Medium Level – GK Questions

RBI's Digital Lending Guidelines (2022) regulate: entity eligibility (banks/NBFCs only), direct fund flow, standardized disclosures, cooling-off period, and grievance redressal. Aims to protect consumers from predatory apps and data misuse. Critical for fintech compliance questions.

Automated Rollback uses monitoring alerts (error rate, latency) to trigger immediate reversion to previous stable version. Reduces MTTR and blast radius of bad releases. Implemented via CI/CD pipelines and SRE runbooks. Critical for production reliability practices.

CDC captures insert/update/delete operations from database logs (binlog, WAL) and streams to targets (Kafka, data lake). Enables real-time analytics, cache invalidation, and event-driven architectures. Tools: Debezium, AWS DMS. Critical for modern data integration patterns.

StatefulSet manages stateful applications: provides stable pod names (pod-0, pod-1), persistent storage, and ordered deployment/scaling. Used for databases, Kafka, etc. Deployment is for stateless apps; DaemonSet runs one pod per node; ReplicaSet manages pod replicas.

Zero Trust Security assumes breach and verifies every request regardless of source. Implements: identity verification, least privilege, micro-segmentation, and continuous monitoring. Contrasts with perimeter-based 'castle-and-moat' model. Critical for modern enterprise security architecture.

Blue-Green deployment: run current version (Blue) and new version (Green) in parallel; switch traffic to Green after validation. Enables instant rollback by reverting traffic. Reduces risk vs in-place updates. Critical for zero-downtime release strategies in DevOps.

Apache Spark provides in-memory distributed computing for big data: batch, streaming, ML, graph processing. Faster than Hadoop MapReduce due to in-memory processing. Supports Scala, Python, SQL. Critical for big data engineering and analytics questions.

Containerization (Docker, containerd) packages apps with runtime, libraries, and config into isolated, portable containers. Enables consistent deployment across environments. Contrasts with VMs (heavier, OS-level isolation). Foundation of cloud-native architecture. Critical for modern deployment questions.

Behavioral Analytics uses ML to establish baselines of normal behavior and flag deviations indicating threats (insider threats, zero-days). Complements signature-based detection. Implemented in UEBA, NDR solutions. Critical for next-gen SOC and threat hunting questions.

IT Rules 2021 mandate Significant Social Media Intermediaries (50L+ users) to appoint: Chief Compliance Officer, Nodal Contact Person, and Resident Grievance Officer. Also require traceability of first originator for certain messages. Critical for platform governance and compliance questions.

Lead Time for Changes (DORA metric) measures time from code commit to successful production deployment. Key indicator of delivery speed and process efficiency. Other DORA metrics: Deployment Frequency, Change Failure Rate, MTTR. Critical for DevOps performance questions.

ETL (Extract, Transform, Load) is foundational data integration process: extract from sources, transform (clean, enrich, aggregate), load to target (warehouse, lake). Modern variants: ELT (load first, transform later) for cloud data platforms. Critical for data pipeline questions.

Deployment manages replica sets of pods: defines replicas, update strategy, rollback, and scaling. Ensures desired state is maintained. Pod is smallest deployable unit; Service exposes pods; ConfigMap stores configuration. Critical for Kubernetes administration questions.

Section 10 of DPDP Act mandates Significant Data Fiduciaries (based on data volume, sensitivity, risk) to appoint DPO, conduct audits, and implement grievance redressal. Enhances accountability for high-risk data processing. Critical for compliance and governance questions.

Observability enables understanding system internal state from external outputs: metrics (Prometheus), logs (ELK), traces (Jaeger). Goes beyond monitoring by enabling debugging of unknown issues. Critical for SRE practices and cloud operations questions in technical exams.

Service mesh (Istio, Linkerd, Consul) deploys sidecar proxies to handle service-to-service communication: mTLS encryption, traffic splitting, retries, metrics, and distributed tracing. Decouples infrastructure concerns from application code. Critical for cloud-native architecture questions.

Identity federation attacks exploit trust between identity providers (IdP) and service providers (SP) in SSO setups. Attackers compromise IdP to gain access to multiple federated services. Mitigation: MFA, short-lived tokens, strict trust policies. Critical for cloud security architecture questions.

Reserve Bank of India (RBI) regulates payment systems under Payment and Settlement Systems Act 2007. Oversees UPI, cards, wallets, PPIs, and fintech innovations. Issues guidelines for security, interoperability, and consumer protection. Critical for banking and fintech policy questions.

CI/CD pipeline automates: Continuous Integration (code merge + testing), Continuous Delivery (release-ready artifacts), Continuous Deployment (auto-production release). Tools: Jenkins, GitLab CI, GitHub Actions. Critical for agile software delivery and DevOps questions in technical exams.

Data lakes (S3, ADLS) store raw structured/unstructured data for flexible analysis; data warehouses (Snowflake, Redshift) store curated, schema-on-write data for BI. Modern architectures use lakehouse pattern combining both. Critical for understanding data platform design in analytics exams.