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Which technique handles imbalanced datasets by adjusting class weights during training?
- Oversampling
- Undersampling
- Class Weighting
- All of these
Answer: All of these
Imbalance handling: oversampling (SMOTE) increases minority, undersampling reduces majority, class weighting adjusts loss function. Choice depends on data size and model type.
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.
Which metric is most appropriate for evaluating regression models?
- Accuracy
- Precision
- RMSE
- F1 Score
Answer: RMSE
Regression metrics: RMSE (Root Mean Square Error) penalizes large errors, MAE for robustness, R² for variance explained. Choice depends on business impact of errors.
The technique that converts categorical variables into numerical format for ML models is called ________ Encoding.
Answer: Label / One-Hot
Categorical encoding: label encoding for ordinal variables, one-hot for nominal, target encoding for high-cardinality. Choice impacts model performance and interpretability.
Which pattern enables stateless microservices to scale independently based on load?
- Stateless Design
- Horizontal Scaling
- Both A and B
- Neither
Answer: Both A and B
Stateless microservices store session data externally (Redis, database), enabling horizontal scaling by adding instances. Critical for elastic, resilient cloud applications.
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.
Which service enables serverless data processing triggered by events?
- Lambda
- Cloud Functions
- Both A and B
- Neither
Answer: Both A and B
Serverless compute (AWS Lambda, GCP Cloud Functions) executes code in response to events: API calls, file uploads, messages. Auto-scaling, pay-per-execution pricing. Critical for event-driven architectures.
The framework that standardizes security control implementation for cloud environments is called ________.
Answer: CSA CCM / Cloud Controls Matrix
Cloud Security Alliance Cloud Controls Matrix provides comprehensive security controls for cloud services, mapped to standards like ISO 27001, NIST. Critical for cloud security governance.
Which technology provides endpoint detection and response capabilities for advanced threats?
- EDR
- XDR
- Both A and B
- Neither
Answer: Both A and B
EDR focuses on endpoint telemetry and response; XDR extends to network, cloud, email for cross-domain threat detection. Critical for comprehensive threat protection.
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.
Which practice ensures ML models are monitored for performance degradation in production?
- Drift Detection
- Metric Dashboards
- Alerting
- All of these
Answer: All of these
Production monitoring: drift detection for data/concept changes, dashboards for real-time metrics, alerting for anomalies. Critical for maintaining ML system reliability and business value.
The technique that enables LLMs to generate multiple plausible responses and select the best is called ________ Sampling.
Answer: Beam / Nucleus
Advanced decoding: beam search explores multiple hypotheses, nucleus (top-p) sampling truncates low-probability tokens. Balance diversity and coherence in text generation.
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.
Which technique enables LLMs to maintain context across long conversations?
- Context Window
- Memory Mechanisms
- Both A and B
- Neither
Answer: Both A and B
Long-context handling: larger context windows (128K+ tokens), memory mechanisms (summarization, retrieval) maintain conversation history. Critical for chatbots and assistants.
Which framework provides guidelines for ethical AI development adopted by India?
- OECD AI Principles
- UNESCO AI Ethics
- Both A and B
- Neither
Answer: Both A and B
India endorses OECD AI Principles (2019) and UNESCO Recommendation on AI Ethics (2021). Both emphasize human rights, transparency, accountability, sustainability. Critical for AI governance.
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.
Which initiative provides a unified platform for startup mentoring, funding, and compliance?
- Startup India Hub
- DPIIT Recognition
- Both A and B
- Neither
Answer: Both A and B
Startup India ecosystem integrates: Hub for resources/networking, DPIIT recognition for benefits, NITI Aayog for policy. Supports innovation, job creation, global competitiveness.
The Indian policy for promoting electric vehicle adoption through subsidies is called ________ India.
Answer: FAME
FAME (Faster Adoption and Manufacturing of Electric Vehicles) India provides demand incentives for EVs and charging infrastructure. Phase-II focuses on public transport and shared mobility.
Which technique handles time series forecasting with seasonal patterns?
- ARIMA
- Prophet
- LSTM
- All of these
Answer: All of these
Time series methods: ARIMA for stationary series, Prophet for seasonality/holidays, LSTM for complex patterns. Choice depends on data characteristics and forecast horizon.
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.