Databricks-GenAI-Assoc Evaluation and Monitoring Practice Question
When monitoring a production LLM, you detect a drift where the model's responses are becoming increasingly verbose and less helpful compared to the baseline. Which strategy is most effective for detecting this quality decay?
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
✓
Implement automated LLM-as-a-judge evaluations on a sample of production inputs.
Model quality decay in LLMs is best detected through continuous automated evaluation using a 'judge' model. By running a set of evaluation prompts through the production model and grading them against an LLM-as-a-judge, you can quantify performance trends. This proactive monitoring allows teams to identify when model behavior deviates from expected standards, triggering alerts before the issues impact a large number of end-users or require significant architectural changes.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Monitor system-level CPU and memory usage of the inference cluster.
Why it's wrong here
CPU and memory metrics describe infrastructure health, not the semantic quality of generated text; a model can degrade in verbosity while resource usage stays flat. Infrastructure monitoring is tempting because it is standard operational practise and would catch saturation or memory leaks, but it cannot measure helpfulness drift.
- ✗
Set up alerts for high request volume spikes in the API logs.
Why it's wrong here
Request volume spikes indicate load or abuse, not semantic quality decay; verbosity and reduced helpfulness require evaluating response content against a baseline. Volume alerting is tempting because it is cheap and already instrumented, and it would suit capacity or rate-limit monitoring, but it cannot detect changes in answer quality.
- ✓
Implement automated LLM-as-a-judge evaluations on a sample of production inputs.
Why this is correct
Using an LLM as a judge allows for automated, scalable evaluation of qualitative metrics. By comparing production outputs against predefined criteria or reference answers, you can effectively track quality drift over time and identify when model responses deteriorate.
- ✗
Require manual reviews for every single response generated by the model.
Why it's wrong here
Manual review of every response is not scalable for production monitoring and gives no automated drift signal; it also cannot keep pace with traffic. Human review is tempting because it judges quality directly and suits low-volume or high-risk sampling, but exhaustive review is impractical and detects decay only retrospectively.
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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.