Hard-level Cardano interview questions covering advanced optimization and formal verification. Q1: How do you formally verify Plutus contracts? Answer: Formal Verification: Use Agda/Coq for proofs Prove contract properties Ensure correctness Mathematical guarantees Q2: How do you optimize for Cardano's execution model? …

Medium-level Cardano interview questions covering advanced Plutus development and optimization. Q1: How do you optimize Plutus contract execution costs? Answer: Optimization: Minimize on-chain code Use off-chain code when possible Optimize datum/redeemer size Batch operations Q2: How do you implement complex state …

Hard-level Cosmos chain operation questions covering advanced algorithms, performance optimization, and complex validator management. Q1: How do you implement advanced consensus optimizations and reduce latency? Answer: Optimistic Execution: 1type OptimisticExecutor struct { 2 pendingTxs map[string]*PendingTx 3 …

Hard-level Polkadot interview questions covering advanced optimization and complex parachain design. Q1: How do you implement advanced runtime upgrades? Answer: Runtime Upgrades: 1pub struct CustomUpgrade; 2 3impl OnRuntimeUpgrade for CustomUpgrade { 4 fn on_runtime_upgrade() -> Weight { 5 // Migration logic 6 // …

Hard-level Protocol Buffers interview questions covering advanced topics, performance optimization, and complex scenarios. Q1: How does Protocol Buffer encoding work internally? Answer: Varint Encoding: Variable-length encoding for integers Smaller numbers use fewer bytes Most significant bit indicates continuation …

Hard-level Solana interview questions covering advanced optimization, security, and complex program design. Q1: How do you implement advanced account compression and state optimization? Answer: State Compression: 1// Use Merkle trees for state 2use merkle_tree::MerkleTree; 3 4pub struct CompressedState { 5 tree: …

Hard-level LLM and Agentic AI interview questions covering multi-agent systems, production optimization, and advanced architectures. Q1: Design a multi-agent system with agent communication and coordination. Answer: How Multi-Agent Systems Work: Multiple specialized agents collaborate, communicate, and coordinate to …

Medium-level scalability interview questions covering advanced scaling techniques and optimization. Q1: Explain database partitioning strategies. Answer: Horizontal Partitioning (Sharding) Range Partitioning: Hash Partitioning: Vertical Partitioning Q2: How do you handle database hotspots? Answer: Problem: Uneven data …

Database performance issues, solutions, and SQL vs NoSQL comparison. Common Performance Issues 1. Missing Indexes 1-- ❌ Problem: Full table scan 2SELECT * FROM users WHERE email = 'test@example.com'; 3 4-- ✅ Solution: Add index 5CREATE INDEX idx_email ON users(email); 6 7-- Verify index usage 8EXPLAIN SELECT * …

Techniques for building small, efficient Docker images with focus on Alpine, scratch, and Go binaries. Multi-Stage Builds Basic Multi-Stage Build 1# Build stage 2FROM golang:1.21-alpine AS builder 3WORKDIR /app 4COPY go.mod go.sum ./ 5RUN go mod download 6COPY . . 7RUN go build -o myapp 8 9# Final stage 10FROM …

Go pprof commands and practices for analyzing CPU, memory, goroutine, and block profiles. Enable Profiling HTTP Server 1package main 2 3import ( 4 "log" 5 "net/http" 6 _ "net/http/pprof" // Import for side effects 7) 8 9func main() { 10 // Your application code here 11 12 // Start pprof server …

Numerical methods, regularization techniques, and useful concise algorithms.

Minimize $f(x)$. Gradient Descent $$ x_{k+1} = x_k - \alpha \nabla f(x_k) $$ 1def gradient_descent(f, grad_f, x0, alpha=0.01, tol=1e-6, max_iter=1000): 2 x = x0 3 for i in range(max_iter): 4 grad = grad_f(x) 5 if np.linalg.norm(grad) < tol: 6 break 7 x = x - alpha * grad 8 return x SciPy 1from scipy.optimize import …