Databricks-Certified-Data-Engineer-Professional Ressourcen Prüfung - Databricks-Certified-Data-Engineer-Professional Prüfungsguide & Databricks-Certified-Data-Engineer-Professional Beste Fragen

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

Section	Objectives
Topic 1: Delta Lake and Data Management	- Time travel and versioning - Delta Lake transactions and ACID properties - Schema evolution and enforcement
Topic 2: Production Pipelines and Orchestration	- Databricks Workflows - Error handling and recovery strategies - Job scheduling and monitoring
Topic 3: Data Modeling and Transformation	- Performance optimization techniques - Spark SQL transformations - Dimensional modeling concepts
Topic 4: Data Ingestion and Processing	- Batch and streaming ingestion with Auto Loader - Structured Streaming fundamentals - ETL pipeline design patterns
Topic 5: Databricks Lakehouse Platform Architecture	- Medallion architecture (Bronze, Silver, Gold) - Workspace and cluster architecture - Data governance concepts (Unity Catalog basics)

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Databricks Certified Data Engineer Professional Exam Databricks-Certified-Data-Engineer-Professional Prüfungsfragen mit Lösungen (Q108-Q113):

108. Frage
A view 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. 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.
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.

Antwort: C

109. Frage
A data engineer is creating a daily reporting job. There are two reporting notebooks--one for weekdays and one for weekends. An "if/else condition" task is configured as
{{job.start_time.is_weekday}} == true to route the job to either the weekday or weekend notebook tasks. The same job would be used across multiple time zones. Which action should a senior data engineer take upon reviewing the job to merge or reject the pull request?

A. Reject, as they should use {{job.trigger_time.is_weekday}} instead.
B. Reject, as the {{job.start_time.is_weekday}} is for the UTC timezone.
C. Merge, as the job configuration looks good.
D. Reject, as the {{job.start_time.is_weekday}} is not a valid value reference.

Antwort: B

Begründung:
Databricks parameter templates like {{job.start_time.is_weekday}} evaluate in UTC time by default, not in local workspace or regional time zones. Therefore, when jobs are configured to run across different time zones, relying on is_weekday using UTC may cause scheduling and task routing mismatches (for example, triggering the weekday notebook in one region while it's still the weekend locally).
Databricks recommends adjusting conditional logic or pipeline parameters explicitly to handle time zone conversions if business requirements depend on local times. Because the engineer's configuration does not account for this behavior, a senior data engineer should reject the pull request and suggest time-zone-aware logic before merging.

110. Frage
Assuming that the Databricks CLI has been installed and configured correctly, which Databricks CLI command can be used to upload a custom Python Wheel to object storage mounted with the DBFS for use with a production job?

A. workspace
B. jobs
C. fs
D. configure
E. libraries

Antwort: C

Begründung:
https://docs.databricks.com/en/archive/dev-tools/cli/dbfs-cli.html

111. Frage
A data engineer, User A, has promoted a new pipeline to production by using the REST API to programmatically create several jobs. A DevOps engineer, User B, has configured an external orchestration tool to trigger job runs through the REST API. Both users authorized the REST API calls using their personal access tokens.
Which statement describes the contents of the workspace audit logs concerning these events?

A. Because the REST API was used for job creation and triggering runs, user identity will not be captured in the audit logs.
B. Because User A created the jobs, their identity will be associated with both the job creation events and the job run events.
C. Because User B last configured the jobs, their identity will be associated with both the job creation events and the job run events.
D. Because these events are managed separately, User A will have their identity associated with the job creation events and User B will have their identity associated with the job run events.
E. Because the REST API was used for job creation and triggering runs, a Service Principal will be automatically used to identity these events.

Antwort: D

Begründung:
The events are that a data engineer, User A, has promoted a new pipeline to production by using the REST API to programmatically create several jobs, and a DevOps engineer, User B, has configured an external orchestration tool to trigger job runs through the REST API. Both users authorized the REST API calls using their personal access tokens. The workspace audit logs are logs that record user activities in a Databricks workspace, such as creating, updating, or deleting objects like clusters, jobs, notebooks, or tables. The workspace audit logs also capture the identity of the user who performed each activity, as well as the time and details of the activity.
Because these events are managed separately, User A will have their identity associated with the job creation events and User B will have their identity associated with the job run events in the workspace audit logs.

112. Frage
A data engineer is working in an interactive notebook with many transformations before outputting the result from display(df.collect() ). The notebook includes wide transformations and a cross join.
The data engineer is getting the following error: "The spark driver has stopped unexpectedly and is restarting. Your notebook will be automatically reattached." Which action should the data engineer take?

A. Run the notebook on a single node cluster to keep driver from falling.
B. Check into the Spark UI to see how many jobs are assigned to each stage as they are employing fewer executors.
C. Rewrite their code to avoid putting memory pressure on the driver node.
D. Look at the compute metrics UI to see if the executors have higher than 90% memory utilization.

Antwort: C

Begründung:
Calling df.collect() on a large DataFrame forces all data to be loaded into the driver's memory.
With wide transformations and a cross join, this can easily exceed the driver's capacity, causing it to crash. The data engineer should rewrite the code to avoid collecting large datasets on the driver, using operations like display(df) or writing to storage instead.

113. Frage
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