Microsoft DP-750 test insides dumps : 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 that is enabled for Unity Catalog and contains a catalog named Catalog 1. Catalog 1 contains a table named Transactions. Transactions contains the following columns:
* transaction_id
* customet_name
* email address
* credit_card_number
* transaction_amount
You need to ensure that business analysts can query all the tows 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_catd_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-level encryption
B) Grant the analysts the SELECT permission 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 select permission for the Transactions table and apply column masks to email_address and credit_card_number

2. You have an Azure Databricks workspace that uses Unity Catalog.
You have a Lakeflow Spark Declarative Pipelines (SDP) pipeline that ingests data into a managed Delta table named Table1. Table1 is used for analytics.
New columns are added to the source data, causing pipeline failures during writes to Table1.
You need to prevent the pipeline failures. The solution must ensure that schema changes are detected and handled.
What should you do?

A) Create a separate table for each schema version.
B) Enable schema evolution.
C) Use row filters to exclude records that have new columns.
D) Disable schema enforcement for Table1.

3. You have an Azure Databricks workspace that contains a job in Lakeflow Jobs named Job1.
Job1 processes raw data files stored in Azure Storage.
New files arrive at unpredictable intervals.
You need to ensure that Job1 starts automatically when new files arrive and does NOT consume compute resources when no data is available.
Which type of job trigger should you use?

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

4. You have an Azure Databricks workspace that is enabled for Unity Catalog and contains a managed Delta table named Table1.
Table1 stores customer profile data.
Business users must analyze how customer profile records change over time. They must also be able to query earlier versions of the table.
You need to implement a solution that:
* Maintains persistent historical versions of customer profile records for long-term analysis.
* Allows users to query earlier versions of the Delta table.
* Minimizes maintenance effort.
What should you do? 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 contains a Delta table named Customer.
A job named Job1 performs frequent upserts into Customer.
You discover that Job1 has created many small Parquet files in Customer, and the small files are degrading query performance.
You need to improve query performance for the current data already stored in Customer. The solution must not affect the travel for the Customer table.
What should you do?

A) Run the OPTIMIZE command on the Customer table.
B) Set the delta.autoOptimize.optimizeWrite Apache Spark configuration to true.
C) Set the delta.deletedFileRetentionDuration table property to 1 day.
D) Run the VACUUM command on the Customer table.

Solutions: