Microsoft DP-750 Exam : Implementing Data Engineering Solutions Using Azure Databricks

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Microsoft DP-750 Exam Syllabus Topics:

Microsoft Implementing Data Engineering Solutions Using Azure Databricks Sample Questions:

1. 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?

A) Merge main into Branch1, resolve the conflicts, and then complete the merge of Branch1 into main.
B) From Workspace1, clone the main branch as a new Git folder.
C) Apply the changes directly to the main branch.
D) From Workspace1, clone Branch1 as a new Git folder.

2. You have an Azure Databricks workspace that is enabled for Unity Catalog.
You need to ensure that data lineage is captured and can be reviewed for tables accessed by Databricks notebooks and jobs. The solution must minimize administrative effort.
Which compute configuration should you use to capture the data lineage, and what should you use to review the data lineage? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

3. You have an Azure Databricks workspace that is enabled for Unity Catalog and contains two managed Delta tables named sales.schema1.table1 and sales.schema1.table2.
sales.schema1.table1 contains sales data from the current year.
sales.schema1.table2 contains historical data.
You need to load all the rows from sales.schema1.table1 into sales.schema1.table2. The solution must preserve any existing data in sales.schema1.table2 and minimize processing effort.
Which command should you run?

A) INSERT OVERWRITE sales.schema1.table2 SELECT * FROM sales.schema1.table1;
B) INSERT INTO sales.schema1.table2 SELECT * FROM sales.schema1.table1;
C) CREATE OR REPLACE TABLE sales.schema1.table2 AS SELECT * FROM sales.schema1.table1;
D) CREATE TABLE sales.schema1.table2 AS SELECT * FROM sales.schema1.table1;

4. You have an Azure Databricks workspace.
You need to ingest streaming data from Azure Event Hubs by using Apache Spark Structured Streaming The solution must authenticate to Event Hubs and read the event payload.
How should you complete the PySpark code segment? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

5. You have an Azure Databricks workspace that is enabled for Unity Catalog and contains a managed Delta table named Sales. Sales stores transaction data and contains the following columns:
* transactionjd (string)
* transaction date (date)
* amount (decimal)
You need to implement the following data quality requirements by using table-level data quality enforcement:
* amount must be greater than 0.
* transaction id must never be null.
* Invalid records must be rejected when data is written to the Sales table.
What should you do?

A) Create a view that filters out rows where transactionjd is null or amount is less than or equal to 0.
B) Use a select statement with where conditions to validate the data before querying.
C) Configure row-level security (RLS) where transactionjd is null or amount is less than or equal to 0.
D) Add a not null constraint to transactionjd and a check constraint to amount.

Solutions: