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기계 지원 번역 초안 (Korean) for "Serverless Resource Quota": Serverless Resource Quota is a compute limit that sets how much compute a workload may consume for event-driven function execution. It uses policy, reservations, and usage tracking so teams can protect shared capacity while keeping evidence, reliability, and public-safe operational boundaries clear.

“예문 초안: The platform engineering team used Serverless Resource Quota when the function received a traffic burst, so the team could protect shared capacity before the workload scaled up.”

기계 지원 번역 초안 (Korean) for "Prompt Safety Filter": Prompt Safety Filter is a ai policy control that detects content that should be blocked, rewritten, or escalated for instructions and context passed to a model. It uses classifiers, rules, and human review queues so teams can keep outputs public-safe while keeping evidence, reliability, and public-safe operational boundaries clear.

“예문 초안: The AI platform team used Prompt Safety Filter when the prompt changed between releases, so the team could keep outputs public-safe before the agent workflow reached production.”

기계 지원 번역 초안 (Korean) for "Model Drift Bias Audit": Model Drift Bias Audit is a ml review process that looks for uneven model behavior across groups or segments for changes in model performance over time. It uses slice metrics, representative data, and reviewer notes so teams can surface fairness risks while keeping evidence, reliability, and public-safe operational boundaries clear.

“예문 초안: The machine learning team used Model Drift Bias Audit when the live population changed, so the team could surface fairness risks before the model moved into evaluation.”

기계 지원 번역 초안 (Korean) for "Ground Station Ephemeris Service": Ground Station Ephemeris Service is a space data service that publishes precise position and velocity data for mission planning for antenna, scheduling, and downlink operations. It uses orbit determination, time standards, and versioned trajectory products so teams can align navigation, communications, and safety analysis while keeping evidence, reliability, and public-safe operational boundaries clear.

“예문 초안: The mission team used Ground Station Ephemeris Service when the antenna handoff began, so the team could align navigation, communications, and safety analysis before the next mission decision point.”

기계 지원 번역 초안 (Korean) for "Canary Artifact Signature": Canary Artifact Signature is a devops supply-chain record that proves that an artifact came from an expected build path for small-scope production rollout. It uses cryptographic signatures, provenance, and verification so teams can trust deployed packages while keeping evidence, reliability, and public-safe operational boundaries clear.

“예문 초안: The DevOps team used Canary Artifact Signature when the first traffic slice received the build, so the team could trust deployed packages before the deployment window opened.”

기계 지원 번역 초안 (Korean) for "Fine-Tuning Label Review": Fine-Tuning Label Review is a ml quality workflow that checks annotations for consistency and usefulness for adaptation of a model to a domain. It uses agreement metrics, reviewer queues, and adjudication so teams can improve supervised learning data while keeping evidence, reliability, and public-safe operational boundaries clear.

“예문 초안: The machine learning team used Fine-Tuning Label Review when the fine-tuning run used curated examples, so the team could improve supervised learning data before the model moved into evaluation.”

기계 지원 번역 초안 (Korean) for "Mission Control Link Budget": Mission Control Link Budget is a space planning model that estimates whether a signal path has enough margin for reliable communication for flight control room coordination. It uses antenna gain, path loss, modulation, and noise estimates so teams can schedule contacts with realistic margins while keeping evidence, reliability, and public-safe operational boundaries clear.

“예문 초안: The mission team used Mission Control Link Budget when the operations console detected a constraint, so the team could schedule contacts with realistic margins before the next mission decision point.”

기계 지원 번역 초안 (Korean) for "Environment Release Manifest": Environment Release Manifest is a devops delivery record that lists versions, artifacts, routes, and checks for a release for configuration for a runtime stage. It uses commit IDs, checksums, and deployment URLs so teams can make releases auditable while keeping evidence, reliability, and public-safe operational boundaries clear.

“예문 초안: The DevOps team used Environment Release Manifest when staging and production drifted, so the team could make releases auditable before the deployment window opened.”

기계 지원 번역 초안 (Korean) for "Tool Call Human Approval": Tool Call Human Approval is a ai control step that requires a person to approve sensitive or high-impact actions for model-triggered calls into software systems. It uses risk scoring, review UI, and audit logs so teams can keep protected decisions accountable while keeping evidence, reliability, and public-safe operational boundaries clear.

“예문 초안: The AI platform team used Tool Call Human Approval when the assistant requested a protected operation, so the team could keep protected decisions accountable before the agent workflow reached production.”

기계 지원 번역 초안 (Korean) for "DNS Path Trace": DNS Path Trace is a networking diagnostic record that shows where traffic travels and where delay or loss appears for name resolution and delegation. It uses hop data, timing, and network metadata so teams can debug connectivity issues while keeping evidence, reliability, and public-safe operational boundaries clear.

