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Answer: Interdisciplinary Cyber Physical Systems / NM-ICPS
NM-ICPS (DST) funds R&D in AI, IoT, robotics, quantum technologies. Aims to build indigenous capabilities and innovation ecosystem. Critical for strategic technology questions.
Answer: Model-Based Imputation
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
Hyperparameter tuning: grid search, random search, Bayesian optimization. Critical for maximizing model performance but requires careful validation to avoid overfitting.
Answer: Both A and B
Box plots show median, quartiles, outliers; violin plots add density estimation. Both effective for comparing distributions across categories. Critical for exploratory data analysis.
Answer: Stratified
Stratified sampling maintains class proportions in train/test splits, critical for imbalanced classification tasks. Prevents evaluation bias from skewed splits.
Answer: Event-Driven Architecture
Event-driven architecture uses message brokers (Kafka, RabbitMQ) for decoupled, asynchronous service communication. Enables scalability, resilience, and real-time processing.
Answer: True
CDNs (CloudFront, Cloud CDN) distribute content to edge locations near users, reducing latency and origin load. Critical for global application performance and user experience.
Answer: Both A and B
Managed databases (AWS RDS, GCP Cloud SQL) handle provisioning, patching, backups, scaling. Reduces operational overhead vs self-managed databases. Critical for cloud application development.
Answer: Incident
Post-incident review captures lessons learned: what happened, response effectiveness, root causes, improvements. Critical for continuous security program maturity.
Answer: Both A and B
Email security combines: SPF/DKIM for authentication, ML for content analysis, heuristics for known patterns. Layered defense critical for phishing protection.
Answer: True
A/B testing randomly assigns users to model variants, measuring metrics like conversion, engagement. Critical for data-driven model iteration and business value validation.
Answer: Both A and B
MLOps tools: DVC versions data and models, MLflow tracks experiments and deployments. Combined with Git for end-to-end reproducibility. Critical for enterprise ML governance.
Answer: Reinforcement / RLHF
Reinforcement Learning from Human Feedback (RLHF) trains reward models from human preferences, then optimizes LLM to maximize rewards. Critical for aligning AI with human values.
Answer: True
Prompt engineering: clear instructions, examples, formatting, chain-of-thought. Low-cost method to improve LLM outputs without retraining. Critical for practical LLM applications.
Answer: Both A and B
Structured output: function calling defines schemas for LLM responses, parsing libraries validate/extract structured data. Critical for integrating LLMs with APIs and databases.
Answer: WTO / OECD
WTO e-commerce negotiations and OECD digital economy work address cross-border data flows, digital taxation, consumer protection. India actively participates in shaping global digital trade rules.
Answer: All of these
Effective tech policy: proportionate to actual risks, technology-neutral (focus on outcomes), risk-based (higher scrutiny for higher-risk activities). Critical for balanced policy design.
Answer: True
PLI for IT hardware (2021) provides incentives on incremental sales of laptops, tablets, servers manufactured in India. Aims to reduce imports, create jobs, strengthen electronics ecosystem.
Answer: Both A and B
CERT-In handles cyber incident response; NCIIPC (National Critical Information Infrastructure Protection Centre) protects critical infrastructure. Both under MeitY for cybersecurity governance.
Answer: Digital India / UPI
Digital India encompasses UPI, Aadhaar, DigiLocker for inclusive digital services. UPI enables instant, interoperable payments driving financial inclusion. Critical for digital economy questions.