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The metric that measures the proportion of positive predictions that are actually correct is called ________.
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.
Which visualization type is best for showing distribution of a continuous variable?
- Bar Chart
- Histogram
- Pie Chart
- Scatter Plot
Answer: Histogram
Histograms bin continuous data to show frequency distribution, revealing skewness, outliers, and modality. Critical for exploratory data analysis and statistical understanding.
Infrastructure as Code enables version-controlled, repeatable cloud resource provisioning.
- True
- False
Answer: True
IaC (Terraform, CloudFormation) defines infrastructure in declarative code, enabling version control, peer review, automated testing, and consistent deployments. Critical for cloud governance.
Which migration strategy involves re-architecting applications for cloud-native patterns?
- Rehost
- Replatform
- Refactor
- Repurchase
Answer: Refactor
Refactor (re-architect) redesigns applications for cloud-native: microservices, serverless, containers. Maximizes cloud benefits but requires significant effort. Critical for digital transformation.
The approach of keeping sensitive data on-premises while using cloud for compute is called ________ Cloud.
Answer: Hybrid
Hybrid cloud combines on-premises infrastructure with public cloud services. Enables data sovereignty compliance while leveraging cloud scalability. Critical for regulated industries.
Which pattern enables applications to run across multiple cloud providers for resilience?
- Multi-Cloud
- Hybrid Cloud
- Cloud Bursting
- All of these
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.
SOAR platforms automate repetitive security tasks and orchestrate response workflows.
- True
- False
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.
Which framework standardizes incident response phases: preparation, detection, containment, eradication, recovery?
- NIST SP 800-61
- ISO 27035
- Both A and B
- Neither
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.
The process of actively searching for threats that evade automated detection is called Threat ________.
Answer: Hunting
Threat hunting uses hypotheses, analytics, and analyst expertise to find hidden adversaries. Complements automated detection. Critical for advanced persistent threat (APT) defense.
Threat intelligence feeds provide indicators of compromise (IOCs) for proactive defense.
- True
- False
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.
Which SOC component correlates alerts from multiple sources to identify advanced threats?
- SIEM
- SOAR
- EDR
- All of these
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.
The technique that trains models on multiple tasks simultaneously to improve generalization is called ________ Learning.
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.
Which platform component manages ML model deployment and serving at scale?
- Feature Store
- Model Registry
- Serving Infrastructure
- All of these
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.
Quantization reduces LLM model size and inference latency by using lower-precision numbers.
- True
- False
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.
Which technique enables LLMs to execute code or use external tools for complex tasks?
- Function Calling
- Tool Use
- Both A and B
- Neither
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.
The practice of versioning datasets, models, and code for reproducibility is called ________ Management.
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.
Which practice ensures ML models are continuously monitored and retrained in production?
- CI/CD
- Model Drift Detection
- A/B Testing
- All of these
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.
Parameter-Efficient Fine-Tuning (PEFT) methods like LoRA reduce compute costs for adapting large models.
- True
- False
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.
Which method reduces LLM hallucinations by grounding responses in retrieved external knowledge?
- Fine-tuning
- RAG
- Prompt Engineering
- All of these
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.
The technique that enables LLMs to reason step-by-step by generating intermediate thoughts is called Chain-of-________.
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.