Databricks-GenAI-Assoc Data Preparation Practice Question
A data engineer needs to persist a prepared instruction-tuning dataset so that downstream fine-tuning jobs can read it with ACID guarantees, time travel, and schema enforcement, and so that Unity Catalog can track column-level lineage. Which storage format and registration should be used?
⚠ Common exam trap
The trap here is treating a governed file path as equivalent to a governed table, when lineage and ACID properties come from the table format and catalog registration.
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
✓
Write the dataset as a Delta table registered in Unity Catalog.
Delta Lake on Unity Catalog is the only option that simultaneously delivers ACID transactions, time travel, schema enforcement, and column-level lineage for the prepared dataset. Parquet, CSV, and ORC files lack transactional table semantics, and the Hive metastore does not provide the Unity Catalog lineage the team needs.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Write the dataset as an ORC table in the Hive metastore.
Why it's wrong here
ORC in the legacy Hive metastore does not provide Delta time travel or ACID semantics on Databricks, and it is outside Unity Catalog, so column-level lineage is unavailable. It also splits governance across two catalogs, which complicates the downstream fine-tuning access model the team requires.
- ✓
Write the dataset as a Delta table registered in Unity Catalog.
Why this is correct
Delta Lake provides ACID transactions, time travel, and schema enforcement on the prepared dataset, and registering the table in Unity Catalog enables column-level lineage and fine-grained governance. This combination directly satisfies every stated requirement, including the ability for downstream fine-tuning jobs to read a consistent, versioned table.
- ✗
Write the dataset as CSV files to an external location registered in Unity Catalog.
Why it's wrong here
CSV lacks type fidelity, has no transactional guarantees, and cannot enforce schemas, so downstream jobs could read partially written or malformed data. Registering the external location grants governance over the path but does not create a table with lineage or time travel, failing the stated needs.
- ✗
Write the dataset as Parquet files in a Unity Catalog volume.
Why it's wrong here
Parquet files in a volume are just files: they offer no ACID transactions, no time travel, and no schema enforcement at the table level. Unity Catalog can govern the volume path, but column-level lineage and table semantics apply to tables, not raw files, so this choice misses the transaction and versioning requirements.
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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.