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Microsoft Implementing Data Engineering Solutions Using Azure Databricks Sample Questions:

1. What improves join performance for small lookup tables?

A) Cartesian join
B) Sort merge join
C) Shuffle join
D) Broadcast join

2. You have an Azure Databricks workspace.
You are creating a Lakeflow Spark Declarative Pipelines (SDP) pipeline that scales automatically.
You need to configure compute for the pipeline. The solution must minimize operational costs and effort.
What should you use?

A) the existing SQL warehouse
B) a job cluster that uses autoscaling
C) an all-purpose cluster that uses autoscaling
D) a single-node, all-purpose cluster

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. Hotspot Question
You have an Azure Databricks workspace that contains a job in Lakeflow Jobs named Job1.
Job1 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.

5. What improves join performance for small lookup tables?

A) Cartesian join
B) Sort merge join
C) Shuffle join
D) Broadcast join

Solutions: