Databricks Certified Data Engineer Professional - 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 Structured Streaming job deployed to production has been resulting in higher than expected cloud storage costs. At present, during normal execution, each microbatch of data is processed in less than 3s; at least 12 times per minute, a microbatch is processed that contains 0 records. The streaming write was configured using the default trigger settings. The production job is currently scheduled alongside many other Databricks jobs in a workspace with instance pools provisioned to reduce start-up time for jobs with batch execution.
Holding all other variables constant and assuming records need to be processed in less than 10 minutes, which adjustment will meet the requirement?

A. Set the trigger interval to 3 seconds; the default trigger interval is consuming too many records per batch, resulting in spill to disk that can increase volume costs.
B. Set the trigger interval to 500 milliseconds; setting a small but non-zero trigger interval ensures that the source is not queried too frequently.
C. Set the trigger interval to 10 minutes; each batch calls APIs in the source storage account, so decreasing trigger frequency to maximum allowable threshold should minimize this cost.
D. Increase the number of shuffle partitions to maximize parallelism, since the trigger interval cannot be modified without modifying the checkpoint directory.
E. Use the trigger once option and configure a Databricks job to execute the query every 10 minutes; this approach minimizes costs for both compute and storage.

Question 2

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. Users granted with USE CATALOG can modify the owner's permissions to downstream tables.
B. The platform engineer needs to execute a REFRESH statement as the table permissions did not automatically update for owners.
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. Permissions explicitly given by the table creator are the only way the Platform Engineer could access the underlying tables in their schema.

Question 3

A Data Engineer is building a fraud detection pipeline that calls out to Open AI, via a Python library, and needs to include an access token when using the API. Which Databricks CLI command should the Data Engineer use to create the secret?

A. databricks secrets put-secret SCOPE KEY; dbutils.secrets.get (SCOPE, KEY)
B. databricks tokens put-token KEY SCOPE; dbutils.secrets.get (KEY, SCOPE)
C. databricks secrets put-secret KEY SCOPE; dbutils.secrets.get (KEY, SCOPE)
D. databricks tokens put-token SCOPE KEY; dbutils.tokens.get (SCOPE, KEY)

Question 4

A data engineer is using the AUTO CDC API in Lakeflow Spark Declarative Pipeline to propagate deletions from a source table (orders_source) to a target table (orders_target). The source has Change Data Feed (CDF) enabled, but some delete events arrive out of order due to upstream delays. How does the AUTO CDC API internally ensure deletions are applied correctly despite out-of-order events?

A. It uses sequence_by to order events and retains tombstones for deleted rows until older sequences are processed.
B. It runs VACUUM on the target table to purge conflicting records.
C. It ignores deletions if they arrive after updates for the same key.
D. It manually sorts incoming events by timestamp before applying changes.

Question 5

Which approach demonstrates a modular and testable way to use DataFrame transform for ETL code in PySpark?

A.

B.

C.

D.

Solutions: