Databricks-GenAI-Assoc Evaluation and Monitoring Practice Question
A media company uses an LLM-as-a-judge evaluation pipeline on Databricks to score a summarization assistant nightly. The judge is the same model family as the assistant and was given a rubric that rewards stylistic fluency. Over two months, nightly scores climb steadily while editor spot-checks find summaries increasingly omit key facts. Which corrective action best restores the evaluation's ability to detect this regression?
⚠ Common exam trap
The trap here is treating rising judge scores as improving quality when the judge and the assistant share a family and the rubric rewards fluency, so the metric drifts upward while factual omissions grow.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Revise the judge rubric to weight factual coverage against the source document, add a small human-labeled calibration set, and periodically verify judge agreement with editors.
The rising scores reflect a judge that rewards fluency because it shares a model family with the assistant and was given a style-oriented rubric, so the instrument itself is blind to factual omission. Restoring detection requires redefining the rubric around factual coverage of the source, anchoring it with human-labeled examples, and monitoring judge-human agreement so drift in the judge is caught before it masks another regression.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Revise the judge rubric to weight factual coverage against the source document, add a small human-labeled calibration set, and periodically verify judge agreement with editors.
Why this is correct
The judge is self-preferring a fluent sibling model and rewarding style over substance, so the rubric must explicitly score factual coverage and be validated against human labels. A calibration set and periodic agreement checks detect when the judge drifts away from editorial standards, which is the only way the nightly signal can be trusted again.
- ✗
Increase the judge's temperature so its scores vary more between runs and regressions become statistically visible.
Why it's wrong here
Higher temperature adds noise, not sensitivity to omitted facts. The problem is a systematic bias toward fluency in the rubric and model pairing, which more randomness cannot correct; it would simply make scores less reproducible while the underlying blind spot for missing content persists, delaying detection even further.
- ✗
Run the judge more frequently, moving from nightly to hourly scoring, so trends are captured with finer granularity.
Why it's wrong here
Frequency improves trend resolution but not measurement validity. If the judge systematically equates fluency with quality, hourly scoring produces the same upward drift faster and with more compute cost. The regression in factual omission would remain invisible because the instrument itself does not penalize omission.
- ✗
Switch the judge to the largest available frontier model and remove the rubric so the judge can apply its own judgment freely.
Why it's wrong here
Removing the rubric discards the team's definition of quality and invites the new judge's own stylistic biases, which may differ from editorial standards. A larger model is not automatically aligned with factuality requirements, and without a rubric or calibration set there is no way to verify that the new scores track what editors actually care about.
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JA
Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Databricks exam blueprint
This Databricks-GenAI-Assoc practice question is part of Courseiva's free Databricks certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the Databricks-GenAI-Assoc exam.