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Microsoft Implementing Data Engineering Solutions Using Azure Databricks Sample Questions:

1. Which component enforces table-level permissions in Databricks?

A) Cluster policy
B) Spark configuration
C) DBFS permissions
D) Unity Catalog

2. 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 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.dropna(subset=["order_amount"])
Does this meet the goal?

A) Yes
B) No

3. 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:
- transaction_id (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) Add a NOT NULL constraint to transaction_id and a CHECK constraint to amount.
B) Use a SELECT statement with WHERE conditions to validate the data before querying.
C) Create a view that filters out rows where transaction_id is null or amount is less than or equal to
0.
D) Configure row-level security (RLS) where transaction_id is null or amount is less than or equal to
0.

4. Hotspot Question
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.

5. A data engineer notices slow query performance on a large Delta table in Azure Databricks. The table has frequent updates and deletes. Which action best improves query performance?

A) Enable Delta caching only
B) Run OPTIMIZE and ZORDER BY on frequently filtered columns
C) Convert table to Parquet format
D) Increase cluster size

Solutions: