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Which technique identifies compromised accounts by analyzing login patterns and behaviors?
- UEBA
- SIEM
- EDR
- All of these
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.
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.
Which practice ensures ML models are tested for security vulnerabilities before deployment?
- Adversarial Testing
- Penetration Testing
- Both A and B
- Neither
Answer: Both A and B
ML security testing: adversarial examples test robustness, penetration testing finds implementation flaws. Critical for secure AI deployment in sensitive domains.
The technique that enables LLMs to reason about visual inputs by combining vision and language models is called ________.
Answer: Multimodal Learning
Multimodal models (CLIP, LLaVA) process text, images, audio jointly, enabling visual question answering, image captioning, and cross-modal retrieval. Critical for next-gen AI applications.
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.
Which technique enables LLMs to generate code from natural language descriptions?
- Code LLMs
- Program Synthesis
- Both A and B
- Neither
Answer: Both A and B
Code LLMs (Codex, CodeLlama) trained on code corpora enable natural language to code generation. Program synthesis automates code creation from specifications. Critical for developer productivity tools.
The Digital Personal Data Protection Act 2023 applies extraterritorially to processing related to Indian individuals.
- True
- False
Answer: True
DPDP Act governs processing of digital personal data: (1) within India, and (2) outside India if offering goods/services or profiling individuals in India. Aligns with GDPR's extraterritorial scope.
Which body regulates telecommunications services and spectrum allocation in India?
- TRAI
- DoT
- TSDSI
- MeitY
Answer: TRAI
Telecom Regulatory Authority of India (TRAI) recommends policies, regulates tariffs, ensures QoS, and manages spectrum auctions. Critical for telecom sector governance questions.
The Indian initiative for promoting semiconductor manufacturing is called India ________ Mission.
Answer: Semiconductor
India Semiconductor Mission implements ₹76,000 crore incentive scheme for fabs, display fabs, ATMP, design. Partnerships with global players announced. Critical for tech sovereignty questions.
Which technique handles high-cardinality categorical variables efficiently?
- One-Hot Encoding
- Target Encoding
- Embedding Layers
- All of these
Answer: All of these
High-cardinality handling: one-hot for low cardinality, target encoding for medium, embeddings for high (deep learning). Choice depends on model type and cardinality. Critical for feature preprocessing.
Feature engineering creates new input variables from raw data to improve model performance.
- True
- False
Answer: True
Feature engineering: domain knowledge, transformations, interactions, encoding. Often more impactful than algorithm selection. Critical for successful machine learning projects.
Which metric measures the ability of a classifier to distinguish between classes?
- Accuracy
- Precision
- ROC-AUC
- F1 Score
Answer: ROC-AUC
ROC-AUC (Receiver Operating Characteristic - Area Under Curve) measures classifier performance across all thresholds, robust to class imbalance. Critical for evaluating binary classification models.
The technique that combines multiple models to improve prediction accuracy is called ________ Learning.
Answer: Ensemble
Ensemble methods (bagging, boosting, stacking) combine weak learners to reduce variance, bias, or improve predictions. Random Forests and Gradient Boosting are widely used ensemble techniques.
Which disaster recovery strategy maintains a fully operational standby environment in another region?
- Backup and Restore
- Pilot Light
- Warm Standby
- Multi-Site Active-Active
Answer: Multi-Site Active-Active
Active-active runs full workload in multiple regions simultaneously, enabling instant failover with zero RPO/RTO. Highest cost but maximum resilience. Critical for mission-critical systems.
Service mesh provides infrastructure-level observability, security, and traffic management for microservices.
- True
- False
Answer: True
Service mesh (Istio, Linkerd) deploys sidecar proxies for mTLS, retries, metrics, tracing. Decouples infrastructure concerns from application code. Critical for cloud-native architectures.
Which pattern enables automatic scaling of resources based on demand metrics?
- Auto-scaling
- Load Balancing
- Both A and B
- Neither
Answer: Both A and B
Auto-scaling adjusts resource count based on CPU, memory, or custom metrics; load balancing distributes traffic across instances. Combined for elastic, resilient cloud applications.
The process of containing and eradicating threats after detection is called Incident ________.
Answer: Response
Incident response follows structured phases: preparation, detection, containment, eradication, recovery, lessons learned. Critical for minimizing breach impact and improving defenses.
Which tool automates vulnerability scanning and prioritization for remediation?
- Nessus
- Qualys
- Both A and B
- Neither
Answer: Both A and B
Vulnerability scanners (Nessus, Qualys, OpenVAS) identify security weaknesses, prioritize by risk, and track remediation. Critical for proactive security posture management.
Canary deployments release new model versions to small user subsets before full rollout.
- True
- False
Answer: True
Canary deployment minimizes risk by gradually expanding model exposure, monitoring metrics, and rolling back if issues arise. Critical for safe ML system updates in production.
Which practice detects when model performance degrades due to changing data distributions?
- Data Drift Detection
- Concept Drift Detection
- Both A and B
- Neither
Answer: Both A and B
Data drift: input feature distribution changes; concept drift: relationship between features and target changes. Monitoring both enables timely model retraining. Critical for production ML reliability.