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Answer: True
Reusable rockets (SpaceX Falcon 9, ISRO RLV-TD) recover and refurbish boosters, reducing launch costs by 30-70%. Critical for sustainable space economy and frequent satellite deployments.
Answer: 26.5-40
Ka-band (26.5-40 GHz) offers high bandwidth for satellite broadband but susceptible to rain attenuation. Requires adaptive coding and ground station diversity. Critical for satellite communication planning.
Answer: Aditya-L1
Aditya-L1 positioned at Sun-Earth L1 point for uninterrupted solar observation. Studies coronal heating, CMEs, solar flares critical for space weather forecasting affecting satellites and power grids.
Answer: True
Moon's south pole has permanently shadowed craters with water ice, valuable for future lunar bases (life support, rocket fuel). Chandrayaan-3's Pragyan rover analyzed soil composition. Critical for space resource questions.
Answer: OneWeb
OneWeb (Bharti Group partnership) operates LEO constellation for global broadband, especially rural/remote areas. Competes with Starlink. Critical for digital connectivity and space commerce questions.
Answer: NavIC
NavIC (Navigation with Indian Constellation) has 7 satellites providing positioning accuracy better than 20m over India and 1500 km surrounding area. Alternative to GPS for strategic autonomy.
Answer: True
LEO satellites (~500-2000 km altitude) have lower propagation delay (~20-50ms) vs GEO (~35,786 km, ~250ms latency). Critical for real-time applications: video calls, gaming, financial trading.
Answer: Entanglement
Quantum entanglement enables correlated states between particles, foundational for quantum teleportation, superdense coding, and quantum computing. Critical for understanding quantum information science.
Answer: AES-256
AES-256 with 256-bit keys is considered quantum-resistant because Grover's algorithm only provides quadratic speedup, requiring doubling key size for equivalent security. DES/3DES/RC4 are broken or weak.
Answer: ₹8000 crore
NM-QTA (2020) allocates ₹8000 crore for quantum computing, communication, sensing, and materials research. Implemented by DST with IITs, IISc participation. Critical for science policy questions.
Answer: Classical transistors
Leading qubit implementations: superconducting (IBM, Google), trapped ions (IonQ), photonic (Xanadu). Classical transistors cannot maintain quantum coherence. Critical for understanding quantum hardware landscape.
Answer: True
QKD uses no-cloning theorem and measurement disturbance to detect eavesdropping during key exchange. Provides provable security unlike computational security of RSA/ECC. Deployed in limited networks globally.
Answer: True
#AIforAll (NITI Aayog) focuses on leveraging AI for social empowerment: AI for farmers, teachers, doctors. Balances innovation with ethics, inclusion, and national interest. Critical for AI policy questions.
Answer: All of these
Privacy-preserving AI: Differential Privacy adds noise to aggregates; Federated Learning trains on-device; Synthetic Data generates artificial datasets. All enable AI while protecting individual privacy.
Answer: Auditing / Assessment
AI Auditing involves systematic evaluation of models for fairness, accuracy, robustness, and compliance. Tools include bias detection frameworks, performance metrics, and third-party assessments. Critical for responsible AI deployment.
Answer: Both A and B
India endorses OECD AI Principles (2019) and UNESCO Recommendation on AI Ethics (2021). Both emphasize human rights, transparency, accountability, and sustainability. Critical for AI governance questions.
Answer: True
DPDP Act mandates transparency in automated decisions: individuals have right to explanation for decisions affecting rights, benefits, or obligations. Aligns with global standards like GDPR Article 22.
Answer: in-the-loop
Human-in-the-loop (HITL) ensures human oversight of AI decisions, especially in high-stakes domains. Critical for accountability, error correction, and ethical compliance. Complements human-on-the-loop supervision.
Answer: True
Algorithmic accountability mandates transparency in AI: documenting data sources, model design, testing, and decision logic. Enables auditing, bias detection, and redressal. Supported by DPDP Act and sectoral guidelines.
Answer: Fairness
Fairness in AI requires systems to avoid bias based on race, gender, caste, religion, etc. Techniques include bias detection metrics, diverse training data, and fairness-aware algorithms. Critical for ethical AI deployment in hiring, lending, and public services.