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

Databricks Certified Data Engineer Professional Sample Questions:

1. Which statement describes Delta Lake Auto Compaction?

A) Before a Jobs cluster terminates, optimize is executed on all tables modified during the most recent job.
B) An asynchronous job runs after the write completes to detect if files could be further compacted; if yes, an optimize job is executed toward a default of 128 MB.
C) Optimized writes use logical partitions instead of directory partitions; because partition boundaries are only represented in metadata, fewer small files are written.
D) Data is queued in a messaging bus instead of committing data directly to memory; all data is committed from the messaging bus in one batch once the job is complete.
E) An asynchronous job runs after the write completes to detect if files could be further compacted; if yes, an optimize job is executed toward a default of 1 GB.

2. A junior data engineer has manually configured a series of jobs using the Databricks Jobs UI.
Upon reviewing their work, the engineer realizes that they are listed as the "Owner" for each job.
They attempt to transfer "Owner" privileges to the "DevOps" group, but cannot successfully accomplish this task.
Which statement explains what is preventing this privilege transfer?

A) Other than the default "admins" group, only individual users can be granted privileges on jobs.
B) A user can only transfer job ownership to a group if they are also a member of that group.
C) Only workspace administrators can grant "Owner" privileges to a group.
D) Databricks jobs must have exactly one owner; "Owner" privileges cannot be assigned to a group.
E) The creator of a Databricks job will always have "Owner" privileges; this configuration cannot be changed.

3. A data engineer is analyzing transactional data in a PySpark DataFrame df containing customer_id, transaction_timestamp (precise to milliseconds), and amount_spent. The objective is to compute a cumulative sum of amount_spent per customer, strictly ordered by transaction_timestamp. The cumulative sum must include all transactions from the earliest timestamp up to and including the current row, respecting temporal ordering within each customer partition. Which PySpark code snippet most accurately constructs the appropriate window specification and applies the aggregation to yield the correct cumulative expenditure per customer?

A)

B)

C)

D)

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

5. A data engineer is designing a system to process batch patient encounter data stored in an S3 bucket, creating a Delta table (patient_encounters) with columns encounter_id, patient_id, encounter_date, diagnosis_code, and treatment_cost. The table is queried frequently by patient_id and encounter_date, requiring fast performance. Fine-grained access controls must be enforced. The engineer wants to minimize maintenance and boost performance. How should the data engineer create the patient_encounters table?

A) Create a managed table in Hive Metastore. Configure Hive Metastore permissions for access controls, and rely on predictive optimization to enhance query performance and simplify maintenance.
B) Create a managed table in Unity Catalog. Configure Unity Catalog permissions for access controls, schedule jobs to run OPTIMIZE and VACUUM commands daily to achieve best performance.
C) Create a managed table in Unity Catalog. Configure Unity Catalog permissions for access controls, and rely on predictive optimization to enhance query performance and simplify maintenance.
D) Create an external table in Unity Catalog, specifying an S3 location for the data files. Enable predictive optimization through table properties, and configure Unity Catalog permissions for access controls.

Solutions: