Observability & Evaluation · Data artifact

Strategic Evaluation Sampling Policy

Data artifactObservability & EvaluationObservability & Evaluationarc:StrategicEvaluationSamplingPolicy

An evaluation sampling policy that combines a random sample with oversampling of errored (and edge-case) traces.

Responsibility. Selects a small evaluation sample biased toward failing and boundary interactions.

Also known as: Error-oversampling policy, 5% strategic sample, Uncertainty-prioritised feedback sampling

Variant of Evaluation Sampling Policy abstract

When to choose. Choose for production systems where evaluation should focus on problem areas; recommended default (5-10% with error oversampling).

Direct neighbourhood (hover for relationship types)

Relationships

configures structural

Feedback Collector abstract Ch10.5

alternative to variability

Quantitative guidance

As stated by the sources; verify before use.

5% strategic sample (errors + random) costs ~0.1-0.3% of inference cost with no production latency impact (Ref8.03).

Classification

Patterns: Error oversamplingStratified sampling
Quality attributes: Cost efficiencyReliability (ISO/IEC 25010 | NIST AI RMF: valid and reliable)
Risks mitigated: Rare failures missed by uniform sampling

Sources

Ch10.5: T. Nguyen, "Human-over-the-Loop," in Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam, 1st ed. 2026, ch. 10.5. ISBN: 9798244538229.
Ref8.03: "Agent Evaluation Frameworks and Metrics," unpublished reference note (03-Agent-Evaluation-Frameworks.md), Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam supplementary materials, 2026. unpublished note