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Microsoft Implementing Data Engineering Solutions Using Azure Databricks Sample Questions:

1. 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?

A) Yes
B) No

2. You have an Azure Databricks workspace named Workspace1 that uses a Git repository. The repository 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, the merge fails due to conflicts.
You need to merge Branch1 into the main branch. The solution must ensure that Notebook1 includes all the changes from both the branches.
What should you do?

A) Apply the main branch changes to Branch1 and resolve the conflicts.
B) From Workspace1, clone Branch1 as a new repository.
C) Apply the changes directly to the main branch.
D) From Workspace1, clone the main branch as a new repository.

3. You have an Azure Databricks workspace that is enabled for Unity Catalog and contains a Delta table named Sales_orders.
Sales_orders stores historical sales data.
You receive a daily CSV file daily that contains new sales records only. The file does NOT contain updates to existing rows.
You need to load the daily data into Sales_orders. The solution must meet the following requirements:
- Preserve the existing data.
- Add only the new records.
- Minimize processing effort.
Which command should include in the loading strategy?

A) INSERT INTO
B) INSERT OVERWRITE
C) UPDATE

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

5. What does the VACUUM command do in Delta Lake?

A) Deletes old unreferenced files
B) Optimizes query performance
C) Rewrites table schema
D) Compresses JSON files

Solutions: