Databricks-Certified-Professional-Data-Engineer試験無料問題集「Databricks Certified Professional Data Engineer 認定」
A data engineer is configuring a Databricks Asset Bundle to deploy a job with granular permissions. The requirements are:
* Grant the data-engineers group CAN_MANAGE access to the job.
* Ensure the auditors' group can view the job but not modify/run it.
* Avoid granting unintended permissions to other users/groups.
How should the data engineer deploy the job while meeting the requirements?
* Grant the data-engineers group CAN_MANAGE access to the job.
* Ensure the auditors' group can view the job but not modify/run it.
* Avoid granting unintended permissions to other users/groups.
How should the data engineer deploy the job while meeting the requirements?
正解：D
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A junior data engineer has manually configured a series of jobs using the Databricks Jobs UI. Upon reviewing their work, the engineer realizes that they are listed as the " Owner " for each job. They attempt to transfer " Owner " privileges to the " DevOps " group, but cannot successfully accomplish this task.
Which statement explains what is preventing this privilege transfer?
Which statement explains what is preventing this privilege transfer?
正解：A
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A platform team is creating a standardized template for Databricks Asset Bundles to support CI/CD. The template must specify defaults for artifacts, workspace root paths, and a run identity, while allowing a "dev" target to be the default and override specific paths.
How should the team use databricks.yml to satisfy these requirements?
How should the team use databricks.yml to satisfy these requirements?
正解：C
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A Databricks SQL dashboard has been configured to monitor the total number of records present in a collection of Delta Lake tables using the following query pattern:
SELECT COUNT (*) FROM table -
Which of the following describes how results are generated each time the dashboard is updated?
SELECT COUNT (*) FROM table -
Which of the following describes how results are generated each time the dashboard is updated?
正解：C
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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?
The function is displayed below with a blank:
Which response correctly fills in the blank to meet the specified requirements?
正解：E
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A small company based in the United States has recently contracted a consulting firm in India to implement several new data engineering pipelines to power artificial intelligence applications. All the company ' s data is stored in regional cloud storage in the United States.
The workspace administrator at the company is uncertain about where the Databricks workspace used by the contractors should be deployed.
Assuming that all data governance considerations are accounted for, which statement accurately informs this decision?
The workspace administrator at the company is uncertain about where the Databricks workspace used by the contractors should be deployed.
Assuming that all data governance considerations are accounted for, which statement accurately informs this decision?
正解：D
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A data governance team at a large enterprise is improving data discoverability across its organization. The team has hundreds of tables in their Databricks Lakehouse with thousands of columns that lack proper documentation. Many of these tables were created by different teams over several years, with missing context about column meanings and business logic. The data governance team needs to quickly generate comprehensive column descriptions for all existing tables to meet compliance requirements and improve data literacy across the organization. They want to leverage modern capabilities to automatically generate meaningful descriptions rather than manually documenting each column, which would take months to complete.
Which approach should the team use in Databricks to automatically generate column comments and descriptions for existing tables?
Which approach should the team use in Databricks to automatically generate column comments and descriptions for existing tables?
正解：B
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A data engineer is building a Lakeflow Declarative Pipelines pipeline to process healthcare claims data. A metadata JSON file defines data quality rules for multiple tables, including:
{
" claims " : [
{ " name " : " valid_patient_id " , " constraint " : " patient_id IS NOT NULL " },
{ " name " : " non_negative_amount " , " constraint " : " claim_amount > = 0 " }
]
}
The pipeline must dynamically apply these rules to the claims table without hardcoding the rules.
How should the data engineer achieve this?
{
" claims " : [
{ " name " : " valid_patient_id " , " constraint " : " patient_id IS NOT NULL " },
{ " name " : " non_negative_amount " , " constraint " : " claim_amount > = 0 " }
]
}
The pipeline must dynamically apply these rules to the claims table without hardcoding the rules.
How should the data engineer achieve this?
正解：D
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