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

Section	Objectives
Security, Governance, Monitoring, and Optimization	- Monitor and optimize Spark workloads - Cost optimization and performance tuning - Implement Unity Catalog governance and access control
Data Ingestion and Transformation	- Handle batch and streaming data pipelines - Ingest data using Apache Spark and Databricks - Transform and clean datasets using Spark SQL and DataFrame APIs
Production Pipelines and Orchestration	- Pipeline reliability and fault tolerance - Automate ETL pipelines and scheduling - Build and manage workflows using Databricks Jobs
Data Modeling and Storage	- Design scalable data lakehouse architectures - Delta Lake table design and optimization - Schema evolution and data partitioning strategies

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Databricks Certified Professional Data Engineer Exam Sample Questions (Q108-Q113):

NEW QUESTION # 108
A data engineer wants to automate job monitoring and recovery in Databricks using the Jobs API. They need to list all jobs, identify a failed job, and rerun it.
Which sequence of API actions should the data engineer perform?

A. Use the jobs/get endpoint to retrieve job details, then use jobs/update to rerun failed jobs.
B. Use the jobs/list endpoint to list jobs, then use the jobs/create endpoint to create a new job, and run the new job using jobs/run-now.
C. Use the jobs/cancel endpoint to remove failed jobs, then recreate them with jobs/create and run the new ones.
D. Use the jobs/list endpoint to list jobs, check job run statuses with jobs/runs/list, and rerun a failed job using jobs/run-now.

Answer: D

Explanation:
Comprehensive and Detailed Explanation From Exact Extract of Databricks Data Engineer Documents:
The Databricks Jobs REST API provides several endpoints for automation. The correct monitoring and rerun flow uses three specific calls:
GET /api/2.1/jobs/list - Lists all available jobs within the workspace.
GET /api/2.1/jobs/runs/list - Returns all runs for a specific job, including their current state (e.g., TERMINATED: FAILED).
POST /api/2.1/jobs/run-now - Immediately triggers a rerun of the specified job.
This sequence aligns with Databricks' prescribed automation model for job observability and recovery. Using jobs/update modifies metadata but does not rerun jobs, and jobs/create is only used for creating new jobs, not rerunning failed ones. Cancelling and recreating jobs introduces unnecessary duplication. Therefore, option A is the correct automated recovery workflow.

NEW QUESTION # 109
A Data engineer wants to run unit's tests using common Python testing frameworks on python functions defined across several Databricks notebooks currently used in production.
How can the data engineer run unit tests against function that work with data in production?

A. Define and import unit test functions from a separate Databricks notebook
B. Define and unit test functions using Files in Repos
C. Define units test and functions within the same notebook
D. Run unit tests against non-production data that closely mirrors production

Answer: D

NEW QUESTION # 110
You were asked to create a notebook that can take department as a parameter and process the data accordingly, which is the following statements result in storing the notebook parameter into a py-thon variable

A. SET department = dbutils.widget.get("department")
B. department = notebook.param.get("department")
C. ASSIGN department == dbutils.widget.get("department")
D. department = dbutils.widget.get("department")
E. department = notebook.widget.get("department")

Answer: D

Explanation:
Explanation
The answer is department = dbutils.widget.get("department")
Refer to additional documentation here
https://docs.databricks.com/notebooks/widgets.html

NEW QUESTION # 111
Which of the following is correct for the global temporary view?

A. global temporary views cannot be accessed once the notebook is detached and attached
B. global temporary views can be still accessed even if the cluster is restarted
C. global temporary views can be still accessed even if the notebook is detached and at-tached
D. global temporary views can be accessed across many clusters
E. global temporary views are created in a database called temp database

Answer: C

Explanation:
Explanation
The answer is global temporary views can be still accessed even if the notebook is detached and attached There are two types of temporary views that can be created Local and Global
* A local temporary view is only available with a spark session, so another notebook in the same cluster can not access it. if a notebook is detached and reattached local temporary view is lost.
* A global temporary view is available to all the notebooks in the cluster, even if the notebook is detached and reattached it can still be accessible but if a cluster is restarted the global temporary view is lost.

NEW QUESTION # 112
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. Databricks only allows foreign key constraints on hashed identifiers, which avoid collisions in highly-parallel writes.
B. Committing to multiple tables simultaneously requires taking out multiple table locks and can lead to a state of deadlock.
C. All Delta Lake transactions are ACID compliance against a single table, and Databricks does not enforce foreign key constraints.
D. Foreign keys must reference a primary key field; multi-table inserts must leverage Delta Lake's upsert functionality.

Answer: C

Explanation:
In Databricks and Delta Lake, transactions are indeed ACID-compliant, but this compliance is limited to single table transactions. Delta Lake does not inherently enforce foreign key constraints, which are a staple in relational database systems for maintaining referential integrity between tables. This means that when migrating workloads from a relational database system to Databricks Lakehouse, engineers need to reconsider how to maintain data integrity and relationships that were previously enforced by foreign key constraints.
Unlike traditional relational databases where foreign key constraints help in maintaining the consistency across tables, in Databricks Lakehouse, the data engineer has to manage data consistency and integrity at the application level or through careful design of ETL processes.References:
* Databricks Documentation on Delta Lake: Delta Lake Guide
* Databricks Documentation on ACID Transactions in Delta Lake: ACID Transactions in Delta Lake

NEW QUESTION # 113
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