Databricks-DA-Assoc Developing AI/BI Genie Spaces Practice Question
An analyst wants to ensure that a Genie space only uses the most recent data for all queries. How can this be achieved?
⚠ Common exam trap
Candidates often believe that creating manual data refresh schedules or modifying user query inputs is necessary to enforce data freshness across the board.
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 instructions to mandate filters on date columns
To enforce data freshness, the analyst should instruct the Genie space to use views or filter conditions that point to the latest partitions or versioned tables. By incorporating this logic into the instructions, the developer ensures that the model always applies the necessary filter clauses (like 'WHERE date = CURRENT_DATE') to every generated query, maintaining data integrity without requiring manual input from the users.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Set the SQL Warehouse to auto-terminate every hour
Why it's wrong here
Auto-termination is a cost-saving measure that shuts down idle compute. It does not force queries to select specific data versions or timestamps. The data freshness must be controlled via the SQL logic generated by the Genie space, not by the lifecycle settings of the compute resource.
- ✓
Use instructions to mandate filters on date columns
Why this is correct
Instructions are the correct place to enforce business rules, including data freshness. By explicitly telling the model to always filter by the most recent partition or date, you ensure that all results provided to users are current, preventing stale data from being reported in the Genie space.
- ✗
Delete old data from the Unity Catalog tables
Why it's wrong here
Deleting historical data is a destructive action that destroys the audit trail and trend analysis capabilities. It is a poor way to manage freshness. Data should remain in the catalog, and the AI should be configured to query the relevant subsets of that data appropriately.
- ✗
Rename tables whenever new data arrives
Why it's wrong here
Renaming tables is a highly disruptive practice that breaks downstream pipelines and BI dashboards. It is not a sustainable or professional method for handling data freshness. The logic should be handled through query parameters and instructions, not through constant schema changes that trigger maintenance overhead across the organization.
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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
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