Question Bank

Practice algorithm and coding problems to ace your technical interviews.

18 real interview questions collected so far

Questions I personally encountered in interviews — more coming as I interview! These are shown first with a red background.

Recommended Next

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Easy

Maximum Depth of Binary Tree

tree, dfsLeetCode #104

Your Progress

0 of 62 completed

Source

Difficulty

Status

Topic

Tag

Real

Describe your experience with ontology frameworks. How have you used them in practice, and what are the key design and governance practices you follow?

Hard

ontology, knowledge-graph, data-modeling +2

Real

Explain agentic workflow and frameworks. How have you designed or used them in production systems?

Hard

agents, LLM, agentic +3

Real

How do you think about prompt engineering versus fine-tuning when working with LLMs?

Medium

LLM, prompting, fine-tuning +2

Real

How do you pick chunk size and overlap when chunking a PDF document for retrieval?

Medium

RAG, chunking, retrieval +3

Real

How do you handle tables when ingesting PDFs for RAG systems?

Medium

RAG, PDF, tables +3

Real

At a high-level, how do vector databases work, and how do you choose an embedding model when using one?

Medium

vector, embedding, retrieval +3

Real

When is it bad to use embedding models?

Medium

embedding, retrieval, search +2

Maximum Depth of Binary Tree

Easy

tree, dfs, recursionLeetCode #104

Two Sum

Easy

array, hash-tableLeetCode #1

Number of Islands

Medium

graph, dfs, bfs +1LeetCode #200

Merge Two Sorted Lists

Easy

linked-list, recursionLeetCode #21

Valid Parentheses

Easy

stack, stringLeetCode #20

Coin Change

Medium

dynamic-programming, array, bfsLeetCode #322

Linked List Cycle

Easy

linked-list, two-pointersLeetCode #141

Same Tree

Easy

tree, dfs, recursionLeetCode #100

Reverse Linked List

Easy

linked-list, recursionLeetCode #206

Invert Binary Tree

Easy

tree, dfs, recursionLeetCode #226

Binary Tree Level Order Traversal

Medium

tree, bfs, queueLeetCode #102

Min Stack

Medium

stack, designLeetCode #155

Kth Largest Element in an Array

Medium

heap, quickselect, sortingLeetCode #215

Clone Graph

Medium

graph, dfs, bfs +1LeetCode #133

Climbing Stairs

Easy

dynamic-programming, math, memoizationLeetCode #70

Contains Duplicate

Easy

array, hash-tableLeetCode #217

Valid Anagram

Easy

hash-table, string, sortingLeetCode #242

Group Anagrams

Medium

hash-table, string, sortingLeetCode #49

Top K Frequent Elements

Medium

array, hash-table, heap +1LeetCode #347

Merge K Sorted Lists

Hard

linked-list, heap, divide-and-conquerLeetCode #23

Regularization: L1, L2, and Beyond

Medium

machine-learning, regularization, overfitting +1Generated

System Design: AI Customer Support with AWS Bedrock

Senior

AWS, Bedrock, System Design +5

You mention 'Python (Advanced)' on your resume. Explain Python's GIL and how it affects multi-threading in ML workloads.

Hard

python, concurrency, performance +1

What is overfitting? How do you prevent it?

Easy

fundamental, regularization, must-know

Explain Python's type hints and how you use them in production ML code. Why are they important?

Medium

python, type-hints, code-quality +1

Explain the bias-variance tradeoff.

Easy

fundamental, theory, must-know

Explain backpropagation. How does it work?

Medium

deep-learning, optimization, must-know

Walk me through your code structure for a production ML API. What design patterns do you use?

Hard

python, architecture, design-patterns +1

Your ML interview website is built with TypeScript. Why TypeScript over JavaScript for this project?

Medium

typescript, frontend, type-safety +1

Explain precision, recall, and F1 score. When to optimize for which?

Easy

evaluation, metrics, must-know

You mention React experience from Android/React Native. How did you apply that to building web interfaces for your ML projects?

Medium

react, frontend, gradio +1

What is the attention mechanism in transformers?

Medium

nlp, transformers, deep-learning +1

Compare zero-shot, few-shot, and chain-of-thought prompting. When would you use each?

Easy

llm-engineering, prompting, must-know

Why did you choose Next.js for your ML interview website instead of plain React?

Medium

next.js, ssg, ssr +2

You've built 4 production FastAPI services. What are your REST API design principles?

Hard

rest-api, fastapi, api-design +1

You use pgvector for semantic search in your RAG chatbot. How does it work and why PostgreSQL over a dedicated vector DB?

Hard

postgresql, pgvector, vector-search +1

You mention 35% cache hit rate with Redis. Walk me through your caching strategy.

Medium

redis, caching, optimization +1

You fine-tuned BERT for sentiment analysis (89% F1). Explain the fine-tuning process step-by-step.

