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Answer: Precision
Precision = TP / (TP + FP). Critical for imbalanced classification tasks where false positives are costly (fraud detection, medical diagnosis). Complements recall for comprehensive evaluation.
Answer: Histogram
Histograms bin continuous data to show frequency distribution, revealing skewness, outliers, and modality. Critical for exploratory data analysis and statistical understanding.
Answer: True
IaC (Terraform, CloudFormation) defines infrastructure in declarative code, enabling version control, peer review, automated testing, and consistent deployments. Critical for cloud governance.
Answer: Refactor
Refactor (re-architect) redesigns applications for cloud-native: microservices, serverless, containers. Maximizes cloud benefits but requires significant effort. Critical for digital transformation.
Answer: Hybrid
Hybrid cloud combines on-premises infrastructure with public cloud services. Enables data sovereignty compliance while leveraging cloud scalability. Critical for regulated industries.
Answer: Multi-Cloud
Multi-cloud architecture distributes workloads across AWS, Azure, GCP to avoid vendor lock-in, improve resilience, and leverage best-of-breed services. Critical for enterprise cloud strategy.
Answer: True
SOAR (Security Orchestration, Automation, and Response) automates playbooks: alert triage, enrichment, containment actions. Reduces analyst workload and response time. Critical for SOC efficiency.
Answer: Both A and B
NIST SP 800-61 and ISO 27035 provide structured incident response methodologies. Critical for consistent, effective handling of security incidents in enterprises.
Answer: Hunting
Threat hunting uses hypotheses, analytics, and analyst expertise to find hidden adversaries. Complements automated detection. Critical for advanced persistent threat (APT) defense.
Answer: True
Threat intelligence (commercial, open-source, ISACs) provides IOCs: malicious IPs, domains, hashes, TTPs. Enables proactive blocking and hunting. Critical for mature security operations.
Answer: SIEM
SIEM (Security Information and Event Management) aggregates logs, correlates events, and detects anomalies across network, endpoint, and application sources. Foundation for modern SOC operations.
Answer: Multi-Task
Multi-task learning shares representations across related tasks, improving sample efficiency and generalization. Critical for building versatile AI systems with limited labeled data per task.
Answer: All of these
MLOps platforms integrate: feature stores for consistent data, model registries for versioning, serving infrastructure for scalable inference. Critical for production ML system reliability.
Answer: True
Quantization (INT8, INT4) reduces model size 2-4x with minimal accuracy loss, enabling deployment on edge devices and reducing cloud inference costs. Critical for scalable LLM serving.
Answer: Both A and B
Function calling/tool use enables LLMs to invoke APIs, run code, query databases for tasks beyond text generation. Critical for building agentic AI systems that interact with real-world systems.
Answer: Artifact / Model Registry
Artifact management (MLflow, DVC) tracks datasets, model versions, hyperparameters, and code commits. Enables reproducibility, auditability, and rollback in ML workflows. Critical for enterprise MLOps.
Answer: All of these
MLOps combines: CI/CD for automated deployment, drift detection for performance monitoring, A/B testing for validation. Critical for maintaining ML system reliability and business value over time.
Answer: True
PEFT methods (LoRA, Adapter, Prefix-tuning) update small subset of parameters or add lightweight modules, reducing compute/memory vs full fine-tuning. Critical for resource-constrained LLM adaptation.
Answer: RAG
Retrieval-Augmented Generation (RAG) combines LLMs with external knowledge retrieval, reducing hallucinations by grounding responses in verified sources. Critical for factual accuracy in enterprise AI applications.
Answer: Thought
Chain-of-Thought prompting encourages LLMs to generate reasoning steps before final answer, improving performance on complex reasoning tasks. Critical for enhancing LLM reliability in high-stakes applications.