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Databricks Databricks-Certified-Data-Engineer-Professional Exam Syllabus Topics:

Databricks Certified Data Engineer Professional Sample Questions:

1. The Databricks workspace administrator has configured interactive clusters for each of the data engineering groups. To control costs, clusters are set to terminate after 30 minutes of inactivity.
Each user should be able to execute workloads against their assigned clusters at any time of the day.
Assuming users have been added to a workspace but not granted any permissions, which of the following describes the minimal permissions a user would need to start and attach to an already configured cluster.

A) Workspace Admin privileges, cluster creation allowed. "Can Attach To" privileges on the required cluster
B) Cluster creation allowed. "Can Restart" privileges on the required cluster
C) Cluster creation allowed. "Can Attach To" privileges on the required cluster
D) "Can Manage" privileges on the required cluster
E) "Can Restart" privileges on the required cluster

2. A data engineer is designing a secure data sharing strategy for their organization. The company needs to share sensitive customer analytics data with two different partners. Partner A uses Databricks with Unity Catalog enabled, while Partner B uses Apache Spark on AWS without Databricks. How should the company implement secure data sharing for these scenarios?

A) Both partners should use the same Delta Sharing approach since security requirements are identical. You should create bearer tokens for both partners and use the open sharing protocol (D2O) for maximum compatibility.
B) For Partner A, implement Databricks-to-Databricks sharing (D2D) with Unit Catalog integration and no-token exchange system. For Partner B, use open sharing protocol (D2O) with either bearer tokens or OIDC federation for authentication, ensuring both approaches maintain robust security and governance.
C) Databricks-to-Databricks sharing (D2D) can only be used within the same cloud provider, so you must use open sharing (D2O) for any cross-cloud scenarios. Unit Catalog governance is not available when sharing with external platforms.
D) Open sharing protocol (D2O) should be used for both partners because it provides better security than D2D sharing. The bearer token approach is always more secure than Unity Catalog's native authentication.

3. A table named user_ltv is being used to create a view that will be used by data analysts on various teams. Users in the workspace are configured into groups, which are used for setting up data access using ACLs.
The user_ltv table has the following schema:
email STRING, age INT, ltv INT
The following view definition is executed:

An analyst who is not a member of the marketing group executes the following query:
SELECT * FROM email_ltv
Which statement describes the results returned by this query?

A) The email, age. and ltv columns will be returned with the values in user ltv.
B) The email and ltv columns will be returned with the values in user itv.
C) Three columns will be returned, but one column will be named "redacted" and contain only null values.
D) Only the email and itv columns will be returned; the email column will contain all null values.
E) Only the email and ltv columns will be returned; the email column will contain the string
"REDACTED" in each row.

4. A data engineer is designing a Lakeflow Declarative Pipeline to process streaming order data.
The pipeline uses Auto Loader to ingest data and must enforce data quality by ensuring customer_id and amount are greater than zero. Invalid records should be dropped. Which Lakeflow Declarative Pipelines configurations implement this requirement using Python?

A) @dlt.table
def silver_orders():
return (
dlt.read_stream("bronze_orders")
.expect("valid_customer", "customer_id IS NOT NULL")
.expect("valid_amount", "amount > 0")
)
B) @dlt.table
@dlt.expect("valid_customer", "customer_id IS NOT NULL")
@dlt.expect("valid_amount", "amount > 0")
def silver_orders():
return dlt.read_stream("bronze_orders")
C) @dlt.table
def silver_orders():
return (
dlt.read_stream("bronze_orders")
.expect_or_drop("valid_customer", "customer_id IS NOT NULL")
.expect_or_drop("valid_amount", "amount > 0")
)
D) @dlt.table
@dlt.expect_or_drop("valid_customer", "customer_id IS NOT NULL")
@dlt.expect_or_drop("valid_amount", "amount > 0")
def silver_orders():
return dlt.read_stream("bronze_orders")

5. A platform engineer is creating catalogs and schemas for the development team to use.
The engineer has created an initial catalog, catalog_A, and initial schema, schema_A. The engineer has also granted USE CATALOG, USE SCHEMA, and CREATE TABLE to the development team so that the engineer can begin populating the schema with new tables.
Despite being owner of the catalog and schema, the engineer noticed that they do not have access to the underlying tables in Schema_A.
What explains the engineer's lack of access to the underlying tables?

A) The platform engineer needs to execute a REFRESH statement as the table permissions did not automatically update for owners.
B) Permissions explicitly given by the table creator are the only way the Platform Engineer could access the underlying tables in their schema.
C) The owner of the schema does not automatically have permission to tables within the schema, but can grant them to themselves at any point.
D) Users granted with USE CATALOG can modify the owner's permissions to downstream tables.

Solutions: