Databricks-DE-Pro Data Modelling Practice Question
A financial services firm maintains a Delta Lake table of account transactions that must support both current-state queries and full audit history of every change, including corrections that arrive days later. Regulators require the ability to query the table as it existed at any prior date. Which Delta Lake capability should the engineer rely on to satisfy the audit requirement?
⚠ Common exam trap
The trap here is treating Change Data Feed as a historical snapshot mechanism, when it only surfaces row-level change records rather than a complete reconstructable table state at an arbitrary point in time.
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
✓
Time travel using the transaction log to query historical versions of the table by version number or timestamp
Delta Lake's transaction log records each commit atomically, enabling time travel to any retained version or timestamp. Because late corrections create new versions while prior versions remain queryable, auditors can reconstruct the exact table state at a prior date, which is precisely what the regulatory requirement demands.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Time travel using the transaction log to query historical versions of the table by version number or timestamp
Why this is correct
Delta Lake records every commit in the transaction log, so time travel can reconstruct the table as of a specific version or timestamp, directly satisfying the requirement to query prior states. Corrections create new versions rather than destroying old ones, so auditors can retrieve the exact state at any retained point in time using the same table.
- ✗
Relying on the underlying cloud object store's versioning to recover previous Parquet files
Why it's wrong here
Object store versioning operates at the file level and lacks the table-level commit semantics needed to identify which set of files constituted a consistent table state. Reconstructing a coherent snapshot from raw file versions is error-prone and does not account for Delta's transaction log, so it cannot reliably answer point-in-time audit queries.
- ✗
Maintaining a separate archive table populated by a nightly full copy of the transactions table
Why it's wrong here
A nightly full copy only preserves state at day boundaries and doubles storage and compute costs, while still missing intra-day corrections. It also cannot answer queries for an arbitrary prior timestamp, and the archive drifts from the source unless carefully synchronized, making it a costly and imprecise substitute for native versioning.
- ✗
Enabling Change Data Feed to capture row-level changes and querying the feed instead of the table
Why it's wrong here
Change Data Feed exposes inserts, updates, and deletes with metadata, which is useful for incremental downstream processing, but it is not a mechanism for reconstructing the full table state at an arbitrary past date. Querying the feed returns change records, not a complete snapshot, so it cannot directly answer what the table looked like at a given prior moment.
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-DE-Pro 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-DE-Pro exam.