Databricks-DA-Assoc Data Modeling with Databricks SQL Practice Question
You are modeling a table where users need to query based on a 'user_id' but also need to perform historical point-in-time analysis. Which feature is most appropriate?
⚠ Common exam trap
Candidates often confuse Time Travel with Change Data Feed (CDF). While both relate to history, they are used for different purposes: Time Travel for state snapshots and CDF for granular row-level changes.
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
✓
Use Delta Time Travel.
Time Travel is a native feature of Delta Lake that allows users to query previous versions of a table using a timestamp or version number. This is crucial for point-in-time analysis, as it lets analysts reproduce reports or audit changes without needing to maintain manual snapshots or separate history tables. It leverages the transaction log to provide a consistent, historical view of the data efficiently.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Implement a materialized view with a static snapshot.
Why it's wrong here
Materialized views represent the data as of the last refresh, not the history of changes. They do not provide a native mechanism to query 'as of' a specific point in time, requiring manual snapshots which are difficult to manage and consume unnecessary storage for large datasets.
- ✓
Use Delta Time Travel.
Why this is correct
Time Travel in Delta Lake allows querying data as of a specific version or timestamp using the 'VERSION AS OF' or 'TIMESTAMP AS OF' clauses. This provides seamless access to historical states without requiring extra storage for snapshots or complex architectural workarounds for point-in-time analytical requirements.
- ✗
Create a new table for every daily load.
Why it's wrong here
Creating daily tables leads to massive technical debt and complicates the query layer significantly. Users would have to union multiple tables to perform longitudinal analysis, which is inefficient and prone to errors. Time Travel renders this redundant by providing a built-in historical view in a single table.
- ✗
Store all historical records in a single table with an 'is_current' flag.
Why it's wrong here
While the Type 2 SCD approach works, it complicates query logic as users must filter by the 'is_current' flag or specific dates for every query. It also increases the size of the active table, potentially slowing down standard performance. Delta Time Travel provides this functionality natively without complicating the schema.
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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-DA-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-DA-Assoc exam.