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Answer: Event-Driven Architecture
Event-driven architecture uses message brokers (Kafka, RabbitMQ) for asynchronous, decoupled service communication. Enables scalability, resilience, and real-time processing. Critical for modern distributed systems.
Answer: Multi-AZ / High-Availability
Multi-AZ deployment distributes resources across physically separate data centers within a region, protecting against zone failures. Critical for business continuity and SLA compliance.
Answer: CaaS
CaaS (Container as a Service) provides managed Kubernetes (EKS, AKS, GKE) for container deployment, scaling, and management. Abstracts infrastructure while retaining control. Critical for cloud-native development.
Answer: True
Deception deploys fake assets (servers, credentials) to lure attackers, enabling early detection and threat intelligence. Critical for proactive defense against advanced adversaries.
Answer: All of these
Malware analysis combines: static (code inspection), dynamic (runtime behavior), sandboxing (isolated execution). Critical for threat intelligence and incident response.
Answer: MITRE ATT&CK
MITRE ATT&CK matrix catalogs adversary TTPs across enterprise, cloud, mobile. Enables threat-informed defense, detection engineering, and red teaming. Critical for mature security operations.
Answer: Anomaly-based
Anomaly detection establishes baselines of normal behavior and flags deviations, enabling zero-day threat detection. Critical for advanced threat protection in modern SOCs.
Answer: True
Feature stores (Feast, Tecton) manage feature engineering, versioning, and serving, preventing training-serving skew. Critical for reliable ML system performance in production.
Answer: All of these
Responsible MLOps: model cards document limitations, fairness audits detect bias across groups, A/B testing validates real-world impact. Critical for ethical AI deployment.
Answer: Pruning
Model pruning removes low-importance weights/neurons, reducing size and inference cost. Combined with quantization for efficient edge deployment. Critical for scalable AI systems.
Answer: True
Prompt injection exploits LLM instruction-following to bypass safety guards, extract training data, or execute unauthorized actions. Critical vulnerability for LLM applications requiring robust input sanitization.
Answer: RLHF
Reinforcement Learning from Human Feedback (RLHF) trains reward models from human rankings, then optimizes LLM to maximize rewards. Critical for aligning AI with human values and safety.
Answer: Artificial Intelligence / GPAI
GPAI (founded 2020) brings together experts for responsible AI: research, policy, best practices. India is founding member. Critical for understanding global AI governance landscape.
Answer: All of these
Effective tech policy: technology-neutral (focus on outcomes), proportionate to risks, adaptable to innovation. Balances consumer protection with innovation incentives. Critical for policy design.
Answer: True
Digital diplomacy: cyber norms, data governance, tech partnerships, capacity building. India actively engages in UN, GPAI, OECD on AI ethics, cybersecurity, digital trade. Critical for foreign policy questions.
Answer: TSDSI
Telecommunications Standards Development Society, India (TSDSI) develops indigenous telecom standards, represents India in 3GPP, ITU. Critical for technology sovereignty and 5G/6G leadership.
Answer: India
India Stack layers: identity (Aadhaar), payments (UPI), data sharing (AA), documents (DigiLocker). Enables innovation on public digital infrastructure. Critical for digital governance questions.
Answer: Multiple Imputation
Multiple imputation creates several completed datasets with different plausible values for missing data, analyzing each and combining results. Accounts for uncertainty vs single imputation methods.
Answer: True
Responsible data science: fairness audits for bias, privacy-preserving techniques, impact assessments. Critical for ethical AI deployment and regulatory compliance (DPDP Act).
Answer: PCA
Principal Component Analysis (PCA) transforms features into orthogonal components ordered by variance explained. Enables visualization, noise reduction, and faster model training.