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Answer: Both A and B
Managed databases (AWS RDS, GCP Cloud SQL) handle provisioning, patching, backups, scaling. Reduces operational overhead vs self-managed databases. Critical for cloud application development.
Answer: Both A and B
Email security combines: SPF/DKIM for authentication, ML for content analysis, heuristics for known patterns. Layered defense critical for phishing protection.
Answer: Both A and B
MLOps tools: DVC versions data and models, MLflow tracks experiments and deployments. Combined with Git for end-to-end reproducibility. Critical for enterprise ML governance.
Answer: Both A and B
Structured output: function calling defines schemas for LLM responses, parsing libraries validate/extract structured data. Critical for integrating LLMs with APIs and databases.
Answer: All of these
Effective tech policy: proportionate to actual risks, technology-neutral (focus on outcomes), risk-based (higher scrutiny for higher-risk activities). Critical for balanced policy design.
Answer: Both A and B
CERT-In handles cyber incident response; NCIIPC (National Critical Information Infrastructure Protection Centre) protects critical infrastructure. Both under MeitY for cybersecurity governance.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Answer: All of these
Proportion visualization: bar charts for clear comparison, pie charts for part-to-whole (limited categories), stacked bars for multiple groups. Choice depends on data and audience.
Answer: All of these
Safe deployment patterns: blue-green (instant switch), canary (gradual rollout), feature flags (toggle functionality). Combined for zero-downtime, low-risk releases. Critical for DevOps practices.
Answer: Glacier Deep Archive
Glacier Deep Archive offers lowest cost for long-term retention with 12+ hour retrieval times. Ideal for regulatory archives, backups. Trade-off: low storage cost vs high retrieval latency.
Answer: UEBA
UEBA (User and Entity Behavior Analytics) uses ML to establish behavioral baselines and detect anomalies indicating compromised accounts or insider threats. Critical for identity security.