Technology – Medium Level – GK Questions

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

Serverless compute (AWS Lambda, GCP Cloud Functions) executes code in response to events: API calls, file uploads, messages. Auto-scaling, pay-per-execution pricing. Critical for event-driven architectures.

Cloud Security Alliance Cloud Controls Matrix provides comprehensive security controls for cloud services, mapped to standards like ISO 27001, NIST. Critical for cloud security governance.

EDR focuses on endpoint telemetry and response; XDR extends to network, cloud, email for cross-domain threat detection. Critical for comprehensive threat protection.

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.

Production monitoring: drift detection for data/concept changes, dashboards for real-time metrics, alerting for anomalies. Critical for maintaining ML system reliability and business value.

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

Long-context handling: larger context windows (128K+ tokens), memory mechanisms (summarization, retrieval) maintain conversation history. Critical for chatbots and assistants.

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

Startup India ecosystem integrates: Hub for resources/networking, DPIIT recognition for benefits, NITI Aayog for policy. Supports innovation, job creation, global competitiveness.

FAME (Faster Adoption and Manufacturing of Electric Vehicles) India provides demand incentives for EVs and charging infrastructure. Phase-II focuses on public transport and shared mobility.

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

Proportion visualization: bar charts for clear comparison, pie charts for part-to-whole (limited categories), stacked bars for multiple groups. Choice depends on data and audience.

Regularization (L1/Lasso, L2/Ridge, Dropout) adds penalty terms to loss function or randomly drops units during training. Prevents overfitting by discouraging complex models. Critical for generalization.

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

Glacier Deep Archive offers lowest cost for long-term retention with 12+ hour retrieval times. Ideal for regulatory archives, backups. Trade-off: low storage cost vs high retrieval latency.

Red teaming conducts adversarial simulations (cyber, physical, social) to identify gaps in detection, response, and resilience. Distinct from vulnerability scanning. Critical for mature security programs.

UEBA (User and Entity Behavior Analytics) uses ML to establish behavioral baselines and detect anomalies indicating compromised accounts or insider threats. Critical for identity security.

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

ML security testing: adversarial examples test robustness, penetration testing finds implementation flaws. Critical for secure AI deployment in sensitive domains.