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Python practice
Problems
Problems
13 problems for Machine Learning Engineer. Read any of them; sign in to code, run and submit.
All roles
Python Developer
DevOps Engineer
AI Engineer
Generative AI Engineer
Forward Deployed Engineer
Platform Engineer
Site Reliability Engineer
Cloud Engineer
AWS Engineer
Backend Engineer
Data Engineer
Machine Learning Engineer
MLOps Engineer
Kubernetes Engineer
Infrastructure Automation Engineer
Security Engineer / DevSecOps
Full Stack Engineer
Software Development Engineer
Automation Engineer
Solutions Architect
All levels
Beginner
Easy
Medium
Hard
Expert
All topics
Strings
Lists and tuples
Loops and control flow
Functions
Formatting output
Dates and times
Classes and objects
Dataclasses
Decorators
Dictionaries
Sets
Stacks and queues
Hashing
Heaps
Tries
Sorting
Graphs
Intervals
Binary search
Dynamic programming
Simulation and state machines
asyncio
Threads and concurrency
HTTP and REST APIs
Pagination
Rate limiting
Validation and schemas
Streaming responses
Files and configuration
Log analysis
Monitoring and alerting
Infrastructure as code
CI/CD automation
Security automation
Kubernetes
AWS SDK (Boto3-style)
IAM and policies
Vectors and embeddings
Retrieval-augmented generation
LLM integration
AI agents and tools
MCP and JSON-RPC
Model evaluation
JSON
Parsing text
Regular expressions
ETL pipelines
SQL patterns in Python
Statistics
Exceptions
Retries and backoff
Resilience patterns
Context managers
Testing and test data
Caching
Generators and laziness
Time and space complexity
Filter
Count word frequencies
Strings · Dictionaries
~8 min
Beginner
Running average
Loops and control flow · Statistics
~6 min
Beginner
Transpose a metrics table
Lists and tuples
~5 min
Beginner
Cosine similarity of embeddings
Vectors and embeddings · Functions
~8 min
Easy
Group-by aggregation
SQL patterns in Python · ETL pipelines · Dictionaries
~12 min
Easy
Latest model version per stage
Dictionaries · Model evaluation
~10 min
Easy
Tokenise text for an LLM pipeline
LLM integration · Strings · Regular expressions
~10 min
Easy
Build a lazy generator pipeline
Generators and laziness · Time and space complexity
~20 min
Medium
Keyword retrieval with TF-IDF
Retrieval-augmented generation · Statistics · Dictionaries
~25 min
Medium
Scale features for a model
Statistics · Vectors and embeddings · Lists and tuples
~15 min
Medium
Detect latency anomalies with z-scores
Statistics · Monitoring and alerting · Time and space complexity
~25 min
Hard
Diversify retrieval with MMR
Retrieval-augmented generation · Vectors and embeddings
~30 min
Hard
Precision, recall and F1
Model evaluation · Statistics
~25 min
Hard