Databricks-GenAI-Assoc Evaluation and Monitoring Practice Question
Your organization requires an auditable record of all LLM evaluation results for compliance. Which Databricks feature provides the best centralized storage for these evaluation runs?
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
✓
MLflow Experiments
MLflow Experiments act as the centralized repository for all model development and evaluation runs. By logging evaluation results to MLflow, teams create a permanent, version-controlled audit trail. This allows stakeholders to compare different iterations of models, review evaluation metrics, and verify that quality gates were met before deployment, ensuring compliance with internal AI governance policies and external regulatory frameworks for model transparency.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Databricks File System (DBFS) text logs
Why it's wrong here
DBFS text logs store unstructured files without run metadata, metrics or queryable lineage, so they cannot satisfy an auditable evaluation record. It is tempting because DBFS is the default landing zone for arbitrary artefacts, and would suffice for raw log retention rather than structured evaluation tracking.
- ✓
MLflow Experiments
Why this is correct
MLflow Experiments provide a structured environment to log parameters, metrics, and artifacts. This creates a highly auditable, searchable, and version-controlled record of every evaluation, which is ideal for compliance and tracking the history of model performance.
- ✗
Unity Catalog volume for raw CSV storage
Why it's wrong here
While Unity Catalog volumes are great for data governance, storing evaluation results in raw CSVs lacks the built-in tracking, comparison UI, and integration with the Databricks model lifecycle that MLflow Experiments offer out of the box.
- ✗
Git commit history of the inference code
Why it's wrong here
Git tracks code changes, not the results of model evaluation runs. A commit history tells you what version of the code was deployed, but it does not store the specific metrics or evaluation outputs required for compliance.
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.