Hard

bert, nlp, fine-tuning +1

You used ResNet50 for image classification (94% accuracy). Why ResNet over other architectures?

Medium

resnet, computer-vision, transfer-learning

You implemented Grad-CAM for model interpretability. How does it work and why is it useful?

Hard

grad-cam, interpretability, explainability +1

You mention AWS (S3, EC2, SageMaker). How have you used these services in your ML projects?

Medium

aws, s3, ec2 +2

Implement K-Means Clustering

Medium

clustering, unsupervised, classic-ml +2

Implement Linear Regression with Gradient Descent

Medium

regression, gradient-descent, optimization +2

Implement Softmax and Cross-Entropy Loss

Easy

neural-networks, classification, loss-functions +2

Question Bank

Recommended Next

Maximum Depth of Binary Tree

Linear Regression Implementation

What is Regularization?

When would you fine-tune an LLM vs using prompting?

Explain function calling in LLMs. How does the model decide when to use tools?

What causes LLM hallucinations? How do you detect and prevent them?

How do you evaluate LLM outputs? What metrics matter?

How do you manage context windows in LLM applications?

Explain RAG (Retrieval-Augmented Generation). When and how would you use it?

Compare GPT-4, Claude, and open-source LLMs. When would you use each?

How do you ensure LLMs output valid structured data (JSON, specific formats)?

AI Orchestration: How do you coordinate multiple AI agents in workflows?

Describe your experience with ontology frameworks. How have you used them in practice, and what are the key design and governance practices you follow?

Explain agentic workflow and frameworks. How have you designed or used them in production systems?

How do you think about prompt engineering versus fine-tuning when working with LLMs?

How do you pick chunk size and overlap when chunking a PDF document for retrieval?

How do you handle tables when ingesting PDFs for RAG systems?

At a high-level, how do vector databases work, and how do you choose an embedding model when using one?

When is it bad to use embedding models?

Maximum Depth of Binary Tree

Two Sum

Number of Islands

Merge Two Sorted Lists

Valid Parentheses

Coin Change

Linked List Cycle

Same Tree

Reverse Linked List

Invert Binary Tree

Binary Tree Level Order Traversal

Min Stack

Kth Largest Element in an Array

Clone Graph

Climbing Stairs

Contains Duplicate

Valid Anagram

Group Anagrams

Top K Frequent Elements

Merge K Sorted Lists

Regularization: L1, L2, and Beyond

System Design: AI Customer Support with AWS Bedrock

You mention 'Python (Advanced)' on your resume. Explain Python's GIL and how it affects multi-threading in ML workloads.

What is overfitting? How do you prevent it?

Explain Python's type hints and how you use them in production ML code. Why are they important?

Explain the bias-variance tradeoff.

Explain backpropagation. How does it work?

Walk me through your code structure for a production ML API. What design patterns do you use?

Your ML interview website is built with TypeScript. Why TypeScript over JavaScript for this project?

Explain precision, recall, and F1 score. When to optimize for which?

You mention React experience from Android/React Native. How did you apply that to building web interfaces for your ML projects?

What is the attention mechanism in transformers?

Compare zero-shot, few-shot, and chain-of-thought prompting. When would you use each?

Why did you choose Next.js for your ML interview website instead of plain React?

You've built 4 production FastAPI services. What are your REST API design principles?

You use pgvector for semantic search in your RAG chatbot. How does it work and why PostgreSQL over a dedicated vector DB?

You mention 35% cache hit rate with Redis. Walk me through your caching strategy.

You fine-tuned BERT for sentiment analysis (89% F1). Explain the fine-tuning process step-by-step.

You used ResNet50 for image classification (94% accuracy). Why ResNet over other architectures?

You implemented Grad-CAM for model interpretability. How does it work and why is it useful?

You mention AWS (S3, EC2, SageMaker). How have you used these services in your ML projects?

Implement K-Means Clustering

Implement Linear Regression with Gradient Descent

Implement Softmax and Cross-Entropy Loss

Recommended Next

Maximum Depth of Binary Tree

Linear Regression Implementation

What is Regularization?

When would you fine-tune an LLM vs using prompting?

Explain function calling in LLMs. How does the model decide when to use tools?

What causes LLM hallucinations? How do you detect and prevent them?

How do you evaluate LLM outputs? What metrics matter?

How do you manage context windows in LLM applications?

Explain RAG (Retrieval-Augmented Generation). When and how would you use it?

Compare GPT-4, Claude, and open-source LLMs. When would you use each?

How do you ensure LLMs output valid structured data (JSON, specific formats)?

AI Orchestration: How do you coordinate multiple AI agents in workflows?

Describe your experience with ontology frameworks. How have you used them in practice, and what are the key design and governance practices you follow?

Explain agentic workflow and frameworks. How have you designed or used them in production systems?