Databricks-GenAI-Assoc Application Development Practice Question
A developer needs to store prompt templates, model parameters, and evaluation results for a GenAI application so that each iteration can be compared and reproduced later. Which Databricks capability should they use?
⚠ Common exam trap
The trap here is assuming that any storage location for prompts, such as a Unity Catalog volume, also provides experiment tracking and run comparison.
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 Tracking with experiments and runs
MLflow Tracking provides experiments and runs that capture parameters, metrics, and artifacts, making it the right tool to version prompts, record model settings, and store evaluation results for comparison and reproduction. Data pipelines, volumes, and dashboards serve different purposes and do not offer run-based experiment tracking.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Delta Live Tables pipelines
Why it's wrong here
Delta Live Tables is a declarative framework for building data pipelines with expectations and lineage. It manages data transformations, not experiment metadata such as prompts or evaluation metrics. Using it to track prompt versions would conflate data engineering with experiment tracking and provide no native run comparison or artifact storage for prompts.
- ✓
MLflow Tracking with experiments and runs
Why this is correct
MLflow Tracking records parameters, metrics, artifacts, and tags per run within an experiment, which is exactly what is needed to compare prompt templates, model settings, and evaluation metrics across iterations. It is the standard mechanism in Databricks for reproducible GenAI development and integrates with Mosaic AI evaluation outputs.
- ✗
Databricks SQL dashboards
Why it's wrong here
SQL dashboards visualize query results but are not an experiment tracking system. They cannot log parameters and artifacts per iteration or link a prompt version to its evaluation run. While useful for monitoring production metrics, dashboards lack the run-centric metadata model needed for reproducible GenAI development.
- ✗
Unity Catalog volumes
Why it's wrong here
Volumes store files such as model weights or datasets under governance, but they do not provide run comparison, metric logging, or parameter tracking. Storing prompt files in a volume is possible, yet without MLflow there is no structured way to associate a prompt version with its evaluation results or to reproduce an iteration.
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.