Databricks-GenAI-Assoc Evaluation and Monitoring Practice Question
Exhibit
{
"model_eval": {
"name": "prod_chatbot_v2",
"eval_data": "eval_set_v4",
"results": {
"faithfulness": 0.95,
"relevance": 0.88
}
}
}Refer to the exhibit. An engineer observes an unexpected drop in "relevance" for the latest deployment. What is the most likely cause related to the evaluation process itself?
⚠ Common exam trap
Candidates often blame model degradation first, overlooking that an outdated or misaligned evaluation dataset itself can cause an unexpected drop in metric scores.
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
✓
The evaluation dataset is misaligned with current production query trends.
A drop in relevance often suggests that the evaluation dataset (eval_set_v4) may have become outdated or misaligned with the current production use cases. If the user base or query patterns shift, the original evaluation set may no longer accurately reflect the actual performance requirements. Regularly refreshing the evaluation dataset to match current production trends is essential to ensure that metrics remain valid indicators of system performance.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
The model was trained on too much data.
Why it's wrong here
Training on large datasets typically improves model performance. It is highly unlikely that having more training data would directly cause a significant drop in relevance scores unless the model suffered from severe overfitting to irrelevant patterns.
- ✓
The evaluation dataset is misaligned with current production query trends.
Why this is correct
If production traffic evolves but the evaluation set remains static, the metrics will reflect outdated user needs. A drop in relevance usually signifies that the model is no longer performing well on the types of questions users are currently asking.
- ✗
The inference cluster has too much compute capacity.
Why it's wrong here
Excessive compute capacity might lead to higher costs, but it does not affect the semantic quality or relevance of the model's responses. Compute resources are independent of the logic determining how relevant an answer is to a prompt.
- ✗
The faithfulness score increased too much.
Why it's wrong here
Faithfulness and relevance are distinct metrics. An increase in faithfulness means the model is better at sticking to context; this should theoretically aid relevance, not harm it. A high faithfulness score is generally a positive performance indicator.
About these practice questions
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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.