DP-750 試験問題を無料オンラインアクセス

You use Declarative Automation Bundles to manage two jobs and an app.
You need to deploy the bundle to development and production environments. The solution must meet the following requirements:
* Deploy the app to both environments.
* Deploy only one job to development.
* Minimize administrative effort.
What should you use?

• A.separate databricks.yml files for each environment
• B.a resources node in a databricks.yml file
• C.a targets node in a databricks.yml file
• D.a variables node in a databricks.yml file

You have an Azure Databricks workspace that contains a job in Lakeflow Jobs named Job1.
Job! contains three tasks named Task1, Task2. and Task3.
If Task1 fails, Task2 and Task3 must be prevented from running. Successfully completed tasks must NOT rerun during recovery.
You need to configure Job1 to support controlled failure handling and recovery What should you configure? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

正解：

Explanation:
Two configurations are needed:
Task dependency with ' All succeeded ' run condition: Set Task2 and Task3 to depend on Task1. Change the run condition on Task2 and Task3 to ' All succeeded ' - this means they only execute when all their upstream dependencies (Task1) have succeeded. If Task1 fails, both downstream tasks are skipped automatically, not run with failed inputs.
Repair run for recovery: Lakeflow Jobs ' Repair Run feature lets you re-execute only the tasks that failed (Task1 in this case) and their dependents (Task2 and Task3 if they were skipped), while skipping Task1 and any other tasks that already completed successfully. Successfully completed tasks are never re-executed during repair - their results are reused as-is.
Together these provide both controlled failure propagation (nothing runs downstream of a failure) and efficient recovery.
Reference: https://learn.microsoft.com/en-us/azure/databricks/jobs/repair-job-failures

You need to develop the task logic for a new job in Lakeflow Jobs that processes telemetry data.
Each task must contain only the appropriate logic for its step in the pipeline. The solution must support the planned changes and meet the data ingestion and processing requirements.
What should you do?

• A.Use a single SQL task that performs ingestion, cleansing, and curation by running merge commands.
• B.Create separate tasks for ingestion, cleansing, and curation.
• C.Use a single Databricks notebook task that performs ingestion, cleansing, and curation in one script.
• D.Create three tasks that each contains the identical logic and use task retries.

You have an Azure Databricks workspace that is enabled for Unity Catalog You have an Apache Spark Structured Streaming job that writes data to a Delta table.
After the cluster restarts, the streaming job reprocesses previously ingested data You need to prevent the streaming job from reprocessing the data after the cluster restarts.
What should you do?

• A.Configure a watermark for the streaming query.
• B.Configure a checkpoint location for the streaming query.
• C.Increase the trigger interval of the streaming query.
• D.Enable change data feed (CDF) for the target table.

You have an Azure Databricks workspace that is enabled for Unity Catalog and contains a managed Delta table named Table1.
Table1 stores customer profile data.
Business users must analyze how customer profile records change over time. They must also be able to query earlier versions of the table.
You need to implement a solution that:
* Maintains persistent historical versions of customer profile records for long-term analysis.
* Allows users to query earlier versions of the Delta table.
* Minimizes maintenance effort.
What should you do? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

正解：

Explanation:
To record historical changes: Implement a Type 2 slowly changing dimension (SCD).
To support temporal analysis: Use Delta Lake time travel.
A Type 2 slowly changing dimension preserves customer-profile history by inserting a new record whenever a tracked attribute changes instead of overwriting the existing record. Effective dates, expiration dates, version values, or current-record indicators can identify which version applied during a particular period. This provides persistent business history for long-term analysis. Delta Lake time travel supports temporal analysis of the physical table by allowing users to query an earlier version with VERSION AS OF or TIMESTAMP AS OF. Time travel is useful for auditing and reproducing previous results, but its availability depends on retained Delta log entries and data files. Therefore, it should not replace a Type 2 SCD for permanent customer history. Together, the two features satisfy the historical-record and earlier-version requirements.