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

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

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Databricks-Certified-Professional-Data-Engineer New Study Questions - Databricks-Certified-Professional-Data-Engineer Valid Exam Dumps

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Databricks Certified Professional Data Engineer Exam Sample Questions (Q38-Q43):

NEW QUESTION # 38
What is a method of installing a Python package scoped at the notebook level to all nodes in the currently active cluster?

A. Install libraries from PyPi using the cluster UI
B. Use &sh install in a notebook cell
C. Use &Pip install in a notebook cell
D. Run source env/bin/activate in a notebook setup script

Answer: A

Explanation:
Installing a Python package scoped at the notebook level to all nodes in the currently active cluster in Databricks can be achieved by using the Libraries tab in the cluster UI. This interface allows you to install libraries across all nodes in the cluster. While the%pipcommand in a notebook cell would only affect the driver node, using the cluster UI ensures that the package is installed on all nodes.
References:
* Databricks Documentation on Libraries: Libraries

NEW QUESTION # 39
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. No: the change data feed only tracks inserts and updates not deleted records.
B. No: the Delta Lake DELETE command only provides ACID guarantees when combined with the MERGE INTO command
C. Yes: Delta Lake ACID guarantees provide assurance that the DELETE command successed fully and permanently purged these records.
D. No: files containing deleted records may still be accessible with time travel until a BACUM command is used to remove invalidated data files.

Answer: D

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 # 40
A data team is implementing an append-only Delta Lake pipeline that processes both batch and streaming data . They want to ensure that schema changes in the source data are automatically incorporated without breaking the pipeline.
Which configuration should the team use when writing data to the Delta table?

A. mergeSchema = true
B. validateSchema = false
C. overwriteSchema = true
D. ignoreChanges = false

Answer: A

NEW QUESTION # 41
A data engineer has created a new cluster using shared access mode with default configurations. The data engineer needs to allow the development team access to view the driver logs if needed.
What are the minimal cluster permissions that allow the development team to accomplish this?

A. CAN MANAGE
B. CAN ATTACH TO
C. CAN RESTART
D. CAN VIEW

Answer: D

Explanation:
Databricks provides different permission levels to control access to clusters. The correct minimal permission required for viewing driver logs is CAN VIEW.
Databricks Cluster Permission Levels:
CAN ATTACH TO:
Allows users to attach notebooks to a cluster but does not allow them to view logs.
Not sufficient for viewing driver logs.
CAN MANAGE:
Grants full control over the cluster, including starting, stopping, and editing configurations.
Too broad for this requirement.
CAN VIEW (Correct Answer):
Allows users to view cluster details, logs, and status but not modify any configurations.
Minimal required permission for viewing logs.
CAN RESTART:
Grants permission to restart the cluster, but does not include log access.
Not sufficient for viewing logs.
Conclusion:
The minimal permission needed to allow the development team to view driver logs is CAN VIEW.
Reference:
Databricks Cluster Permissions Documentation

NEW QUESTION # 42
You are working on a table called orders which contains data for 2021 and you have the second table called orders_archive which contains data for 2020, you need to combine the data from two tables and there could be a possibility of the same rows between both the tables, you are looking to combine the results from both the tables and eliminate the duplicate rows, which of the following SQL statements helps you accomplish this?

A. SELECT distinct * FROM orders JOIN orders_archive on order.id = or-ders_archive.id
B. SELECT * FROM orders UNION SELECT * FROM orders_archive
(Correct)
C. SELECT * FROM orders INTERSECT SELECT * FROM orders_archive
D. SELECT * FROM orders_archive MINUS SELECT * FROM orders
E. SELECT * FROM orders UNION ALL SELECT * FROM orders_archive

Answer: B

Explanation:
Explanation
Answer is SELECT * FROM orders UNION SELECT * FROM orders_archive
UNION and UNION ALL are set operators,
UNION combines the output from both queries but also eliminates the duplicates.
UNION ALL combines the output from both queries.

NEW QUESTION # 43
......

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