Databricks-GenAI-Assoc Evaluation and Monitoring Practice Question
A team uses an LLM judge to score their RAG agent on Databricks and sees scores that fluctuate by several points between identical evaluation runs. They need more stable, reproducible quality signals for release gating. Which action best addresses the root cause?
⚠ Common exam trap
The trap here is chasing aggregate stability by enlarging the dataset or blending judges, when the per-example variance originates from judge sampling settings and version drift.
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
✓
Pin the judge model version and set its temperature to zero, then validate the judge against a human-labeled sample.
Fluctuating judge scores usually stem from nondeterministic sampling and drifting judge model versions. Pinning the judge version and setting temperature to zero makes each scoring pass reproducible, and validating the judge against human labels confirms that the stabilized scores are also accurate enough to gate releases reliably.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Increase the number of evaluation examples until the average stabilizes across runs.
Why it's wrong here
A larger sample reduces variance in the aggregate mean but does not remove the per-example nondeterminism of the judge. Release gating on individual cases would still be unstable, and the underlying judge configuration remains the root cause. Scaling the dataset treats a symptom rather than the source of the fluctuation.
- ✗
Average the scores from three different judge models and use the mean for gating.
Why it's wrong here
Ensembling judges adds cost and complexity while each judge remains nondeterministic unless its sampling is controlled. Averaging noisy signals does not make any single run reproducible, so identical inputs can still yield different gate decisions. The root cause of sampling variance is left unaddressed.
- ✗
Switch from an LLM judge to BLEU or ROUGE string-overlap metrics.
Why it's wrong here
String-overlap metrics are deterministic but correlate poorly with semantic quality for open-ended RAG answers, rewarding lexical overlap over correctness. Replacing a semantically meaningful judge with BLEU or ROUGE would stabilize scores at the cost of validity, undermining release gating rather than improving it.
- ✓
Pin the judge model version and set its temperature to zero, then validate the judge against a human-labeled sample.
Why this is correct
Judge variance often comes from sampling temperature and model version changes. Setting temperature to zero and pinning the judge version makes scoring deterministic and reproducible, while human-labeled validation confirms the judge's scores are trustworthy before they gate releases.
About these practice questions
Courseiva writes every Databricks-GenAI-Assoc question from scratch — 330 in total, each with an explanation and a wrong-answer breakdown. None are copied from real exams or dumps. Learn why practice questions differ from exam dumps →
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.