Technology – Medium Level – GK Questions

Q: Knowledge distillation transfers capabilities from large teacher models to smaller student models.

True – 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.

Q: Which technique enables LLMs to generate code from natural language descriptions?

Both A and B – Code LLMs (Codex, CodeLlama) trained on code corpora enable natural language to code generation. Program synthesis automates code creation from specifications. Critical for developer productivity tools.

Q: The Digital Personal Data Protection Act 2023 applies extraterritorially to processing related to Indian individuals.

True – 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.

Q: Which body regulates telecommunications services and spectrum allocation in India?

TRAI – Telecom Regulatory Authority of India (TRAI) recommends policies, regulates tariffs, ensures QoS, and manages spectrum auctions. Critical for telecom sector governance questions.

Q: The Indian initiative for promoting semiconductor manufacturing is called India ________ Mission.

Semiconductor – 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.

Q: Feature engineering creates new input variables from raw data to improve model performance.

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

Q: Which metric measures the ability of a classifier to distinguish between classes?

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

Q: The technique that combines multiple models to improve prediction accuracy is called ________ Learning.

Ensemble – 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.

Q: Service mesh provides infrastructure-level observability, security, and traffic management for microservices.

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

Q: Which pattern enables automatic scaling of resources based on demand metrics?

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