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Answer: FAME
FAME (Faster Adoption and Manufacturing of Electric Vehicles) India provides demand incentives for EVs and charging infrastructure. Phase-II focuses on public transport and shared mobility.
Answer: Regularization
Regularization (L1/Lasso, L2/Ridge, Dropout) adds penalty terms to loss function or randomly drops units during training. Prevents overfitting by discouraging complex models. Critical for generalization.
Answer: Red Team / Penetration
Red teaming conducts adversarial simulations (cyber, physical, social) to identify gaps in detection, response, and resilience. Distinct from vulnerability scanning. Critical for mature security programs.
Answer: Multimodal Learning
Multimodal models (CLIP, LLaVA) process text, images, audio jointly, enabling visual question answering, image captioning, and cross-modal retrieval. Critical for next-gen AI applications.
Answer: Semiconductor
India Semiconductor Mission implements ₹76,000 crore incentive scheme for fabs, display fabs, ATMP, design. Partnerships with global players announced. Critical for tech sovereignty questions.
Answer: Ensemble
Ensemble methods (bagging, boosting, stacking) combine weak learners to reduce variance, bias, or improve predictions. Random Forests and Gradient Boosting are widely used ensemble techniques.
Answer: Response
Incident response follows structured phases: preparation, detection, containment, eradication, recovery, lessons learned. Critical for minimizing breach impact and improving defenses.
Answer: AI / Autonomous
AI agents combine LLMs with planning, tool use, memory for autonomous task execution. Applications: research assistants, customer service, workflow automation. Critical for next-gen AI systems.
Answer: Consortium / W3C
W3C develops web standards: HTML, CSS, XML, accessibility guidelines. Ensures interoperability and innovation on the web. India participates through W3C India office. Critical for web policy questions.
Answer: Artificial Intelligence / #AIforAll
#AIforAll strategy (NITI Aayog) focuses on leveraging AI for inclusive growth across healthcare, agriculture, education. Emphasizes research, skilling, data governance, and ethical principles.
Answer: F1
F1 Score = 2 * (Precision * Recall) / (Precision + Recall). Harmonic mean balancing false positives and false negatives. Critical for evaluating models on imbalanced datasets.
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: 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: 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: 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: 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: Precision
Precision = TP / (TP + FP). Critical for imbalanced classification tasks where false positives are costly (fraud detection, medical diagnosis). Complements recall for comprehensive evaluation.
Answer: Hybrid
Hybrid cloud combines on-premises infrastructure with public cloud services. Enables data sovereignty compliance while leveraging cloud scalability. Critical for regulated industries.
Answer: Hunting
Threat hunting uses hypotheses, analytics, and analyst expertise to find hidden adversaries. Complements automated detection. Critical for advanced persistent threat (APT) defense.
Answer: Multi-Task
Multi-task learning shares representations across related tasks, improving sample efficiency and generalization. Critical for building versatile AI systems with limited labeled data per task.