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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.
The Production Linked Incentive scheme for IT hardware aims to boost domestic manufacturing of laptops and servers.
- True
- False
Answer: True
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 occurs when test data influences training, leading to overly optimistic performance estimates.
- True
- False
Answer: True
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 deployment improves application availability by protecting against regional outages.
- True
- False
Answer: True
Multi-region architecture distributes workload across geographic regions, enabling failover during regional disasters. Critical for business continuity and global user experience.
GitOps uses Git repositories as single source of truth for infrastructure and ML 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. Critical for cloud-native MLOps.
Self-supervised learning enables models to learn from unlabeled data by predicting masked tokens.
- True
- False
Answer: True
Self-supervised learning (BERT, GPT pre-training) creates supervisory signals from data structure: mask prediction, next sentence prediction. Enables learning from vast unlabeled corpora.
The National Education Policy 2020 mandates integration of coding and AI literacy from middle school.
- True
- False
Answer: True
NEP 2020 emphasizes computational thinking, coding, AI exposure from Class 6 onwards, with experiential learning and teacher training. Implemented via DIKSHA, CBSE updates, ATLs.
Cross-validation provides more reliable model evaluation than single train-test split.
- True
- False
Answer: True
Cross-validation (k-fold) trains/evaluates on multiple data splits, reducing variance in performance estimates. Critical for small datasets and reliable model selection.
Infrastructure as Code scanning tools detect security misconfigurations before deployment.
- True
- False
Answer: True
IaC scanners (Checkov, tfsec) analyze Terraform/CloudFormation for misconfigurations: public storage, weak IAM, unencrypted resources. Critical for shift-left security in DevOps.
Model registries track versions, metadata, and lineage of ML models for governance.
- True
- False
Answer: True
Model registries (MLflow, SageMaker) manage model versions, parameters, metrics, and deployment history. Enables auditability, reproducibility, and rollback. Critical for enterprise MLOps.
Knowledge distillation transfers capabilities from large teacher models to smaller student models.
- True
- False
Answer: True
Knowledge distillation trains compact models to mimic large teacher outputs, enabling efficient deployment on edge devices with minimal accuracy loss. Critical for scalable AI systems.