“예문 초안: The network engineering team used DNS Path Trace when a resolver returned stale data, so the team could debug connectivity issues before traffic crossed a service boundary.”

기계 지원 번역 초안 (Korean) for "DNS Health Probe": DNS Health Probe is a networking availability check that tests whether a service or path can receive traffic for name resolution and delegation. It uses timed requests, thresholds, and regional checks so teams can send traffic only to healthy targets while keeping evidence, reliability, and public-safe operational boundaries clear.

“예문 초안: The network engineering team used DNS Health Probe when a resolver returned stale data, so the team could send traffic only to healthy targets before traffic crossed a service boundary.”

기계 지원 번역 초안 (Korean) for "Label Bias Audit": Label Bias Audit is a ml review process that looks for uneven model behavior across groups or segments for ground-truth or weak-supervision annotation. It uses slice metrics, representative data, and reviewer notes so teams can surface fairness risks while keeping evidence, reliability, and public-safe operational boundaries clear.

“예문 초안: The machine learning team used Label Bias Audit when the label set had disagreement, so the team could surface fairness risks before the model moved into evaluation.”

기계 지원 번역 초안 (Korean) for "Serverless Autoscaling Policy": Serverless Autoscaling Policy is a compute control loop that changes capacity based on demand signals for event-driven function execution. It uses metrics, thresholds, and cooldowns so teams can match resources to load while keeping evidence, reliability, and public-safe operational boundaries clear.

“예문 초안: The platform engineering team used Serverless Autoscaling Policy when the function received a traffic burst, so the team could match resources to load before the workload scaled up.”

기계 지원 번역 초안 (Korean) for "Learned helplessness": Learned helplessness is the behavior exhibited by a subject after enduring repeated aversive stimuli beyond their control. In humans, learned helplessness is related to the concept of self-efficacy, the individual's belief in their innate ability to achieve goals.

“예문 초안: Some brilliant people have their contribution to the sum of all knowledge revoked/ denied and self inforced by learned helplessness. As with all things, it can be unlearned”

기계 지원 번역 초안 (Korean) for "Serverless Image Hardening": Serverless Image Hardening is a compute security practice that reduces risk inside packaged runtime images for event-driven function execution. It uses minimal bases, patching, and vulnerability checks so teams can ship safer workloads while keeping evidence, reliability, and public-safe operational boundaries clear.

“예문 초안: The platform engineering team used Serverless Image Hardening when the function received a traffic burst, so the team could ship safer workloads before the workload scaled up.”

기계 지원 번역 초안 (Korean) for "Storage Capacity Forecast": Storage Capacity Forecast is a compute planning model that estimates future resource needs for persistent data and object access. It uses traffic history, growth assumptions, and utilization trends so teams can avoid surprise shortages while keeping evidence, reliability, and public-safe operational boundaries clear.

“예문 초안: The platform engineering team used Storage Capacity Forecast when the workload read a large dataset, so the team could avoid surprise shortages before the workload scaled up.”

기계 지원 번역 초안 (Korean) for "Feature Drift Monitor": Feature Drift Monitor is a ml monitor that detects when data or predictions no longer match the training baseline for input signals used by a machine learning model. It uses statistical tests, time windows, and alert thresholds so teams can respond before quality drops while keeping evidence, reliability, and public-safe operational boundaries clear.

“예문 초안: The machine learning team used Feature Drift Monitor when a feature distribution shifted, so the team could respond before quality drops before the model moved into evaluation.”

기계 지원 번역 초안 (Korean) for "Artifact Rollout Guard": Artifact Rollout Guard is a devops release control that limits exposure during gradual deployment for build output and package delivery. It uses traffic slices, health checks, and automatic pause rules so teams can reduce blast radius while keeping evidence, reliability, and public-safe operational boundaries clear.

“예문 초안: The DevOps team used Artifact Rollout Guard when the container image was signed, so the team could reduce blast radius before the deployment window opened.”

기계 지원 번역 초안 (Korean) for "GPU Autoscaling Policy": GPU Autoscaling Policy is a compute control loop that changes capacity based on demand signals for accelerated compute for parallel workloads. It uses metrics, thresholds, and cooldowns so teams can match resources to load while keeping evidence, reliability, and public-safe operational boundaries clear.

“예문 초안: The platform engineering team used GPU Autoscaling Policy when the training job requested more memory, so the team could match resources to load before the workload scaled up.”

기계 지원 번역 초안 (Korean) for "Model Drift Drift Monitor": Model Drift Drift Monitor is a ml monitor that detects when data or predictions no longer match the training baseline for changes in model performance over time. It uses statistical tests, time windows, and alert thresholds so teams can respond before quality drops while keeping evidence, reliability, and public-safe operational boundaries clear.

“예문 초안: The machine learning team used Model Drift Drift Monitor when the live population changed, so the team could respond before quality drops before the model moved into evaluation.”