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Answer: Quantum
Quantum annealing uses quantum tunneling to find global minima in optimization landscapes. Suitable for logistics, finance, machine learning. Different from gate-based quantum computing.
Answer: Inversion
Model inversion exploits model outputs to infer sensitive training data attributes. Critical privacy risk for ML systems handling personal data.
Answer: Uniswap
Uniswap uses automated market maker (AMM) model with liquidity pools instead of order books. Enables permissionless token trading with constant product formula.
Answer: Neuromorphic Computing
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.
Answer: Shor's Algorithm
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.
Answer: Differential
Differential Privacy mathematically guarantees that model outputs don't reveal whether specific individuals were in training data. Critical for privacy-preserving ML deployment.
Answer: Adversarial Examples
Adversarial examples add imperceptible perturbations to inputs that cause ML models to misclassify. Critical vulnerability for computer vision, NLP systems requiring robust defenses.
Answer: Both B and C
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.
Answer: IoT
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.
Answer: Access Control
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.
Answer: All of these
Anomaly detection uses: Supervised Learning (trained on labeled attacks), Unsupervised Learning (clustering normal vs abnormal patterns), and Reinforcement Learning (adaptive response to threats). Combined approaches improve detection of zero-day attacks and reduce false positives in SIEM systems.
Answer: AES-256
AES-256 (Advanced Encryption Standard with 256-bit key) is approved by NIST for top-secret information. Provides strong security against brute-force attacks. DES/3DES are deprecated; RC4 has known vulnerabilities. AES is efficient in both hardware and software implementations.
Answer: Multimodal
Multimodal models (CLIP, LLaVA) process text, images, audio jointly, enabling visual question answering, image captioning, and cross-modal retrieval.
Answer: All of these
Code LLMs (Codex, CodeLlama) trained on code repositories enable natural language to code generation. Combined with fine-tuning and prompt engineering for developer productivity tools.
Answer: Event-Driven Architecture
Event-driven architecture uses message brokers for asynchronous, decoupled service communication. Enables scalability, resilience, real-time processing.
Answer: Smart Contracts
Smart contracts are self-executing programs stored on blockchain that automatically enforce terms when conditions are satisfied. Used in DeFi, supply chain, insurance.
Answer: Both A and B
OWASP Application Security Verification Standard (ASVS) and ISO 27034 provide frameworks for secure software development, testing, and vulnerability management.
Answer: Multi-Site Active-Active
Active-active runs full workload in multiple regions simultaneously, enabling instant failover with zero RPO/RTO. Highest cost but maximum resilience. Critical for mission-critical systems.
Answer: Both A and B
High-dimensional handling: dimensionality reduction (PCA, autoencoders) compresses features; sparse representations (L1 regularization) select relevant features. Critical for text, recommendation systems.
Answer: LLM / Generation
Code LLMs (Codex, CodeLlama) trained on code repositories enable natural language to code generation, debugging, and documentation. Critical for developer productivity tools.