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Zero Trust Architecture assumes breach and verifies every request regardless of network location.
- True
- False
Answer: True
Zero Trust implements: identity verification, least privilege, micro-segmentation, continuous monitoring. Assumes no implicit trust based on network location.
GitOps uses Git repositories as single source of truth for infrastructure and application deployments.
- True
- False
Answer: True
GitOps (ArgoCD, Flux) declaratively defines desired state in Git; operators sync cluster state. Benefits: audit trail, rollback via git revert, peer review via PRs.
Edge computing processes data closer to source to reduce latency for IoT applications.
- True
- False
Answer: True
Edge computing brings computation to network edge (routers, gateways), enabling real-time processing for IoT, autonomous vehicles, industrial automation. Complements cloud.
Digital twins create virtual replicas of physical systems for simulation and optimization.
- True
- False
Answer: True
Digital twins combine IoT, AI, simulation to model physical assets: factories, cities, power grids. Enables predictive maintenance, scenario testing. Critical for Industry 4.0.
ISO/IEC 20022 is a global standard for financial messaging and payment systems.
- True
- False
Answer: True
ISO 20022 defines rich, structured data standards for payments, securities, trade finance. Enables interoperability, automation, compliance. Critical for financial infrastructure modernization.
ISO/IEC 27001 provides requirements for information security management systems.
- True
- False
Answer: True
ISO/IEC 27001 specifies ISMS requirements: risk assessment, controls, monitoring, improvement. Certification demonstrates commitment to security best practices.
DigiLocker documents have legal validity equivalent to physical originals under the IT Act.
- True
- False
Answer: True
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 dashboards enable users to explore data dynamically through filters and drill-downs.
- True
- False
Answer: True
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 requires collecting only necessary personal information for AI training.
- True
- False
Answer: True
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.
Algorithmic bias can perpetuate societal inequalities if not actively mitigated in ML systems.
- True
- False
Answer: True
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 store raw, unprocessed data in native format, while data warehouses store structured, processed data.
- True
- False
Answer: True
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 architectures eliminate the need for capacity planning and infrastructure management.
- True
- False
Answer: True
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.
The National Cyber Security Strategy 2020 outlines India's approach to securing cyberspace.
- True
- False
Answer: True
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 ensures numerical features contribute equally to model training.
- True
- False
Answer: True
Feature scaling (standardization, normalization) prevents features with large ranges from dominating model learning. Critical for distance-based algorithms and gradient descent optimization.
Continuous training automatically retrains models when performance degrades or new data arrives.
- True
- False
Answer: True
Continuous training pipelines monitor data drift, performance metrics, and trigger retraining with validation. Critical for maintaining ML system relevance in dynamic environments.
Instruction tuning adapts LLMs to follow user instructions by training on prompt-response pairs.
- True
- False
Answer: True
Instruction tuning fine-tunes LLMs on datasets of instructions and desired responses, improving task completion and helpfulness. Foundation for chat-optimized models.
Hyperparameter tuning optimizes model performance by searching parameter combinations.
- True
- False
Answer: True
Hyperparameter tuning: grid search, random search, Bayesian optimization. Critical for maximizing model performance but requires careful validation to avoid overfitting.
Content Delivery Networks cache content at edge locations to reduce latency for global users.
- True
- False
Answer: True
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 compares model versions in production to measure business impact.
- True
- False
Answer: True
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 optimizes LLM performance by crafting effective input instructions.
- True
- False
Answer: True
Prompt engineering: clear instructions, examples, formatting, chain-of-thought. Low-cost method to improve LLM outputs without retraining. Critical for practical LLM applications.