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Answer: Slicing
Network Slicing creates multiple logical networks on shared physical 5G infrastructure, each optimized for specific requirements: ultra-reliable low latency (autonomous vehicles), massive IoT (sensors), enhanced mobile broadband (video). Enables customized QoS, security, and management per slice. Critical for monetizing 5G across industries.
Answer: Transparency
Transparency (Explainable AI/XAI) ensures AI decisions are interpretable by humans, enabling trust, debugging, and regulatory compliance. Techniques: LIME, SHAP, attention visualization. Critical in high-stakes domains: healthcare diagnosis, loan approvals, criminal justice. Balances model complexity with interpretability needs.
Answer: Regulatory Sandbox
RBI Regulatory Sandbox enables fintech startups and banks to test new products/services with real customers under relaxed regulations for limited period/time/scale. Reduces time-to-market, fosters innovation while managing risks. Themes include retail payments, KYC, lending, cybersecurity. Successful pilots may lead to regulatory framework updates.
Answer: True
Model-based imputation (MICE, Bayesian) models relationships between variables to predict missing values, accounting for uncertainty. More robust than simple methods for complex missingness patterns.
Answer: True
Code LLMs enable natural language to code generation, debugging, documentation.
Answer: True
Advanced decoding: beam search explores multiple hypotheses, nucleus sampling truncates low-probability tokens. Balance diversity and coherence.
Answer: Both A and B
Spiking neurons fire only when needed; event-driven processing handles only changes. Combined enable orders-of-magnitude better energy efficiency than GPUs for edge AI.
Answer: DNA / Molecular
DNA computing leverages biochemical reactions for parallel problem-solving. Research stage with potential for combinatorial optimization, cryptography, archival storage.
Answer: Grover's Algorithm
Grover's Algorithm searches unsorted database of N items in O(√N) queries vs O(N) classically. Applications: optimization, cryptography, machine learning enhancement.
Answer: Differential
Differential Privacy adds mathematical noise to model outputs, providing provable privacy guarantees against membership inference and model inversion attacks.
Answer: Backdoor Attack
Backdoor attacks inject malicious patterns during training that cause targeted misclassification when trigger present. Critical threat requiring robust training data validation.
Answer: Both A and B
Software-Defined Perimeter (SDP) creates identity-based perimeters; micro-segmentation isolates workloads. Both implement Zero Trust by limiting access to authenticated, authorized entities only.
Answer: Cellular / Biological
Cellular computing uses biochemical reactions in cells for parallel computation. Early research stage.
Answer: Molecular / Biochemical
Molecular computing uses DNA, proteins, or cellular processes for massive parallelism. Research stage with potential for ultra-low power, complex problem solving.
Answer: True
QEC codes (surface code, Shor code) encode logical qubits across many physical qubits to detect/correct errors. Critical for fault-tolerant quantum computing.
Answer: Randomized
Randomized smoothing provides certified robustness guarantees against adversarial perturbations by averaging predictions over noisy inputs.
Answer: Both A and B
Neuromorphic chips use spiking neurons that fire only when needed, and event-driven computation that processes only changes. Critical for edge AI with tight power budgets.
Answer: DNA / Molecular
DNA data storage offers extreme density (exabytes/gram) and longevity (thousands of years). Research ongoing at IITs and global labs for archival applications.
Answer: Shor's Algorithm
Shor's Algorithm factors integers in polynomial time on quantum computers, threatening RSA encryption. Requires fault-tolerant quantum computers not yet available at scale.
Answer: Differential Privacy / DP
DP-SGD adds noise to gradients during training, providing mathematical privacy guarantees. Critical for training ML models on sensitive data while preventing membership inference.