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

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

Databricks Certified Data Engineer Professional Sample Questions:

Question 1

A data engineer is using Lakeflow Declarative Pipelines Expectations feature to track the data quality of their incoming sensor data. Periodically, sensors send bad readings that are out of range, and they are currently flagging those rows with a warning and writing them to the silver table along with the good data. They've been given a new requirement ?the bad rows need to be quarantined in a separate quarantine table and no longer included in the silver table.
This is the existing code for their silver table:
@dlt.table
@dlt.expect("valid_sensor_reading", "reading < 120")
def silver_sensor_readings():
return spark.readStream.table("bronze_sensor_readings")
What code will satisfy the requirements?

A. @dlt.table
@dlt.expect_or_drop("valid_sensor_reading", "reading < 120")
def silver_sensor_readings():
return spark.readStream.table("bronze_sensor_readings")
@dlt.table
@dlt.expect("invalid_sensor_reading", "reading >= 120")
def quarantine_sensor_readings():
return spark.readStream.table("bronze_sensor_readings")
B. @dlt.table
@dlt.expect("valid_sensor_reading", "reading < 120")
def silver_sensor_readings():
return spark.readStream.table("bronze_sensor_readings")
@dlt.table
@dlt.expect("invalid_sensor_reading", "reading >= 120")
def quarantine_sensor_readings():
return spark.readStream.table("bronze_sensor_readings")
C. @dlt.table
@dlt.expect_or_drop("valid_sensor_reading", "reading < 120")
def silver_sensor_readings():
return spark.readStream.table("bronze_sensor_readings")
@dlt.table
@dlt.expect("invalid_sensor_reading", "reading < 120")
def quarantine_sensor_readings():
return spark.readStream.table("bronze_sensor_readings")
D. @dlt.table
@dlt.expect_or_drop("valid_sensor_reading", "reading < 120")
def silver_sensor_readings():
return spark.readStream.table("bronze_sensor_readings")
@dlt.table
@dlt.expect_or_drop("invalid_sensor_reading", "reading >= 120")
def quarantine_sensor_readings():
return spark.readStream.table("bronze_sensor_readings")

Question 2

A data engineer manages a Unity Catalog table customer_data in schema finance that includes sensitive fields like ssn and credit_score. Intern Group should only see masked values, while Analyst Group should only access rows for their assigned region. The data engineer needs to restrict access based on user role and region without duplicating data. How should the data engineer enforce this security policy?

A. Create dynamic views for each user role and manage access with ACLs.
B. Use Unity Catalog's row filters based on the user roles and column masks based on the region.
C. Use Unity Catalog's row filters based on the region and column masks based on user roles.
D. Create views using current_user() and is_account_group_member() functions, and apply masking logic inside the SQL SELECT clause for each sensitive column.

Question 3

A data engineer is optimizing a MERGE operation on an 800GB UC-managed table that experiences frequent updates and deletions. Which two actions should the engineer prioritize to improve MERGE performance? (Choose two.)

A. Apply liquid clustering using the merge join keys.
B. Partition the table by date.
C. Overwrite the table instead of Merge.
D. Use ZORDER on high-cardinality columns.
E. Enable deletion vectors on the table if not already enabled.

Question 4

A data engineer needs to provide access to a group named manufacturing-team. The team needs privileges to create tables in the quality schema. Which set of SQL commands will grant a group named manufacturing-team to create tables in a schema named production with the parent catalog named manufacturing with the least privileges?

A. GRANT CREATE TABLE ON SCHEMA manufacturing.quality TO manufacturing-team; GRANT CREATE SCHEMA ON SCHEMA manufacturing.quality TO manufacturing-team; GRANT USE CATALOG ON CATALOG manufacturing TO manufacturing-team;
B. GRANT CREATE TABLE ON SCHEMA manufacturing.quality TO manufacturing-team; GRANT USE SCHEMA ON SCHEMA manufacturing.quality TO manufacturing-team; GRANT USE CATALOG ON CATALOG manufacturing TO manufacturing-team;
C. GRANT CREATE TABLE ON SCHEMA manufacturing.quality TO manufacturing-team; GRANT CREATE SCHEMA ON SCHEMA manufacturing.quality TO manufacturing-team; GRANT CREATE CATALOG ON CATALOG manufacturing TO manufacturing-team;
D. GRANT USE TABLE ON SCHEMA manufacturing.quality TO manufacturing-team; GRANT USE SCHEMA ON SCHEMA manufacturing.quality TO manufacturing-team; GRANT USE CATALOG ON CATALOG manufacturing TO manufacturing-team;

Question 5

A junior data engineer is migrating a workload from a relational database system to the Databricks Lakehouse. The source system uses a star schema, leveraging foreign key constrains and multi-table inserts to validate records on write.
Which consideration will impact the decisions made by the engineer while migrating this workload?

A. Committing to multiple tables simultaneously requires taking out multiple table locks and can lead to a state of deadlock.
B. Foreign keys must reference a primary key field; multi-table inserts must leverage Delta Lake's upsert functionality.
C. Databricks only allows foreign key constraints on hashed identifiers, which avoid collisions in highly-parallel writes.
D. All Delta Lake transactions are ACID compliance against a single table, and Databricks does not enforce foreign key constraints.
E. Databricks supports Spark SQL and JDBC; all logic can be directly migrated from the source system without refactoring.

Solutions: