True/False – GK Questions

National Cyber Security Strategy (draft) addresses: critical infrastructure protection, cyber diplomacy, capacity building, public-private partnerships. Foundation for India's cyber defense posture.

Feature scaling (standardization, normalization) prevents features with large ranges from dominating model learning. Critical for distance-based algorithms and gradient descent optimization.

Continuous training pipelines monitor data drift, performance metrics, and trigger retraining with validation. Critical for maintaining ML system relevance in dynamic environments.

Instruction tuning fine-tunes LLMs on datasets of instructions and desired responses, improving task completion and helpfulness. Foundation for chat-optimized models.

Hyperparameter tuning: grid search, random search, Bayesian optimization. Critical for maximizing model performance but requires careful validation to avoid overfitting.

CDNs (CloudFront, Cloud CDN) distribute content to edge locations near users, reducing latency and origin load. Critical for global application performance and user experience.

A/B testing randomly assigns users to model variants, measuring metrics like conversion, engagement. Critical for data-driven model iteration and business value validation.

Prompt engineering: clear instructions, examples, formatting, chain-of-thought. Low-cost method to improve LLM outputs without retraining. Critical for practical LLM applications.

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.

Data leakage: using future data, target information, or test set in training/preprocessing. Causes overfitting and poor generalization. Critical to prevent via proper data splitting and pipeline design.

Multi-region architecture distributes workload across geographic regions, enabling failover during regional disasters. Critical for business continuity and global user experience.

GitOps (ArgoCD, Flux) declaratively defines desired state in Git; operators sync cluster state. Benefits: audit trail, rollback via git revert, peer review via PRs. Critical for cloud-native MLOps.

Self-supervised learning (BERT, GPT pre-training) creates supervisory signals from data structure: mask prediction, next sentence prediction. Enables learning from vast unlabeled corpora.

NEP 2020 emphasizes computational thinking, coding, AI exposure from Class 6 onwards, with experiential learning and teacher training. Implemented via DIKSHA, CBSE updates, ATLs.

Cross-validation (k-fold) trains/evaluates on multiple data splits, reducing variance in performance estimates. Critical for small datasets and reliable model selection.

IaC scanners (Checkov, tfsec) analyze Terraform/CloudFormation for misconfigurations: public storage, weak IAM, unencrypted resources. Critical for shift-left security in DevOps.

Model registries (MLflow, SageMaker) manage model versions, parameters, metrics, and deployment history. Enables auditability, reproducibility, and rollback. Critical for enterprise MLOps.

Knowledge distillation trains compact models to mimic large teacher outputs, enabling efficient deployment on edge devices with minimal accuracy loss. Critical for scalable AI systems.

DPDP Act governs processing of digital personal data: (1) within India, and (2) outside India if offering goods/services or profiling individuals in India. Aligns with GDPR's extraterritorial scope.

Feature engineering: domain knowledge, transformations, interactions, encoding. Often more impactful than algorithm selection. Critical for successful machine learning projects.