 5%

Start a Software Development Engineer session

Mode

Guided practiceNo timer. Hints, solutions, notes and as many runs as you like.Timed assessmentA fixed set of questions against a server-side countdown. Unsubmitted work is finalised when time runs out.Mock interviewQuestions one at a time with structured feedback on correctness, quality, efficiency and testing.Adaptive practiceQuestions chosen from your recorded weak topics, with the reason for each choice.Daily challengeOne curated question per day. Solve it to extend your streak.

Roles

Difficulty (any)

Topics (any)

Number of questions

Include questions I attempted but haven't solved Exclude questions I've already solved

Automation Engineer

Reliable scripts and bots — files, schedules, APIs, retries and parallel work.

16 problems

Python competencies

File and text processing
Scheduling
API automation
Retries
Parallelism

Learning path

Files and text
Schedules
API automation
Parallel work
Robust sync jobs

Interview scenarios

Compute the next cron run
Parallelise slow calls
Detect flaky tests

Topics: Files and configuration, Dates and times, HTTP and REST APIs, Retries and backoff, Threads and concurrency, Testing and test data, Pagination

Libraries: pathlib, schedule, requests, concurrent.futures, pytest

Recommended mix: Beginner 15% · Easy 30% · Medium 35% · Hard 15% · Expert 5%

Start a Automation Engineer session

Mode

Guided practiceNo timer. Hints, solutions, notes and as many runs as you like.Timed assessmentA fixed set of questions against a server-side countdown. Unsubmitted work is finalised when time runs out.Mock interviewQuestions one at a time with structured feedback on correctness, quality, efficiency and testing.Adaptive practiceQuestions chosen from your recorded weak topics, with the reason for each choice.Daily challengeOne curated question per day. Solve it to extend your streak.

Roles

Difficulty (any)

Topics (any)

Number of questions

Include questions I attempted but haven't solved Exclude questions I've already solved

Solutions Architect

Design trade-offs proven in code — failover, partitioning, consistency and cost.

11 problems

Python competencies

Multi-region design
Partitioning
Consistency and ordering
Capacity and cost
Resilient integration

Learning path

Capacity math
Partitioning
Failover
Consistency
Resilient gateways

Interview scenarios

Plan multi-region traffic weights
Partition keys with a hash ring
Order events with vector clocks

Topics: Resilience patterns, Hashing, Simulation and state machines, Dynamic programming, Graphs, Rate limiting

Libraries: boto3, asyncio, hashlib

Recommended mix: Beginner 5% · Easy 20% · Medium 35% · Hard 25% · Expert 15%

Start a Solutions Architect session

Mode

Guided practiceNo timer. Hints, solutions, notes and as many runs as you like.Timed assessmentA fixed set of questions against a server-side countdown. Unsubmitted work is finalised when time runs out.Mock interviewQuestions one at a time with structured feedback on correctness, quality, efficiency and testing.Adaptive practiceQuestions chosen from your recorded weak topics, with the reason for each choice.Daily challengeOne curated question per day. Solve it to extend your streak.

Roles

Difficulty (any)

Topics (any)

Number of questions

Include questions I attempted but haven't solved Exclude questions I've already solved

Choose your engineering focus

Python Developer

Python competencies

Learning path

Interview scenarios

DevOps Engineer

Python competencies

Learning path

Interview scenarios

AI Engineer

Python competencies

Learning path

Interview scenarios

Generative AI Engineer

Python competencies

Learning path

Interview scenarios

Forward Deployed Engineer

Python competencies

Learning path

Interview scenarios

Platform Engineer

Python competencies

Learning path

Interview scenarios

Site Reliability Engineer

Python competencies

Learning path

Interview scenarios

Cloud Engineer

Python competencies

Learning path

Interview scenarios

AWS Engineer

Python competencies

Learning path

Interview scenarios

Backend Engineer

Python competencies

Learning path

Interview scenarios

Data Engineer

Python competencies

Learning path

Interview scenarios

Machine Learning Engineer

Python competencies

Learning path

Interview scenarios

MLOps Engineer

Python competencies

Learning path

Interview scenarios

Kubernetes Engineer

Python competencies

Learning path

Interview scenarios

Infrastructure Automation Engineer

Python competencies

Learning path

Interview scenarios

Security Engineer / DevSecOps

Python competencies

Learning path

Interview scenarios

Full Stack Engineer

Python competencies

Learning path

Interview scenarios

Software Development Engineer

Python competencies

Learning path

Interview scenarios

Automation Engineer

Python competencies

Learning path

Interview scenarios

Solutions Architect

Python competencies

Learning path