重考一次 Certified-Data-Engineer-Professional，等於再付一次報名費，還要再花好幾週重新準備。與其承擔重考成本，不如先用 KaoGuTi 的 250 道練習題，把 Databricks Certified Data Engineer Professional 的每個領域練熟再上場。

Databricks Certified-Data-Engineer-Professional 考試概覽:

認證廠商:	Databricks
考試名稱:	Databricks Certified Data Engineer Professional
考試代碼:	Certified-Data-Engineer-Professional
相關認證:	Databricks Certified Data Engineer Associate
實際考試題數:	59 題計分選擇題
考試時間:	120 分鐘
考試形式:	選擇題, 考試中心監考, 線上監考
考試費用:	200 美元（不含適用稅金）
證照有效期限:	2 年
支援語言:	English
推薦課程:	Databricks Academy Advanced Data Engineering with Databricks
考試報名:	Databricks Certified Data Engineer Professional 認證
範例考題:
考試方式:	線上監考或考試中心監考
必備條件:	無強制先決條件。強烈建議修讀相關課程，並具備一年以上本考試所涵蓋資料工程任務的實作經驗。
官方大綱網址:	https://www.databricks.com/sites/default/files/2025-11/databricks-certified-data-engineer-professional-exam-guide-november-30-2025.pdf

Databricks Certified-Data-Engineer-Professional 考試大綱主題:

關於 Certified-Data-Engineer-Professional 考試，考生最常問的問題

最新的 Databricks Certification Certified-Data-Engineer-Professional 免費考試真題：

問題 #1

A user new to Databricks is trying to troubleshoot long execution times for some pipeline logic they are working on. Presently, the user is executing code cell-by-cell, using display() calls to confirm code is producing the logically correct results as new transformations are added to an operation. To get a measure of average time to execute, the user is running each cell multiple times interactively.
Which of the following adjustments will get a more accurate measure of how code is likely to perform in production?

A. Scala is the only language that can be accurately tested using interactive notebooks; because the best performance is achieved by using Scala code compiled to JARs. all PySpark and Spark SQL logic should be refactored.
B. Production code development should only be done using an IDE; executing code against a local build of open source Spark and Delta Lake will provide the most accurate benchmarks for how code will perform in production.
C. The only way to meaningfully troubleshoot code execution times in development notebooks Is to use production-sized data and production-sized clusters with Run All execution.
D. The Jobs Ul should be leveraged to occasionally run the notebook as a job and track execution time during incremental code development because Photon can only be enabled on clusters launched for scheduled jobs.
E. Calling display () forces a job to trigger, while many transformations will only add to the logical query plan; because of caching, repeated execution of the same logic does not provide meaningful results.

問題 #2

A junior data engineer has configured a workload that posts the following JSON to the Databricks REST API endpoint 2.0/jobs/create.

Assuming that all configurations and referenced resources are available, which statement describes the result of executing this workload three times?

A. The logic defined in the referenced notebook will be executed three times on new clusters with the configurations of the provided cluster ID.
B. One new job named "Ingest new data" will be defined in the workspace, but it will not be executed.
C. The logic defined in the referenced notebook will be executed three times on the referenced existing all purpose cluster.
D. Three new jobs named "Ingest new data" will be defined in the workspace, and they will each run once daily.
E. Three new jobs named "Ingest new data" will be defined in the workspace, but no jobs will be executed.

問題 #3

A Databricks job has been configured with 3 tasks, each of which is a Databricks notebook. Task A does not depend on other tasks. Tasks B and C run in parallel, with each having a serial dependency on Task A.
If task A fails during a scheduled run, which statement describes the results of this run?

A. Tasks B and C will attempt to run as configured; any changes made in task A will be rolled back due to task failure.
B. Because all tasks are managed as a dependency graph, no changes will be committed to the Lakehouse until all tasks have successfully been completed.
C. Tasks B and C will be skipped; some logic expressed in task A may have been committed before task failure.
D. Unless all tasks complete successfully, no changes will be committed to the Lakehouse; because task A failed, all commits will be rolled back automatically.
E. Tasks B and C will be skipped; task A will not commit any changes because of stage failure.

問題 #4

A company wants to implement Lakehouse Federation across multiple data sources but is concerned about data consistency and ensuring that all teams access the same authoritative version of their data. Which statement is applicable for Lakehouse Federations to maintain data consistency?

A. A separate data synchronization service must be deployed.
B. Federation creates local copies that must be manually refreshed.
C. Federation implements change data capture (CDC) from all sources.
D. Federation provides read-only access that reflects the current state of source systems.

問題 #5

A table is registered with the following code:

Both users and orders are Delta Lake tables. Which statement describes the results of querying recent_orders?

A. All logic will execute at query time and return the result of joining the valid versions of the source tables at the time the query finishes.
B. Results will be computed and cached when the table is defined; these cached results will incrementally update as new records are inserted into source tables.
C. All logic will execute at query time and return the result of joining the valid versions of the source tables at the time the query began.
D. The versions of each source table will be stored in the table transaction log; query results will be saved to DBFS with each query.
E. All logic will execute when the table is defined and store the result of joining tables to the DBFS; this stored data will be returned when the table is queried.

問題與答案：