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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Secure and govern Unity Catalog objects | 15โ20% | - Implement data governance and security
|
| Topic 2: Set up and configure an Azure Databricks environment | 15โ20% | - Integrate with Azure services
|
| Topic 3: Deploy and maintain data pipelines and workloads | 30โ35% | - Monitor, troubleshoot, and maintain workloads
|
| Topic 4: Prepare and process data | 30โ35% | - Optimize and manage data storage
|
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NEW QUESTION # 54
What happens if incoming data violates Delta table schema?
Answer: D
Explanation:
Delta Lake enforces schema by default. If incoming data does not match schema, the write operation fails unless schema evolution is explicitly enabled. It does not auto-cast or append invalid data. Overwriting would require explicit command.
NEW QUESTION # 55
You have an Azure Databricks workspace and a remote Git repository named Repo1. Repo1 contains two branches named main and Branch1.
You are on a development team that works in Repo1.
You commit changes to Branch1 and must merge the changes into main.
Before completing the merge, you need to meet the following requirements:
* Ensure that Branch1 includes the changes committed to main since Branch1 was created.
* Ensure that merge conflicts are detected and resolved.
What should you do first?
Answer: C
Explanation:
The latest remote changes from main must first be retrieved so that the development environment has the current main-branch state. After pulling those updates, main can be merged into Branch1, and any conflicts can be detected and resolved before Branch1 is proposed for integration into main. Immediately merging a stale local copy of main into Branch1 could omit commits added remotely after Branch1 was created. Pulling Branch1 only synchronizes the feature branch and does not retrieve the required main-branch changes.
Creating a pull request before updating and testing Branch1 would defer conflict discovery until later in the integration process. Pulling the latest main changes is therefore the correct first operation in the sequence.
Microsoft Learn
NEW QUESTION # 56
Which ingestion option should you recommend for each data source? To answer, drag the appropriate options to the correct data sources. Each option may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
The right ingestion tool depends on the source characteristics:
File-based telemetry and maintenance data # Auto Loader (cloudFiles). It monitors ADLS Gen2 for new arrivals, handles schema inference and evolution for the frequent schema drift, and requires minimal operational effort.
Real-time telemetry from Event Hubs # Spark Structured Streaming with the azure-eventhubs-spark connector. This provides exactly-once semantics and checkpoint-based recovery, satisfying 'resume processing after failures without reprocessing.' Structured maintenance data from PostgreSQL # JDBC connector. Databricks supports direct JDBC reads from relational databases with pushdown predicates.
Daily CSV ERP extracts (50-100 GB) # COPY INTO or Auto Loader. Both support idempotent incremental batch loading into Delta tables with minimal code.
Reference: https://learn.microsoft.com/en-us/azure/databricks/ingestion/auto-loader/
NEW QUESTION # 57
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 new native catalog in Unity Catalog.
Does this meet the goal?
Answer: A
Explanation:
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/
NEW QUESTION # 58
You have an Azure Databricks workspace that is enabled for Unity Catalog.
You have 500 GB of sales data stored as multiple CSV files in cloud storage.
You plan to load the data into a Delta table.
You need to ingest the bulk data by using a solution that meets the following requirements:
* Minimize how long it takes to implement the solution.
* Minimize the amount of custom code required.
What should you use?
Answer: A
Explanation:
COPY INTO provides a concise SQL-based mechanism for loading files from cloud storage directly into a Delta table. It requires substantially less custom code than constructing a Spark ingestion application and is suitable for a straightforward bulk load of multiple CSV files. COPY INTO is also retryable and idempotent:
it tracks files already loaded into the target table and skips them during later executions, helping prevent accidental duplication. Auto Loader is optimized primarily for incremental and continuously arriving files and normally requires a streaming or triggered pipeline. Apache Spark read APIs require additional code for reading, transforming, tracking, and writing the files. Manually uploading 500 GB would be inefficient and operationally unsuitable. Therefore, COPY INTO best satisfies both implementation-speed and minimal-code requirements. Microsoft Learn
NEW QUESTION # 59
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