Technology – True/False – GK Questions

ISO 20022 defines rich, structured data standards for payments, securities, trade finance. Enables interoperability, automation, compliance. Critical for financial infrastructure modernization.

ISO/IEC 27001 specifies ISMS requirements: risk assessment, controls, monitoring, improvement. Certification demonstrates commitment to security best practices.

As per IT Rules, documents issued by registered authorities through DigiLocker are legally valid and equivalent to physical originals under the Information Technology Act, 2000.

Interactive visualizations (Tableau, Power BI, Plotly) enable self-service analytics: filtering, sorting, drilling into details. Critical for data-driven decision making across organizations.

Data minimization (DPDP Act, GDPR) limits collection to what's necessary for specified purpose. Reduces privacy risks, storage costs, breach impact. Critical for responsible AI data practices.

Bias in training data or model design can lead to discriminatory outcomes. Mitigation: diverse datasets, fairness metrics, bias audits, human oversight. Critical for ethical AI deployment.

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: lakehouse pattern combines both.

Serverless (Lambda, Cloud Functions) auto-scales based on demand, charges per execution. Trade-offs: cold starts, vendor lock-in, execution limits. Critical for cost-optimized cloud design.

National Cyber Security Strategy (draft) addresses: critical infrastructure protection, cyber diplomacy, capacity building, public-private partnerships. Foundation for India's cyber defense posture.

Feature scaling (standardization, normalization) prevents features with large ranges from dominating model learning. Critical for distance-based algorithms and gradient descent optimization.

Continuous training pipelines monitor data drift, performance metrics, and trigger retraining with validation. Critical for maintaining ML system relevance in dynamic environments.

Instruction tuning fine-tunes LLMs on datasets of instructions and desired responses, improving task completion and helpfulness. Foundation for chat-optimized models.

Hyperparameter tuning: grid search, random search, Bayesian optimization. Critical for maximizing model performance but requires careful validation to avoid overfitting.

CDNs (CloudFront, Cloud CDN) distribute content to edge locations near users, reducing latency and origin load. Critical for global application performance and user experience.

A/B testing randomly assigns users to model variants, measuring metrics like conversion, engagement. Critical for data-driven model iteration and business value validation.

Prompt engineering: clear instructions, examples, formatting, chain-of-thought. Low-cost method to improve LLM outputs without retraining. Critical for practical LLM applications.

PLI for IT hardware (2021) provides incentives on incremental sales of laptops, tablets, servers manufactured in India. Aims to reduce imports, create jobs, strengthen electronics ecosystem.

Data leakage: using future data, target information, or test set in training/preprocessing. Causes overfitting and poor generalization. Critical to prevent via proper data splitting and pipeline design.

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

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.