Databricks-DE-Pro Data Modelling Practice Question
A financial institution is building a Gold layer table that must support point-in-time queries to reconstruct account balances as of any past date. The source data includes transactions with effective dates and an audit log of changes. Which modeling technique is most appropriate?
⚠ Common exam trap
A common mix-up: candidates confuse Delta Lake time travel with a full history tracking mechanism, but time travel only retains versions for a limited period and does not model changes explicitly.
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
✓
Implement a Type 2 slowly changing dimension (SCD) with effective start and end dates.
A Type 2 SCD retains full history by adding new rows with effective date ranges, enabling accurate point-in-time queries. This is essential for financial data where past states must be reconstructable. Delta Lake time travel is limited by retention and not designed for continuous history. Type 1 and Type 3 SCDs lack the necessary historical depth.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use Delta Lake time travel to query previous versions of the table.
Why it's wrong here
Delta Lake time travel allows querying previous snapshots of the table, but it is limited by the retention period (default 30 days). It cannot guarantee point-in-time queries for arbitrary past dates beyond retention. Additionally, it does not provide a continuous history of changes unless every change is committed as a new version. Thus, it is not a reliable modeling technique for this requirement.
- ✗
Use a Type 1 slowly changing dimension (SCD) to overwrite old values.
Why it's wrong here
Type 1 SCD overwrites historical data, losing the ability to reconstruct past states. This fails the point-in-time requirement because previous values are not retained. It is suitable for correcting errors but not for temporal queries. Therefore, it cannot support reconstructing account balances as of any past date.
- ✗
Create a Type 3 slowly changing dimension (SCD) with previous value columns.
Why it's wrong here
Type 3 SCD only stores the current and one previous value, limiting historical depth. It cannot reconstruct balances for any arbitrary past date, only the immediately previous state. This is insufficient for point-in-time queries that may require multiple historical versions. Therefore, it does not meet the requirement.
- ✓
Implement a Type 2 slowly changing dimension (SCD) with effective start and end dates.
Why this is correct
Type 2 SCD preserves history by creating new rows for changes, with effective start and end dates. This allows point-in-time queries by filtering on the desired date. In Databricks, this can be implemented using Delta Lake's merge operations and time travel. It is the standard technique for temporal analysis in data warehousing.
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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.