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

Section	Objectives
Production Pipelines and Orchestration	- Databricks Workflows - Error handling and recovery strategies - Job scheduling and monitoring
Delta Lake and Data Management	- Schema evolution and enforcement - Delta Lake transactions and ACID properties - Time travel and versioning
Databricks Lakehouse Platform Architecture	- Data governance concepts (Unity Catalog basics) - Medallion architecture (Bronze, Silver, Gold) - Workspace and cluster architecture
Data Ingestion and Processing	- ETL pipeline design patterns - Structured Streaming fundamentals - Batch and streaming ingestion with Auto Loader
Data Modeling and Transformation	- Dimensional modeling concepts - Spark SQL transformations - Performance optimization techniques

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Databricks Certified Data Engineer Professional Exam Sample Questions (Q56-Q61):

NEW QUESTION # 56
The data engineering team maintains the following code:
Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from

Assuming that this code produces logically correct results and the data in the source tables has been de-duplicated and validated, which statement describes what will occur when this code is executed?

A. An incremental job will detect if new rows have been written to any of the source tables; if new rows are detected, all results will be recalculated and used to overwrite the enriched_itemized_orders_by_account table.
B. An incremental job will leverage information in the state store to identify unjoined rows in the source tables and write these rows to the enriched_iteinized_orders_by_account table.
C. A batch job will update the enriched_itemized_orders_by_account table, replacing only those rows that have different values than the current version of the table, using accountID as the primary key.
D. No computation will occur until enriched_itemized_orders_by_account is queried; upon query materialization, results will be calculated using the current valid version of data in each of the three tables referenced in the join logic.
E. The enriched_itemized_orders_by_account table will be overwritten using the current valid version of data in each of the three tables referenced in the join logic.

Answer: E

Explanation:
This is the correct answer because it describes what will occur when this code is executed. The Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from code uses three Delta Lake tables as input sources: accounts, orders, and order_items. These tables are joined together using SQL queries to create a view called new_enriched_itemized_orders_by_account, which contains information about each order item and its associated account details. Then, the code uses write.format("delta").mode("overwrite") to overwrite a target table called enriched_itemized_orders_by_account using the data from the view. This means that every time this code is executed, it will replace all existing data in the target table with new data based on the current valid version of data in each of the three input tables.

NEW QUESTION # 57
A data engineer is brining an existing production Databricks job under asset bundle management and wants to ensure that:
- The job's current configuration is captured as YAML, and all
referenced files are included in their bundle project.
- Future changes to the bundle's YAML will update the existing job in-
place (not create a new job)
How should the data engineer successfully move the production job under asset bundle management?

A. Export the job definition as JSON, convert it to YAML, and place it in your bundle. Then, run Databricks bundle deploy to update the existing job.
B. Manually create the YAML configuration for the job in your bundle project, ensuring all settings match the existing job. Then, run Databricks bundle deploy the bundle, which will update the existing job in your workspace.
C. Run databricks bundle generate job --existing-job-id to generate the YAML and download referenced files. Then, run Databricks bundle deployment, bind to link the bundle's job resource to the existing job in Databricks.
D. Run Databricks bundle generate job --existing-job-id to generate the YAML and download referenced files. Then, run Databricks bundle deploy to deploy the bundle, which will always update the existing job automatically.

Answer: C

Explanation:
Generating the bundle from the existing job captures the full job configuration as YAML and pulls in all referenced files into the bundle project. Binding the generated job resource to the existing Databricks job establishes a persistent link, ensuring that future bundle deployments update the same production job in place rather than creating a new one.

NEW QUESTION # 58
A data engineer and a platform engineer are working together to automate their system tasks. A script needs to be executed outside of Databricks only if a particular daily Databricks job finishes successfully for the day. Databricks CLI command was used to check the last execution of the job. What are the required command options for that task?

A. databricks jobs list-runs --job-id JOB_ID --start-time-to TODAY_MIDNIGHT_EPOCH_MS --active- only
B. databricks jobs list-runs --job-id JOB_ID --start-time-from TODAY_MIDNIGHT_EPOCH_MS -- active-only
C. databricks jobs list-runs --job-id JOB_ID --start-time-from TODAY_MIDNIGHT_EPOCH_MS -- completed-only
D. databricks jobs list-runs --job-id JOB_ID --start-time-to TODAY_MIDNIGHT_EPOCH_MS -- completed-only

Answer: C

Explanation:
Using --start-time-from with today's midnight epoch ensures only runs that started during the current day are considered, and --completed-only filters out in-progress runs. This allows the script to reliably check whether the daily job has finished successfully before triggering downstream external actions.

NEW QUESTION # 59
A data engineer is performing a join operation to combine values from a static userlookup table with a streaming DataFrame streamingDF.
Which code block attempts to perform an invalid stream-static join?

A. streamingDF.join(userLookup, ["userid"], how="inner")
B. streamingDF.join(userLookup, ["user_id"], how="outer")
C. streamingDF.join(userLookup, ["user_id"], how="left")
D. userLookup.join(streamingDF, ["user_id"], how="right")
E. userLookup.join(streamingDF, ["userid"], how="inner")

Answer: B

Explanation:
https://spark.apache.org/docs/latest/structured-streaming-programming-guide.html#support- matrix-for-joins-in-streaming-queries

NEW QUESTION # 60
A data engineer is designing a secure data sharing strategy for their organization. The company needs to share sensitive customer analytics data with two different partners. Partner A uses Databricks with Unity Catalog enabled, while Partner B uses Apache Spark on AWS without Databricks. How should the company implement secure data sharing for these scenarios?

A. Both partners should use the same Delta Sharing approach since security requirements are identical. You should create bearer tokens for both partners and use the open sharing protocol (D2O) for maximum compatibility.
B. Open sharing protocol (D2O) should be used for both partners because it provides better security than D2D sharing. The bearer token approach is always more secure than Unity Catalog's native authentication.
C. For Partner A, implement Databricks-to-Databricks sharing (D2D) with Unit Catalog integration and no-token exchange system. For Partner B, use open sharing protocol (D2O) with either bearer tokens or OIDC federation for authentication, ensuring both approaches maintain robust security and governance.
D. Databricks-to-Databricks sharing (D2D) can only be used within the same cloud provider, so you must use open sharing (D2O) for any cross-cloud scenarios. Unit Catalog governance is not available when sharing with external platforms.

Answer: C

Explanation:
Databricks-to-Databricks sharing with Unity Catalog provides the most seamless and secure option for Partner A by enabling native governance, fine-grained access controls, and a no-token exchange model. For Partner B, which does not use Databricks, the open sharing protocol enables secure access from external Spark environments using standard authentication mechanisms such as bearer tokens or OIDC federation, while still enforcing sharing policies and protecting sensitive data.

NEW QUESTION # 61
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