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Databricks Databricks-Certified-Professional-Data-Engineer Exam Overview:

Certification Vendor:	Databricks
Exam Name:	Databricks Certified Professional Data Engineer Exam
Exam Number:	Databricks-Certified-Professional-Data-Engineer
Exam Price:	USD 200
Available Languages:	English, Korean, Japanese, Portuguese (Brazil)
Passing Score:	70%
Certificate Validity Period:	2 years
Exam Duration:	120 minutes
Exam Format:	Multiple Choice
Real Exam Qty:	59
Related Certifications:	Databricks Certified Associate Data Engineer
Recommended Training:	Databricks Data Engineer Professional Training
Exam Registration:	Databricks Official Certification Registration
Sample Questions:	Databricks Databricks-Certified-Professional-Data-Engineer Sample Questions
Exam Way:	Online proctored or onsite test center
Pre Condition:	No mandatory prerequisites; 1+ year hands-on experience and related training highly recommended
Official Syllabus URL:	https://www.databricks.com/learn/certification/data-engineer-professional

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最新的 Databricks Certification Databricks-Certified-Professional-Data-Engineer 免費考試真題 (Q200-Q205):

問題 #200
A data engineering team is setting up deployment automation. To deploy workspace assets remotely using the Databricks CLI command, they must configure it with proper authentication.
Which authentication approach will provide the highest level of security?

A. Use a service principal ID and its OAuth client secret.
B. Use a service principal and its Personal Access Token.
C. Use a shared user account and its OAuth client secret.
D. Use a service principal with OAuth token federation.

答案：D

解題說明：
Comprehensive and Detailed Explanation From Exact Extract of Databricks Data Engineer Documents:
The most secure and enterprise-recommended authentication method for Databricks automation is OAuth token federation with service principals.
This configuration allows service principals (non-human identities) to authenticate using temporary OAuth access tokens from a trusted identity provider (such as Azure AD or AWS IAM federation). These tokens are short-lived and scoped, significantly reducing credential exposure risks.
By contrast, static client secrets (B) or PATs (C) are long-lived and require periodic manual rotation, increasing security vulnerability. Shared user accounts (D) violate least-privilege and auditability principles. Therefore, A provides the strongest, most compliant authentication model for automated CLI and CI/CD workflows.

問題 #201
A data engineer needs to dynamically create a table name string using three Python varia-bles: region, store,
and year. An example of a table name is below when region = "nyc", store = "100", and year = "2021":
nyc100_sales_2021
Which of the following commands should the data engineer use to construct the table name in Py-thon?

A. "{region}+{store}+"_sales_"+{year}"
B. f"{region}+{store}+_sales_+{year}"
C. f"{region}{store}_sales_{year}"
D. "{region}{store}_sales_{year}"
E. "{region}+{store}+_sales_+{year}"

答案：C

問題 #202
Given the following error traceback (from display(df.select(3* " heartrate " ))) which shows AnalysisException: cannot resolve ' heartrateheartrateheartrate ' , which statement describes the error being raised?

A. There is a type error because a DataFrame object cannot be multiplied.
B. There is a syntax error because the heartrate column is not correctly identified as a column.
C. There is a type error because a column object cannot be multiplied.
D. There is no column in the table named heartrateheartrateheartrate.

答案：D

解題說明：
* Exact extract: "select() expects column names or Column expressions." References: PySpark DataFrame select; Column expressions and col().

問題 #203
A data engineer has created a transactions Delta table on Databricks that should be used by the analytics team.
The analytics team wants to use the table with another tool that requires Apache Iceberg format.
What should the data engineer do?

A. Require the analytics team to use a tool that supports Delta table.
B. Create an Iceberg copy of the transactions Delta table which can be used by the analytics team.
C. Enable uniform on the transactions table to 'iceberg' so that the table can be read as an Iceberg table.
D. Convert the transactions Delta table to Iceberg and enable uniform so that the table can be read as a Delta table.

答案：D

解題說明：
Delta Lake introducedDelta Universal Format (Delta UniForm), which allowsseamless interoperability between Delta Lake and Apache Iceberg. This means a Delta table can beconverted into an Iceberg table while maintaining Delta capabilities.
Explanation of Each Option:
* (A) Require the analytics team to use a tool that supports Delta table
* Incorrect:While Delta Lake is widely used, requiring the team to change toolsis not a flexible or scalable solution.
* (B) Enable uniform on the transactions table to 'iceberg' so that the table can be read as an Iceberg table
* Incorrect:
* The uniform featuremust be enabled after conversion.
* You cannotdirectly enable uniformwithout firstconverting the table.
* (C) Create an Iceberg copy of the transactions Delta table which can be used by the analytics team
* Incorrect:
* Creating a separate Iceberg copy wouldduplicate storage and increase maintenance complexity.
* This is not necessary whenDelta UniForm allows direct compatibility with Iceberg.
* (D) Convert the transactions Delta table to Iceberg and enable uniform so that the table can be read as a Delta table
* Correct:
* The best approach is toconvert the existing Delta table to Icebergusing the Databricks Delta to Iceberg migration tools.
* After conversion,enabling uniform ensures the table remains accessible in both Delta and Iceberg formats.
Conclusion:
The best practice forinteroperability between Delta and Icebergis toconvert the Delta table to Iceberg and enable uniform, ensuringcross-compatibilitywithout data duplication.Thus,Option (D) is the correct answer.
References:
* Delta UniForm for Apache Iceberg - Databricks Documentation
* Convert Delta to Iceberg - Databricks

問題 #204
Which statement describes the correct use of pyspark.sql.functions.broadcast?

A. It marks a column as small enough to store in memory on all executors, allowing a broadcast join.
B. It marks a column as having low enough cardinality to properly map distinct values to available partitions, allowing a broadcast join.
C. It caches a copy of the indicated table on all nodes in the cluster for use in all future queries during the cluster lifetime.
D. It caches a copy of the indicated table on attached storage volumes for all active clusters within a Databricks workspace.
E. It marks a DataFrame as small enough to store in memory on all executors, allowing a broadcast join.

答案：E

解題說明：
Explanation
https://spark.apache.org/docs/3.1.3/api/python/reference/api/pyspark.sql.functions.broadcast.html

問題 #205
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