In Databricks, what is the role of a schema within a catalog?

It is a container that holds tables and views within a catalog.

It is a physical storage location for data files.

It is a file format used to store data in Delta Lake.

It is a set of permissions that controls access to tables.

A data engineer creates a table using `CREATE TABLE sales_data USING DELTA LOCATION '/mnt/sales'`. Later, they drop the table from the metastore. What happens to the data files in '/mnt/sales'?

The data files are retained but the table becomes inaccessible to all users.

The data files are deleted because the table is managed and Databricks manages the data lifecycle.

The data files remain in '/mnt/sales' because the table is external and only the metadata is removed.

The data files are moved to a trash folder and can be recovered for 30 days.

A large enterprise has multiple business units that share a Databricks workspace. The governance team wants to ensure that each business unit can manage its own data objects and control access to them, but they also want to maintain a central catalog for certified, enterprise-wide data. What is the best approach?

Create a single catalog with schemas for each business unit and a schema for certified data.

Create a separate workspace for each business unit.

Create a single catalog and use table-level access controls to isolate business units.

Create a separate catalog for each business unit, and a central catalog for certified data.

What does it mean when a table is certified in Databricks?

It has been encrypted with a special algorithm.

It has been marked as trusted and approved for use by the organization.

It is a table that cannot be modified by anyone.

It is a managed table with automatic backups.

Why might an organization certify a table?

To restrict access to the table to a few users.

To enable Delta Lake features on the table.

To indicate that the table is trustworthy for use in analysis.

To automatically optimize the table's performance.

DatabricksCertified Data Analyst Associate

Domain 1Objective 2

Understand Catalogs, Schemas, Managed and External Tables, Access Controls, Views, Certified Tables, and Lineage Within the Catalog Explorer Interface. DATA-ANALYST-ASSOCIATE Practice Questions (Page 1)

Part of the Section 1: Understanding of Databricks Data Intelligence Platform domain, which makes up ~12% of our current practice bank. Databricks does not publish an official question count, but from its 90-minute exam (~35–60 total, ~4–7 in this domain), expect 1–2 from this objective — we provide 30 practice questions to prepare you well beyond it. (estimate)

30questions here

6free pages

9concepts

Questions 1–5

1foundation · easy
In Databricks, what is the role of a schema within a catalog?
Select an answer first
2application · medium
A data engineer creates a table using `CREATE TABLE sales_data USING DELTA LOCATION '/mnt/sales'`. Later, they drop the table from the metastore. What happens to the data files in '/mnt/sales'?
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3expert · hard
A large enterprise has multiple business units that share a Databricks workspace. The governance team wants to ensure that each business unit can manage its own data objects and control access to them, but they also want to maintain a central catalog for certified, enterprise-wide data. What is the best approach?
Select an answer first
4foundation · easy
What does it mean when a table is certified in Databricks?
Select an answer first
5foundation · easy
Why might an organization certify a table?
Select an answer first

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