Technology – Hard Level – GK Questions

Neuromorphic chips excel at processing sparse, event-driven data from sensors: vision, audio, IoT. Critical for edge AI with low power budgets.

DNA computing uses biochemical reactions for massive parallelism. Research stage with potential for solving complex combinatorial problems.

Zero-Knowledge Proofs enable proving model properties without revealing weights. Critical for privacy-preserving model verification.

Yield farming/liquidity mining rewards users with tokens for providing liquidity to DEX pools. Critical DeFi incentive mechanism with impermanent loss risks.

Neuromorphic chips use spiking neurons that fire only when needed, and event-driven computation that processes only changes. Critical for edge AI efficiency.

Protein computing uses biomolecular interactions for parallel computation. Research stage with potential for ultra-low power, massive parallelism.

DP-SGD adds calibrated noise to gradients during training, providing differential privacy guarantees. Critical for privacy-preserving ML training.

Horizontal scaling adds more instances of stateless services to handle increased load. Contrasts with vertical scaling (upgrading instance size).

DeFi risks: smart contract bugs enable fund theft, oracle manipulation feeds false prices, impermanent loss affects liquidity providers. Critical for DeFi security understanding.

Neuromorphic chips achieve orders-of-magnitude better energy efficiency by mimicking brain's event-driven, sparse computation. Critical for edge AI and sustainable computing.

Quantum annealing uses quantum tunneling to find global minima in optimization landscapes. Suitable for logistics, finance, machine learning. Different from gate-based quantum computing.

Model inversion exploits model outputs to infer sensitive training data attributes. Critical privacy risk for ML systems handling personal data.

Uniswap uses automated market maker (AMM) model with liquidity pools instead of order books. Enables permissionless token trading with constant product formula.

Neuromorphic chips (Intel Loihi, IBM TrueNorth) use spiking neural networks and event-driven processing for orders-of-magnitude better energy efficiency than GPUs for AI tasks.

Shor's Algorithm factors integers in polynomial time on quantum computers, breaking RSA which relies on factoring difficulty. Requires fault-tolerant quantum computers not yet available.

Differential Privacy mathematically guarantees that model outputs don't reveal whether specific individuals were in training data. Critical for privacy-preserving ML deployment.

Adversarial examples add imperceptible perturbations to inputs that cause ML models to misclassify. Critical vulnerability for computer vision, NLP systems requiring robust defenses.

Hashing (e.g., SHA-256) generates fixed-size digest to detect changes; any modification alters the hash. Digital Signatures combine hashing with asymmetric encryption to also verify sender identity and provide non-repudiation. Both ensure integrity; signatures add authentication and legal validity.

IoT sensors (strain gauges, accelerometers, temperature) embedded in infrastructure monitor stress, vibrations, and environmental conditions in real-time. Data analytics predict maintenance needs and prevent failures. Critical for smart cities, disaster resilience, and extending asset lifespan with predictive maintenance.

Access Control Models define policies for subject-object interactions: DAC (owner-controlled), MAC (system-enforced labels), RBAC (role-based permissions), ABAC (attribute-based rules). Critical for enforcing least privilege and compliance in enterprise systems. Implemented via IAM solutions and directory services.