Medium Level – GK Questions

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.

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

ROC-AUC (Receiver Operating Characteristic - Area Under Curve) measures classifier performance across all thresholds, robust to class imbalance. Critical for evaluating binary classification models.

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.

Service mesh (Istio, Linkerd) deploys sidecar proxies for mTLS, retries, metrics, tracing. Decouples infrastructure concerns from application code. Critical for cloud-native architectures.

Auto-scaling adjusts resource count based on CPU, memory, or custom metrics; load balancing distributes traffic across instances. Combined for elastic, resilient cloud applications.

Incident response follows structured phases: preparation, detection, containment, eradication, recovery, lessons learned. Critical for minimizing breach impact and improving defenses.

Vulnerability scanners (Nessus, Qualys, OpenVAS) identify security weaknesses, prioritize by risk, and track remediation. Critical for proactive security posture management.

Canary deployment minimizes risk by gradually expanding model exposure, monitoring metrics, and rolling back if issues arise. Critical for safe ML system updates in production.

Data drift: input feature distribution changes; concept drift: relationship between features and target changes. Monitoring both enables timely model retraining. Critical for production ML reliability.

LoRA adds low-rank matrices to weight updates, reducing trainable parameters by 100-1000x vs full fine-tuning. Enables efficient adaptation of LLMs on consumer hardware. Critical for resource-constrained AI.

Retrieval-Augmented Generation (RAG) combines LLMs with vector database retrieval, grounding responses in verified sources. Reduces hallucinations and enables knowledge updates without retraining.

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.

Data localization (RBI payment data, DPDP Act provisions) ensures law enforcement access, reduces foreign surveillance risks. Balances sovereignty with global data flow needs. Critical for policy questions.

Digital India encourages FOSS adoption for cost-effectiveness, security, and vendor independence. MeitY's FOSS policy provides guidelines for government use. Critical for sustainable digital governance.

#AIforAll strategy (NITI Aayog) focuses on leveraging AI for inclusive growth across healthcare, agriculture, education. Emphasizes research, skilling, data governance, and ethical principles.

XAI methods (SHAP, LIME, attention visualization) provide interpretable explanations for AI decisions. Critical for regulated sectors (finance, healthcare) and user acceptance.

Scatter plots display individual data points for two variables, revealing correlations, clusters, and outliers. Foundation for regression analysis and exploratory data science.

F1 Score = 2 * (Precision * Recall) / (Precision + Recall). Harmonic mean balancing false positives and false negatives. Critical for evaluating models on imbalanced datasets.

Data splitting strategies: holdout (simple split), cross-validation (multiple folds), bootstrap (resampling). Critical for reliable model evaluation and generalization assessment.