Free Databricks Databricks-Certified-Professional-Data-Engineer Exam Dumps Questions & Answers

Which is a key benefit of an end-to-end test?

• A.  It pinpoint errors in the building blocks of your application.
• B.  It closely simulates real world usage of your application.
• C.  It provides testing coverage for all code paths and branches.
• D.  It makes it easier to automate your test suite

In order to facilitate near real-time workloads, a data engineer is creating a helper function to leverage the schema detection and evolution functionality of Databricks Auto Loader. The desired function will automatically detect the schema of the source directly, incrementally process JSON files as they arrive in a source directory, and automatically evolve the schema of the table when new fields are detected.
The function is displayed below with a blank:

Which response correctly fills in the blank to meet the specified requirements?

• A.  Option A
• B.  Option B
• C.  Option E
• D.  Option C
• E.  Option D

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.  Three new jobs named " Ingest new data " will be defined in the workspace, and they will each run once daily.
• 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 new clusters with the configurations of the provided cluster ID.
• D.  The logic defined in the referenced notebook will be executed three times on the referenced existing all purpose cluster.
• E.  Three new jobs named " Ingest new data " will be defined in the workspace, but no jobs will be executed.

A data architect is designing a Databricks solution to efficiently process data for different business requirements.
In which scenario should a data engineer use a materialized view compared to a streaming table ?

• A.  Implementing a CDC (Change Data Capture) pipeline that needs to detect and respond to database changes within seconds.
• B.  Ingesting data from Apache Kafka topics with sub-second processing requirements for immediate alerting.
• C.  Processing high-volume, continuous clickstream data from a website to monitor user behavior in real- time.
• D.  Precomputing complex aggregations and joins from multiple large tables to accelerate BI dashboard performance.

A data engineer is designing an append-only pipeline that needs to handle both batch and streaming data in Delta Lake. The team wants to ensure that the streaming component can efficiently track which data has already been processed.
Which configuration should be set to enable this?

• A.  overwriteSchema
• B.  mergeSchema
• C.  partitionBy
• D.  checkpointLocation