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Microsoft Certified: Fabric Data Engineer Associate
DP-700
Implementing Data Engineering Solutions Using Microsoft Fabric Questions and Answers

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Viewing questions 21-30 out of questions

Questions # 21:

You have a Fabric workspace that contains a warehouse named Warehouse1 Warehousel contains a table named DimCustomers. DimCustomers contains the following columns:

• CustomerName

• CustomerlD

• Birth Date

• Email

You need to configure security to meet the following requirements:

• Birth Date in DimCustomer must be masked and display 1960-01-01

• Email in DimCustomer must be masked and display only the first leading character and the last five characters.

How should you complete the statement? To answer, select the appropriate options in the answer area.

NOTE: Each correct selection is worth one point.

Question # 21

Options:

Answer

Answer:

Questions # 22:

You have a Fabric workspace that contains a warehouse named Warehouse!. Warehousel contains a table named DimCustomers. DimCustomers contains the following columns:

• CustomerName

• CustomerlD

• BirthDate

• Email

You need to configure security to meet the following requirements:

• BirthDate in DimCustomer must be masked and display 1900-01-01.

• Email in DimCustomer must be masked and display only the first leading character and the last five characters.

How should you complete the statement? To answer, select the appropriate options in the answer area. NOTE: Each correct selection is worth one point.

Question # 22

Options:

Answer

Answer:

Questions # 23:

You are building a data orchestration pattern by using a Fabric data pipeline named Dynamic Data Copy as shown in the exhibit. (Click the Exhibit tab.)

Question # 23

Dynamic Data Copy does NOT use parametrization.

You need to configure the ForEach activity to receive the list of tables to be copied.

How should you complete the pipeline expression? To answer, select the appropriate options in the answer area.

NOTE: Each correct selection is worth one point.

Question # 23

Options:

Answer

Answer:

Questions # 24:

You are developing a data pipeline named Pipeline1.

You need to add a Copy data activity that will copy data from a Snowflake data source to a Fabric warehouse.

What should you configure?

Options:

Degree of copy parallelism

Fault tolerance

Enable staging

Enable logging

Answer

Explanation

When using the Copy data activity in a data pipeline to move data from Snowflake to a Fabric warehouse, the process often involves intermediate staging to handle data efficiently, especially for large datasets or cross-cloud data transfers.

Staging involves temporarily storing data in an intermediate location (e.g., Blob storage or Azure Data Lake) before loading it into the target destination.

For cross-cloud data transfers (e.g., from Snowflake to Fabric), enabling staging ensures data is processed and stored temporarily in an efficient format for transfer.

Staging is especially useful when dealing with large datasets, ensuring the process is optimized and avoids memory limitations.

Questions # 25:

You have a Fabric workspace that contains a write intensive warehouse named DW1. DW1 stores staging tables that are used to load a dimensional model. The tables are often read once, dropped, and then recreated to process new data.

You need to minimize load time of data from sources to staging tables in DW1.

What should you do?

Options:

Drop statistics.

Enable V Order.

Create statistics.

Disable V Order.

Answer

Questions # 26:

You have a Fabric workspace that contains an eventstream named EventStream1. EventStream1 outputs events to a table in a lakehouse.

You need to remove files that are older than seven days and are no longer in use.

Which command should you run?

Options:

VACUUM

COMPUTE

OPTIMIZE

CLONE

Answer

Explanation

VACUUM is used to clean up storage by removing files no longer in use by a Delta table. It removes old and unreferenced files from Delta tables. For example, to remove files older than 7 days:

VACUUM delta.`/path_to_table` RETAIN 7 HOURS;

Questions # 27:

You have a Fabric workspace.

You have semi-structured data.

You need to read the data by using T-SQL, KQL, and Apache Spark. The data will only be written by using Spark.

What should you use to store the data?

Options:

a lakehouse

an eventhouse

a datamart

a warehouse

Answer

Explanation

A lakehouse is the best option for storing semi-structured data when you need to read it using T-SQL, KQL, and Apache Spark. A lakehouse combines the flexibility of a data lake (which can handle semi-structured and unstructured data) with the performance features of a data warehouse. It allows data to be written using Apache Spark and can be queried using different technologies such as T-SQL (for SQL-based querying), KQL (Kusto Query Language for querying), and Apache Spark (for distributed processing). This solution is ideal when dealing with semi-structured data and requiring a versatile querying approach.

Questions # 28:

You have a Fabric workspace that contains a lakehouse named Lakehouse1. Lakehouse1 contains a Delta table named Table1.

You analyze Table1 and discover that Table1 contains 2,000 Parquet files of 1 MB each.

You need to minimize how long it takes to query Table1.

What should you do?

Options:

Disable V-Order and run the OPTIMIZE command.

Disable V-Order and run the VACUUM command.

Run the OPTIMIZE and VACUUM commands.

Answer

Explanation

Problem Overview:

Table1 has 2,000 small Parquet files (1 MB each).

Query performance suffers when the table contains numerous small files because the query engine must process each file individually, leading to significant overhead.

Solution:

To improve performance, file compaction is necessary to reduce the number of small files and create larger, optimized files.

Commands and Their Roles:

OPTIMIZE Command:

- Compacts small Parquet files into larger files to improve query performance.

- It supports optional features like V-Order, which organizes data for efficient scanning.

VACUUM Command:

- Removes old, unreferenced data files and metadata from the Delta table.

- Running VACUUM after OPTIMIZE ensures unnecessary files are cleaned up, reducing storage overhead and improving performance.

Questions # 29:

You have a Fabric workspace named Workspace1.

You plan to configure Git integration for Workspace1 by using an Azure DevOps Git repository. An Azure DevOps admin creates the required artifacts to support the integration of Workspace1 Which details do you require to perform the integration?