Evaluation Metrics, Scoring, Tracing and Feedback
Evaluation covers test sets, scoring, groundedness, relevance, safety, trace review, feedback loops, and comparison of model or prompt variants.
How to study for the Databricks Generative AI Engineer Associate exam
Treat each item as a production GenAI decision: define the task, prepare governed data, build the prompt or agent, package deployment, then evaluate, monitor, and control risk.
Core concepts
Concept 1
Evaluation should measure task success, groundedness, relevance, retrieval quality, safety, latency, cost, and business acceptance criteria.
Exam cue: Use an evaluation set before comparing prompts, retrievers, or models.
Concept 2
Traces connect model calls, prompts, retrieved chunks, tool calls, timing, and outputs so failures can be diagnosed.
Exam cue: Inspect traces when the output is wrong but the source of failure is unclear.
Concept 3
Feedback from users and reviewers should be tied to traces and evaluation examples so it can drive targeted improvements.
Exam cue: Separate retrieval failures from generation failures in scoring.
Risk pitfalls and guardrails
Using only subjective demos to decide whether a model is ready.
Guardrail: Avoid overusing agents, skipping retrieval evaluation, ignoring Unity Catalog permissions, or promoting prompt changes outside release control.
Averaging all scores without checking failures by topic or user segment.
Guardrail: Avoid overusing agents, skipping retrieval evaluation, ignoring Unity Catalog permissions, or promoting prompt changes outside release control.
Collecting thumbs-up feedback without linking it to prompt, retrieval, and trace data.
Guardrail: Avoid overusing agents, skipping retrieval evaluation, ignoring Unity Catalog permissions, or promoting prompt changes outside release control.
Memory anchors
Evaluation Set
An evaluation set contains representative inputs, expected behavior, references, or scoring criteria.
Groundedness Score
A groundedness score measures whether the answer is supported by retrieved evidence.
Relevance Score
A relevance score measures whether output addresses the user's actual request.
Safety Score
A safety score measures policy, harmful content, privacy, or misuse risk in input or output.
Trace Review
Trace review examines prompts, retrieval, tool calls, timings, and outputs to locate failure causes.
Variant Comparison
Variant comparison tests prompt, model, retriever, or tool changes against the same evaluation set.
Feedback Loop
A feedback loop turns user or reviewer signals into examples, fixes, tests, and monitored improvements.
Slice Analysis
Slice analysis checks performance by topic, user group, data source, language, or workflow segment.
Checkpoint rule
Do the check-up only after you can summarize each concept in one sentence and identify one dangerous pitfall from memory.
Knowledge Check (after reading)
Short check-up to confirm understanding of this module.
Check-up Questions
Three candidate models are evaluated for a support assistant. Which result should carry the most weight?
A candidate improves mean correctness from 0.86 to 0.88 but fails badly on a small high-risk compliance slice. What should the team do?
Answer all questions to submit.
Next step personalized recommendations
Continue learning
Move forward only after this module is stable.
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