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Answer: Differential Privacy / DP
DP-SGD adds noise to gradients during training, providing mathematical privacy guarantees. Critical for training ML models on sensitive data while preventing membership inference.
Answer: True
Model inversion exploits model confidence scores to infer sensitive attributes of training data. Mitigation: differential privacy, output perturbation, access controls.
Answer: All of these
Adversarial defenses: adversarial training includes perturbed examples, defensive distillation smooths outputs, randomized smoothing adds noise. All improve robustness against adversarial attacks.
Answer: Container / Image
Container image scanning identifies known vulnerabilities, misconfigurations, and secrets in images before deployment. Critical for secure CI/CD pipelines in cloud-native environments.
Answer: True
Service mesh (Istio, Linkerd) automatically encrypts service-to-service traffic with mTLS, provides authentication, authorization, and observability without application code changes.
Answer: All of these
Kubernetes security: Pod Security Policies restrict privileges, Network Policies control traffic, Resource Limits prevent DoS. Combined for defense-in-depth in container environments.
Answer: Device Posture
Device posture assessment evaluates endpoint security: OS patches, antivirus status, encryption, compliance. Critical for Zero Trust access decisions in hybrid environments.
Answer: True
Micro-segmentation creates granular security policies around individual workloads/applications, containing breaches and preventing attackers from moving laterally across the network.
Answer: All of these
Zero Trust combines: never trust always verify (continuous authentication), assume breach (detect/contain threats), least privilege (minimum access). All principles work together for comprehensive security.
Answer: Thought
Chain-of-Thought prompting generates reasoning steps.
Answer: True
Fine-tuning updates weights on task-specific data.
Answer: Transformer
Transformer uses self-attention for parallel sequence processing.
Answer: Clustering
Clustering discovers natural groupings in unlabeled data.
Answer: True
Feature engineering: transformations, interactions, encoding.
Answer: Time Series Forecasting
Time series forecasting models temporal patterns.
Answer: Serverless
Serverless query engines query object storage directly.
Answer: True
Data lakes store raw data; warehouses store curated data.
Answer: Parquet
Parquet is columnar storage optimized for analytics.
Answer: Stream
Stream processing handles continuous data flows.
Answer: True
CDC streams database operations for real-time analytics.