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| Section | Weight | Objectives |
|---|---|---|
| Secure and govern Unity Catalog objects | 15–20% | - Implement data governance and security
|
| Prepare and process data | 30–35% | - Optimize and manage data storage
|
| Set up and configure an Azure Databricks environment | 15–20% | - Select and configure compute resources
|
| Deploy and maintain data pipelines and workloads | 30–35% | - Build and orchestrate pipelines
|
Microsoft DP-750認證考試是目前IT人士報名參加的考試中很受歡迎的一個認證考試。通過了Microsoft DP-750認證考試不僅能使你工作和生活帶來提升，而且還能鞏固你在IT 領域的地位。但是事實情況是它通過率確很低。
問題 #86
You have an Azure Databricks workspace named Workspace1 that contains a lakehouse and is enabled for Unity Catalog.
You have a connection to a Microsoft SQL Server database named DB1.
You need to expose the schemas and tables of DB1 to meet the following requirements:
* The schemas and tables can be queried in Databricks.
* The schemas and tables appear alongside other Unity Catalog objects.
* The data is NOT copied into Databricks-managed storage.
Solution: You create a Lakeflow Connect pipeline and connect it to DB1. Does this meet the goal?
答案：B
解題說明：
The correct answer is B - No.
Lakeflow Connect is an ingestion service that physically copies data from external databases into Delta tables managed by Databricks. It's designed for scenarios where you want a replicated, writable Delta copy of external data - essentially a CDC-based ingestion pipeline.
That's the opposite of what's required here. The requirement states 'the data is NOT copied into Databricks- managed storage.' Lakeflow Connect would create Delta tables in Databricks and copy DB1's data into them
- a direct violation.
Additionally, Lakeflow Connect creates Databricks-native Delta tables rather than exposing DB1's original schemas and tables as virtual objects. Analysts querying through a Lakeflow Connect pipeline are querying a replicated copy, not the live source.
For zero-copy, live query federation of an external SQL Server into Unity Catalog, Lakehouse Federation (foreign catalog) is the correct tool.
Reference: https://learn.microsoft.com/en-us/azure/databricks/ingestion/lakeflow-connect/
問題 #87
You have an Azure Databricks workspace that contains a job in Lakeflow Jobs named Job1.
Job! runs every hour.
Occasionally, the job run takes longer than one hour to complete. Overlapping runs must be prevented to avoid data corruption.
You need to configure the job scheduling behavior.
What should you configure? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
答案：
解題說明：
Explanation:
Two settings address the overlapping-run problem:
Concurrent Runs policy set to 'Skip' (or 'Allow only one concurrent run'). When a new scheduled trigger fires while the previous run is still in progress, the new run is skipped rather than starting alongside the ongoing one. This prevents two runs from writing to the same tables at the same time - which is the data corruption risk the question highlights.
Cron-based schedule for the hourly trigger. A cron expression defines the regular execution cadence.
Combined with the concurrency setting, the job runs hourly but never overlaps.
An alternative to 'Skip' is 'Wait' (queue the new run), which ensures every scheduled run eventually executes
- but for this scenario where overlapping is the primary concern, skipping the missed run is typically preferable to building up a queue of back-to-back executions.
Reference: https://learn.microsoft.com/en-us/azure/databricks/jobs/configure-jobs#concurrent-runs
問題 #88
You have an Azure Databricks workspace that contains a job in Lakeflow Jobs named Job1.
Job1 runs every hour.
Occasionally, Job1 takes longer than one hour to complete.
You need to configure the job scheduling behavior to meet the following requirements:
* Overlapping runs must be prevented to avoid data corruption.
* Scheduled runs must not be discarded when another run is already active.
What should you configure? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
答案：
解題說明：
Explanation:
Concurrency setting: Limit concurrent runs to one.
Execution behavior: Queue the new run.
Limiting concurrent runs to one ensures that only one instance of Job1 can execute at a time. This prevents two hourly runs from simultaneously modifying the same tables, files, checkpoints, or downstream systems, thereby reducing the risk of duplicate processing and data corruption. When a scheduled trigger occurs while an earlier execution is still active, queueing the new run preserves that execution and starts it after the active run finishes. Allowing concurrent runs would violate the non-overlap requirement. Restarting the job during an overlap could interrupt partially completed work. Canceling the new run or skipping it would avoid simultaneous execution, but the scheduled processing interval could be lost. Single-run concurrency combined with queueing therefore serializes the executions without discarding scheduled work.
問題 #89
Hotspot Question
You have an Azure Databricks workspace that is enabled for Unity Catalog and contains a Delta table named db1.sales_orders.
db1.sales_orders is updated nightly and has change data feed (CDF) enabled.
You need to ingest all the changes from the db1.sales_orders table, including inserts, updates, and deletes, into a downstream pipeline.
How should you complete the PsySpark code segment? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
答案：
解題說明：
問題 #90
Which component enforces table-level permissions in Databricks?
答案：B
解題說明：
Unity Catalog provides fine-grained access control at table, schema, and column levels. It centralizes governance across workspaces. Cluster policies control compute settings. Spark configuration does not manage security. DBFS permissions are not sufficient for enterprise governance.
問題 #91
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DP-750認證: https://www.kaoguti.com/DP-750_exam-pdf.html