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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 catalog named Catalog1. Catalog1 contains a table named Transactions. Transactions contains the following columns:
- transaction_id
- customer_name
- email_address
- credit_card_number
- transaction_amount
You need to ensure that business analysts can query all the rows in the Transactions table. The solution must meet the following requirements:
- Prevent the analysts from seeing the full values in the email_address and credit_card_number columns.
- Ensure that the analysts can see only the values after the @
character in each email address.
- Ensure that the analysts can see only the last four digits of each
credit card number.
- Enable the analysts to query the table without errors.
- Follow the principle of least privilege.
What should you do?

A) Grant the analysts the SELECT permission for the Transactions table and apply column masks to email_address and credit_card_number.
B) Grant the analysts the SELECTpermission for the Transactions table and implement row-level filters.
C) Grant the analysts the SELECT permission for columns that do NOT contain sensitive data.
D) Grant the analysts the SELECTpermission for the Transactions table and apply column-level encryption.

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

3. 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.

4. You have an Azure Databricks workspace that uses serverless compute.
You need to ingest data by using Lakeflow Jobs. New records must be processed as soon as they become available.
Which type of job trigger should you use for the ingestion?

A) file arrival
B) continuous
C) manual
D) scheduled

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

Solutions: