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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 that is enabled for Unity Catalog and contains a managed Delta table named Payments.
Payments stores transaction data and contains a column named payment_amount of the Decimal data type.
You must enforce the following business rule:
payment_amount must be between 0 and 10,000, inclusive
You need to ensure that records that violate the rule are rejected when data is written to the Payments table.
What should you do?

A) Configure row-level security (RLS) to hide records that have invalid values.
B) Create a table update trigger.
C) Run SELECT statements to filter out records that have invalid values in payment_amount.
D) Add a CHECK constraint to payment_amount to enforce the allowed range.

2. You have an Azure Databricks workspace that is enabled for Unity Catalog and contains a Delta table named Orders.
You load the Orders table into an Apache Spark DataFrame named df.
You need to create a DataFrame that excludes rows where the order amount is null.
Solution: You run the following expression.
df.filter(df.order_amount.isNotNull())
Does this meet the goal?

A) Yes
B) No

3. You have an Azure Databricks workspace that contains a Git folder and uses Azure Repos as the Git provider.
From the main branch, you create a branch named Branch1. You commit changes to Branch1.
You need to incorporate the changes from Branch1 into main The solution must preserve the commit history in the repository. Which command should you run?

A) Push
B) merge
C) pull
D) rebase

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

5. You have an Azure Databricks workspace that is enabled for Unity Catalog and contains a managed Delta table named Tabid.
Table! is written by batch jobs every hour and is queried frequently by filtering two columns named Customerld and EventDate.
You expect Table1 to grow significantly over time.
The rows in Table1 are frequently updated and deleted to support compliance requests.
You need to keep query performance consistent as Table1 grows. The solution must minimize update and deletion effort.
What should you include in the solution? To answer, select the appropriate options in the answer area NOTE: Each correct selection is worth one point.

Solutions: