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

Section	Weight	Objectives
Data Processing with Spark	25-30%	- Python and SQL for data engineering 1. Spark APIs in Python 2. Performance optimization techniques 3. Built-in and user-defined functions - Spark DataFrames and Spark SQL 1. DataFrame operations and transformations 2. Window functions 3. Spark SQL queries and functions
Data Warehouse and Lakehouse Architecture	15-20%	- Lakehouse architecture principles 1. Differences between data lake, data warehouse, and lakehouse 2. Bronze, silver, gold data layers 3. Data governance fundamentals
Data Ingestion	15-20%	- Batch ingestion methods 1. DBR autoloader 2. Integration with external systems 3. Spark APIs for ingestion - Streaming ingestion 1. Kafka integration 2. Structured streaming fundamentals
Pipeline Development and Orchestration	10-15%	- Databricks workflows 1. Monitoring and alerting 2. Task dependencies and orchestration 3. Jobs and job scheduling
Delta Lake	20-25%	- Delta Lake operations 1. Merge, update, delete operations 2. Schema evolution and enforcement 3. Delta Live Tables - Delta Lake fundamentals 1. Optimize and Z-order 2. ACID transactions 3. Time travel and data versioning

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Databricks Certified Professional Data Engineer Exam Sample Questions (Q107-Q112):

NEW QUESTION # 107
A Delta Lake table was created with the below query:

Consider the following query:
DROP TABLE prod.sales_by_store -
If this statement is executed by a workspace admin, which result will occur?

A. The table will be removed from the catalog but the data will remain in storage.
B. Nothing will occur until a COMMIT command is executed.
C. The table will be removed from the catalog and the data will be deleted.
D. An error will occur because Delta Lake prevents the deletion of production data.
E. Data will be marked as deleted but still recoverable with Time Travel.

Answer: C

Explanation:
When a table is dropped in Delta Lake, the table is removed from the catalog and the data is deleted. This is because Delta Lake is a transactional storage layer that provides ACID guarantees. When a table is dropped, the transaction log is updated to reflect the deletion of the table and the data is deleted from the underlying storage. References :
* https://docs.databricks.com/delta/quick-start.html#drop-a-table
* https://docs.databricks.com/delta/delta-batch.html#drop-table

NEW QUESTION # 108
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. Permissions explicitly given by the table creator are the only way the Platform Engineer could access the underlying tables in their schema.
D. The owner of the schema does not automatically have permission to tables within the schema, but can grant them to themselves at any point.

Answer: D

Explanation:
In Databricks, catalogs, schemas (or databases), and tables are managed through the Unity Catalog or Hive Metastore, depending on the environment. Permissions and ownership within these structures are governed by access control lists (ACLs).
* Catalog and Schema Ownership: When a platform engineer creates a catalog (such as catalog_A) and schema (such as schema_A), they automatically become the owner of those entities. This ownership gives them control over granting permissions for those entities (i.e., granting the USE CATALOG and USE SCHEMA privileges to others). However, ownership of the catalog or schema does not automatically extend to ownership or permission of individual tables within that schema.
* Table Permissions: For tables within a schema, the permission model is more granular. The table creator (i.e., whoever creates the table) is automatically assigned as the owner of that table. In this case, the platform engineer owns the schema but does not automatically inherit permissions to any table created within the schema unless explicitly granted by the table's owner or unless they grant permissions to themselves.
* Why the Engineer Lacks Access: The platform engineer notices that they do not have access to the underlying tables in schema_A despite being the owner of the schema. This occurs because the schema's ownership does not cascade to the tables. The engineer must either:
* Grant permissions to themselves for the tables in schema_A, or
* Be granted permissions by whoever created the tables within the schema.
* Resolution: As the owner of the schema, the platform engineer can easily grant themselves the required permissions (such as SELECT, INSERT, etc.) for the tables in the schema. This explains why the owner of a schema may not automatically have access to the tables and must take explicit steps to acquire those permissions.
References
* Databricks Unity Catalog Documentation: Manage Permissions
* [Databricks Permissions and Ownership (https://docs.databricks.com/security/access-control/workspace-acl.html#permissions

NEW QUESTION # 109
The data governance team is reviewing user for deleting records for compliance with GDPR. The following logic has been implemented to propagate deleted requests from the user_lookup table to the user aggregate table.

