Databricks-GenAI-Assoc Data Preparation Practice Question
Which Databricks feature is best suited for maintaining the lineage of data used during the preparation of training sets for Generative AI?
⚠ Common exam trap
Candidates often suggest external logging tools or manual documentation, failing to recognize that Unity Catalog provides native, automated lineage tracking that is integrated directly into the data platform.
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
✓
Unity Catalog
Unity Catalog provides centralized governance, including automated data lineage tracking. This allows engineers to trace the source of data used for model training, which is vital for reproducibility and regulatory compliance. Knowing the exact provenance of training data helps in troubleshooting model bias and ensures that sensitive data sources are correctly identified and managed throughout the lifecycle of the model training process.
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 Lake Time Travel
Why it's wrong here
Delta Time Travel is excellent for viewing previous versions of data, but it does not provide end-to-end lineage across different tables and pipelines. It is a storage-level feature rather than a comprehensive governance and metadata tracking tool needed for complex Generative AI audit requirements and data provenance.
- ✓
Unity Catalog
Why this is correct
Unity Catalog captures fine-grained lineage information as data moves from raw ingestion to final training sets. It offers a unified view of dependencies, which is essential for auditing the training process. This visibility is crucial for ensuring that training data meets safety and compliance standards in enterprise environments.
- ✗
Cluster Policies
Why it's wrong here
Cluster policies are used to define permissions and resource limits for compute resources. They are irrelevant to tracking data lineage or provenance. Using them for data governance would be a misconfiguration, as they do not interact with the data layer or the metadata repository at all.
- ✗
MLflow Experiments
Why it's wrong here
MLflow tracks parameters, metrics, and models, but it does not natively track the lineage of the raw data used for preparation. While it logs the input data URI, it lacks the deep, automated structural lineage integration that Unity Catalog provides for complex multi-stage data engineering pipelines.
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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.