Databricks-DA-Assoc Managing Data Practice Question
A data analyst runs the following command in a Databricks SQL editor connected to a Unity Catalog workspace:
```sql DROP TABLE IF EXISTS analytics.events.raw_clicks; ```
What is the result of this statement if `analytics.events.raw_clicks` is a managed Delta table?
⚠ Common exam trap
The trap here is assuming that DROP only removes metadata, which is true for external tables but false for managed tables where Databricks also deletes the data files.
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
✓
The table metadata, data files, and any associated directories are permanently removed from the metastore and cloud storage.
Dropping a managed Delta table in Unity Catalog removes both the catalog metadata and the underlying data files because Databricks owns the data lifecycle. External tables behave differently: their files persist. Analysts must therefore treat DROP on a managed table as destructive and irreversible through normal query tools, which is why knowing the table type before running DDL matters.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
The table metadata, data files, and any associated directories are permanently removed from the metastore and cloud storage.
Why this is correct
For a managed table, Databricks owns the lifecycle of both metadata and data. Dropping it removes the catalog entry and deletes the underlying data files from the managed storage location. This is the defining behavior that separates managed from external tables and is why the statement succeeds without any additional file cleanup step from the analyst.
- ✗
The table is renamed to a recycle-bin schema and can be restored by any workspace user within 30 days.
Why it's wrong here
Databricks does not maintain a recycle-bin schema for dropped tables in Unity Catalog, and there is no 30-day self-service restore path for arbitrary users. Recovery depends on separate mechanisms such as Delta time travel on an existing table or restoring from backups, neither of which applies after a DROP has removed the table and its data.
- ✗
The command fails because Databricks SQL does not permit dropping tables that other users may be querying.
Why it's wrong here
Databricks SQL does not block DROP statements based on concurrent readers. There is no built-in lock that prevents dropping a table simply because someone might be querying it. The statement executes as long as the principal has the required privileges, so this option misrepresents how concurrency and DDL interact in Databricks.
- ✗
Only the table metadata is removed; the underlying Parquet data files remain in cloud storage for later recovery.
Why it's wrong here
This describes the behavior of an external table, not a managed one. With managed tables, Databricks controls the data location, so it deletes the files as part of the drop. Leaving the files behind would be incorrect here and would also make the storage state inconsistent with the catalog, which Databricks specifically avoids for managed tables.
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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.