Assuming that user_id is a unique identifying key and that all users have requested deletion have been removed from the user_lookup table, which statement describes whether successfully executing the above logic guarantees that the records to be deleted from the user_aggregates table are no longer accessible and why?

A. Yes: Delta Lake ACID guarantees provide assurance that the DELETE command successed fully and permanently purged these records.
B. No: the change data feed only tracks inserts and updates not deleted records.
C. No: files containing deleted records may still be accessible with time travel until a BACUM command is used to remove invalidated data files.
D. No: the Delta Lake DELETE command only provides ACID guarantees when combined with the MERGE INTO command

Answer: C

Explanation:
The DELETE operation in Delta Lake is ACID compliant, which means that once the operation is successful, the records are logically removed from the table. However, the underlying files that contained these records may still exist and be accessible via time travel to older versions of the table. To ensure that these records are physically removed and compliance with GDPR is maintained, a VACUUM command should be used to clean up these data files after a certain retention period. The VACUUM command will remove the files from the storage layer, and after this, the records will no longer be accessible.

NEW QUESTION # 110
A data engineer is running a groupBy aggregation on a massive user activity log grouped by user_id. A few users have millions of records, causing task skew and long runtimes.
Which technique will fix the skew in this aggregation?

A. Filter out the skewed users before the aggregation.
B. Use salting by adding a random prefix to skewed keys before aggregation, then aggregate again after removing the prefix.
C. Increase the Spark driver memory and retry.
D. Use reduceByKey instead of groupBy to avoid shuffles.

Answer: B

Explanation:
Comprehensive and Detailed Explanation From Exact Extract of Databricks Data Engineer Documents:
Task skew occurs when a small subset of keys holds a disproportionate amount of data, causing certain tasks to process significantly more records than others. Databricks documentation recommends salting as an effective mitigation technique.
Salting introduces a random or calculated prefix to skewed keys, distributing records across multiple partitions and balancing the workload during the shuffle stage. After aggregation, a second pass re-aggregates results by removing the prefix to restore key integrity.
Increasing memory (B) does not resolve distribution imbalance; reduceByKey (C) still triggers shuffles; and filtering (D) would remove valid business data. Hence, salting is the correct and officially recommended approach to address skew in Spark aggregations.

NEW QUESTION # 111
A data engineer, User A, has promoted a new pipeline to production by using the REST API to programmatically create several jobs. A DevOps engineer, User B, has configured an external orchestration tool to trigger job runs through the REST API. Both users authorized the REST API calls using their personal access tokens.
Which statement describes the contents of the workspace audit logs concerning these events?

A. Because these events are managed separately, User A will have their identity associated with the job creation events and User B will have their identity associated with the job run events.
B. Because the REST API was used for job creation and triggering runs, user identity will not be captured in the audit logs.
C. Because User B last configured the jobs, their identity will be associated with both the job creation events and the job run events.
D. Because User A created the jobs, their identity will be associated with both the job creation events and the job run events.
E. Because the REST API was used for job creation and triggering runs, a Service Principal will be automatically used to identity these events.

Answer: A

Explanation:
The events are that a data engineer, User A, has promoted a new pipeline to production by using the REST API to programmatically create several jobs, and a DevOps engineer, User B, has configured an external orchestration tool to trigger job runs through the REST API. Both users authorized the REST API calls using their personal access tokens. The workspace audit logs are logs that record user activities in a Databricks workspace, such as creating, updating, or deleting objects like clusters, jobs, notebooks, or tables. The workspace audit logs also capture the identity of the user who performed each activity, as well as the time and details of the activity. Because these events are managed separately, User A will have their identity associated with the job creation events and User B will have their identity associated with the job run events in the workspace audit logs. Verified Reference: [Databricks Certified Data Engineer Professional], under "Databricks Workspace" section; Databricks Documentation, under "Workspace audit logs" section.

NEW QUESTION # 112
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