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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Prepare and process data | 30-35% | - Ingest and transform data
|
| Topic 2: Deploy and maintain data pipelines and workloads | 30-35% | - Manage production workloads
|
| Topic 3: Set up and configure an Azure Databricks environment | 15-20% | - Create and configure Azure Databricks workspaces
|
| Topic 4: Secure and govern Unity Catalog objects | 15-20% | - Implement governance and security
|
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41. Frage
You have an Azure Databricks workspace named Workspace1 that contains a Git folder linked to a remote Git repository. The Git folder contains a Databricks notebook named Notebook1.
From the main branch, you create a feature branch named Branch1 and commit changes to Notebook1.
Another user commits changes to Notebook1 in main.
When you attempt to merge Branch1 into main, a merge conflict occurs.
You need to ensure that Notebook1 in main includes the changes from both branches.
What should you do?
Antwort: C
Begründung:
Branch1 must first incorporate the current changes from main. During that merge, the conflicting sections of Notebook1 can be reviewed and resolved so that the resulting feature-branch version contains the required work from both branches. After committing the resolution, Branch1 can be merged into main normally.
Cloning either branch into another Git folder creates another working copy but does not resolve the conflicting histories. Applying changes directly to main bypasses the controlled feature-branch workflow and risks omitting or overwriting one contributor's work. Resolving the conflict on Branch1 also allows the combined notebook to be tested before updating the shared main branch. Therefore, merging main into Branch1, resolving the conflict, and completing the final merge is the correct workflow.
42. Frage
Note: This section contains one or more sets of questions with the same scenario and problem. Each question presents a unique solution to the problem. You must determine whether the solution meets the stated goals. More than one solution in the set might solve the problem. It is also possible that none of the solutions in the set solve the problem.
After you answer a question in this section, you will NOT be able to return. As a result, these questions do not appear on the Review Screen.
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 foreign catalog in Catalog Explorer.
Does this meet the goal?
Antwort: A
Begründung:
Correct:
* You create a foreign catalog in Catalog Explorer.
You should create a Foreign Catalog using Lakehouse Federation.
Data Copying: Lakehouse Federation queries data directly in the source SQL Server without moving or copying it.
Seamless Integration: The database schemas and tables appear right inside Unity Catalog alongside your other data objects.Real-time Access: It provides immediate access to live SQL Server data.
Incorrect:
* You create a Databricks access connector.
* You create a Lakeflow Connect pipeline and connect it to DB1.
Data Copying: Lakeflow Connect is an ingestion tool that physically replicates and copies data into Databricks-managed storage (Delta tables).
Storage Costs: It violates your requirement to keep data out of Databricks storage.
* You create a new native catalog in Unity Catalog.
Note:
To expose the external SQL Server database in Unity Catalog without copying the data, you must use Lakehouse Federation.
Here are the step-by-step actions you need to take:
1. Create a Connection
Create a securable object in Unity Catalog that specifies the path and credentials to access the SQL Server database.
Go to Catalog Explorer or use SQL.
Select External Data > Connections.
Create a connection using the SQL Server connection details (URL, host, port, and database credentials).
*-> 2. Create a Foreign Catalog
Create a specific type of catalog in Unity Catalog that mirrors the external database.
Use the CREATE FOREIGN CATALOG SQL command or the Catalog Explorer UI.
Link this foreign catalog directly to the connection you created in step 1.
3. Query the DataOnce the foreign catalog is created, Unity Catalog automatically syncs the schemas and tables from SQL Server.
Reference:
https://docs.databricks.com/gcp/en/database-objects/
43. Frage
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.
Antwort:
Begründung:
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
44. Frage
What is the best way to reduce Databricks compute cost for intermittent workloads?
Antwort: A
Begründung:
Autoscaling adjusts resources based on workload demand, and auto-termination shuts down idle clusters, reducing cost significantly. All-purpose clusters are always on. Increasing size raises cost. Single-node clusters limit scalability and are not suitable for production workloads.
45. Frage
You have an Azure Databricks workspace that is attached to a Unity Catalog metastore named metastore1.
Metastore1 contains a catalog named catalog 1.
You need to create a new schema named schema2 that meets the following requirements:
* Is contained in catalog1
* Uses abfss://containergstorageaccount.dfs.core.windows.net/data as the Managed location Which SQL statement should you execute?
Antwort: B
Begründung:
The correct answer is A. The Unity Catalog DDL for creating a schema inside a specific catalog and setting a custom managed storage path uses the three-part name (catalog.schema) and the MANAGED LOCATION clause:
CREATE SCHEMA catalog1.schema2 MANAGED LOCATION ' abfss://... ' ;
The three-part name explicitly places the schema inside catalog1. MANAGED LOCATION tells Unity Catalog where to store managed tables and volumes created under this schema - any managed table without its own explicit location will inherit this path.
Option B uses CREATE CATALOG, which creates an entirely new catalog rather than a schema. Option C uses the LOCATION keyword without MANAGED - that syntax is for external locations, not for overriding the managed storage path of a schema. Option D uses WITH DBPROPERTIES, which stores arbitrary key- value metadata but has no effect on where Unity Catalog physically stores data.
Reference: https://learn.microsoft.com/en-us/azure/databricks/sql/language-manual/sql-ref-syntax-ddl-create- schema
46. Frage
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