Study Databricks GENAI-ASSOC Metrics, Judges, and Tracing: key concepts, common traps, and exam decision cues.
This lesson is about evidence, not intuition. The current Databricks guide now explicitly names evaluation judges, tracing, MLflow scoring, custom scorers, and SME feedback, which means you need a clearer evaluation vocabulary than older prep materials required.
| Need | Better first instinct |
|---|---|
| compare model choices quantitatively | deployment-relevant evaluation metrics |
| review agent behavior in detail | tracing and scoring |
| use a judge that needs known answers | ground-truth-based evaluation judge |
| improve the app with domain insight | SME feedback loop |
| Layer | What it really gives you |
|---|---|
| metrics | structured comparison across candidates or runs |
| judges and scorers | a rubric or reference-based quality signal |
| tracing | visibility into tool use, reasoning, and chain flow |
| SME feedback | domain expertise that automated checks often miss |
| Trap | Better rule |
|---|---|
| relying on “the answer sounded good” | use metrics, judges, scorers, and traces |
| using one metric for every deployment scenario | metrics must match the use case |
| treating SME feedback as optional | domain experts often catch failures automated checks miss |
A team knows final answers are weak, but cannot tell whether the failure came from tool choice, retrieval ordering, or agent path execution. Which evaluation surface is strongest first?
Correct answer: A. When the issue is “how did the system behave,” tracing is the first surface that exposes the actual chain behavior.
Evaluation questions usually reward choosing the signal that matches the failure. If a judge needs known correct references, think ground-truth-dependent evaluation. If you need to understand how the system reached an answer or used tools, think tracing. If automated metrics miss business nuance, bring in SME feedback. The weak answer usually expects one metric family to catch every failure.