DP-750 무료 덤프문제 온라인 액세스

You have an Azure Databricks account that contains workspaces enabled for Unity Catalog.
You need to implement audit logging to meet the following requirements:
* Capture audit logs for all the workspaces in the account.
* Retain the audit logs for 90 days.
* Minimize storage and ingestion costs.
The logs will be reviewed only during security investigations and will NOT be queried regularly.
To where should you send the audit logs?

• A.Azure Monitor metrics
• B.Log Analytics
• C.an Azure Storage account
• D.Azure Event Hubs

You have an Azure Databricks workspace that is enabled for Unity Catalog and contains a catalog named CatalogV Catalog1 contains a schema named Schema! and a table named Table1.
You need to ensure that access to the data in Table1 is controlled by using attribute based access control (ABAC).
What should you apply to Table1, and how should you control access for users? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

정답:

Explanation:
Attribute-based access control (ABAC) in Unity Catalog is implemented through row filters. A row filter is a SQL function registered on a table that evaluates the identity or group membership of the querying user and returns only the rows they ' re entitled to see.
The key functions are CURRENT_USER() (returns the logged-in user ' s email) and IS_ACCOUNT_GROUP_MEMBER() (returns true if the user belongs to a specified group). By building filter logic around these, you create access rules that are data-driven - a user in the ' EMEA ' group sees EMEA rows, a user in ' APAC ' sees APAC rows - without maintaining separate table-level grants per data segment.
This is what distinguishes ABAC from role-based access control: decisions are based on the user ' s attributes evaluated at query time, not on static grant lists. The filter is transparent to end users - they query the table normally and only receive rows the policy allows.
Reference: https://learn.microsoft.com/en-us/azure/databricks/data-governance/unity-catalog/row-and-column- filters

You have an Azure Databricks account that contains a single workspace named Workspace1. Workspace1 is enabled for Unity Catalog.
You discover that data access events for Unity Catalog tables fail to appear in the logs.
You need to ensure that all the data access events are captured centrally for auditing purposes. The log data must be available for analysis as quickly as possible.
What should you do? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

정답:

Explanation:
Enable audit logging for: The Databricks account
Send audit logs to: Log Analytics
Enabling audit logging at the Databricks account level provides centralized coverage of account and workspace activities, including Unity Catalog data-access events. Enabling logging only for Workspace1 would provide a narrower configuration and would not offer the same centralized account-level auditing model. Individual Unity Catalog tables and the Azure subscription are not the appropriate Databricks audit- log scope selections. Log Analytics is the suitable destination because it supports rapid ingestion and immediate querying of log records through Azure Monitor Logs. Auditors can search, filter, correlate, and analyze the captured events without first developing a separate data-processing solution. An Azure Storage account is more suitable for inexpensive archival, while Event Hubs is primarily a streaming transport that normally requires an additional consumer before investigators can analyze the records.

You have an Azure Databricks workspace that is enabled for Unity Catalog.
You have a complex job named Job1 that contains eight tasks. Job1 takes multiple hours to complete.
During the last job run, the final task fails due to a transient issue.
You need to retry the last task without rerunning tasks that have already completed.
What should you do?

• A.Disable and reenable the job schedule.
• B.Restart Job1.
• C.Repair the current job run.
• D.Update the job parameters.

You have a Lakeflow Spark Declarative Pipelines {SDP) pipeline in Azure Databricks. The pipeline ingests transaction data into a table named Table1.
You need to ensure that in the event of an invalid record, the pipeline continues to run. The solution must meet the following requirements:
* Invalid records must NOT be written to Table 1.
* Invalid records must be preserved for review.
* Minimize development effort
What should you do?

• A.Add a check constraint to Table1
• B.Define a pipeline expectation.
• C.Implement advanced logic to quarantine the invalid records.
• D.Run were clauses in downstream queries to filter out invalid records.