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Answer: All of these
IaC (Terraform), Configuration Management (Ansible), and GitOps all use version-controlled code to define infrastructure/app state. Enables: repeatability, auditability, peer review, and automated testing. Critical for modern DevOps practices and cloud operations questions.
Answer: True
Data Mesh treats data as a product: domain teams own data products, with self-serve infrastructure and federated governance. Addresses scalability limits of centralized data teams. Principles: domain ownership, data as product, self-serve platform, federated governance. Critical for modern data architecture questions.
Answer: ConfigMap
ConfigMap stores configuration data (environment variables, config files) as key-value pairs for pods. Secret is for sensitive data (base64-encoded); PersistentVolume for storage; Namespace for resource isolation. Critical for Kubernetes configuration management questions.
Answer: Section 66
Section 66 prescribes punishment (up to 3 years + fine) for computer-related offenses: unauthorized access, data theft, introducing viruses. Section 43 covers civil liability; 66C is identity theft; 67 is obscene content. Critical for cyber law awareness in exams.
Answer: True
GitOps (ArgoCD, Flux) declaratively defines desired state in Git; operators sync cluster state to match. Benefits: audit trail, rollback via git revert, peer review via PRs. Critical for modern cloud-native deployment practices and infrastructure management questions.
Answer: Validity
Validity ensures data conforms to business rules, formats, and value ranges (e.g., email format, date ranges). Other dimensions: Accuracy (correctness), Completeness (no missing values), Consistency (across systems). Critical for data governance and quality management questions.
Answer: kube-scheduler
kube-scheduler assigns pods to nodes based on resource requests, constraints (node selectors, taints), and policies. Works with kubelet (node agent) and API server. Critical for understanding Kubernetes control plane architecture in cloud exams.
Answer: Homomorphic Encryption
Homomorphic Encryption allows computations on ciphertext, producing encrypted results that decrypt to correct output. Enables privacy-preserving cloud analytics, secure voting, and confidential AI. Still computationally intensive but advancing with research. Critical for privacy-enhancing technologies.
Answer: True
Digital India Act (proposed) aims to address: platform accountability, user rights, emerging tech governance, and algorithmic transparency. Complements DPDP Act and IT Rules. Critical for understanding India's evolving digital regulatory framework in policy exams.
Answer: Terraform
Terraform (HashiCorp) uses HCL declarative language to provision cloud resources across providers (AWS, Azure, GCP). Supports state management, modules, and plan/apply workflow. Ansible is configuration management; Jenkins is CI/CD; Docker is containerization. Critical for IaC questions.
Answer: Serverless / Athena
Serverless query engines (AWS Athena, Google BigQuery) enable SQL queries directly on data in object storage (S3, GCS) using schema-on-read. Eliminates ETL for ad-hoc analysis. Pay-per-query pricing. Critical for modern data lake architecture questions.
Answer: True
Authenticator apps (Google Authenticator, Authy) generate TOTP locally, immune to SIM swapping, SS7 attacks, and phishing that compromise SMS OTP. NIST recommends moving away from SMS for high-security applications. Critical for authentication best practices questions.
Answer: Data Protection Board of India
Data Protection Board of India is independent regulatory body established under DPDP Act 2023 to: adjudicate disputes, impose penalties, and enforce compliance. Distinct from CERT-In (cyber incidents) and MeitY (policy). Critical for data governance questions.
Answer: Chaos
Chaos Engineering (Netflix Chaos Monkey) proactively tests system resilience by injecting failures (network latency, instance termination) in controlled experiments. Builds confidence in fault tolerance. Requires monitoring, blast radius limits, and automated rollback. Critical for SRE practices.
Answer: Star Schema
Star Schema (data warehousing) uses denormalized fact tables linked to dimension tables for fast analytical queries. Optimizes read performance at cost of storage redundancy. Contrasts with 3NF (normalized for OLTP). Critical for BI and analytics architecture questions.
Answer: True
Serverless (AWS Lambda, Azure Functions) scales automatically: from zero to thousands of instances based on demand. Pay-per-execution pricing eliminates idle costs. Trade-offs: cold starts, execution time limits, vendor lock-in. Critical for cost-optimized cloud architecture questions.
Answer: RBI
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.
Answer: Automated Rollback
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.
Answer: True
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.
Answer: StatefulSet
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.