Microsoft DP-700 Implementing Data Engineering Solutions Using Microsoft Fabric Exam Practice Test

Page: 1 / 14
Total 67 questions
Question 1

You need to schedule the population of the medallion layers to meet the technical requirements.

What should you do?



Answer : A

The technical requirements specify that:

Why Use a Data Pipeline That Calls Other Data Pipelines?

- Sequential execution of child pipelines.

- Error handling to send email notifications upon failures.

- Parallel execution of tasks where possible (e.g., simultaneous imports into the bronze layer).


Question 2

You have a Fabric notebook named Notebook1 that has been executing successfully for the last week.

During the last run, Notebook1executed nine jobs.

You need to view the jobs in a timeline chart.

What should you use?



Answer : E

The run series from the details of the application run is the most detailed and relevant feature for visualizing job execution in a timeline format, making it the correct choice for this scenario. It provides an intuitive way to analyze job execution patterns and improve the efficiency of the notebook.


Question 3

You have a Fabric warehouse named DW1 that loads data by using a data pipeline named Pipeline1. Pipeline1 uses a Copy data activity with a dynamic SQL source. Pipeline1 is scheduled to run every 15minutes.

You discover that Pipeline1 keeps failing.

You need to identify which SQL query was executed when the pipeline failed.

What should you do?



Answer : B

The input JSON contains the configuration details and parameters passed to the Copy data activity during execution, including the dynamically generated SQL query.

Viewing the input JSON for the failed pipeline run provides direct insight into what query was executed at the time of failure.


Question 4

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?



Answer : A

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;


Question 5

You have a Fabric workspace that contains a warehouse named Warehouse1. Data is loaded daily into Warehouse1 by using data pipelines and stored procedures.

You discover that the daily data load takes longer than expected.

You need to monitor Warehouse1 to identify the names of users that are actively running queries.

Which view should you use?



Answer : E

sys.dm_exec_sessions provides real-time information about all active sessions, including the user, session ID, and status of the session. You can filter on session status to see users actively running queries.


Question 6

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 1MB each.

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

What should you do?



Answer : C

Problem Overview:

Solution:

Commands and Their Roles:

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

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

- 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.


Question 7

Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution.

After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen.

You have a KQL database that contains two tables named Stream and Reference. Stream contains streaming data in the following format.

Reference contains reference data in the following format.

Both tables contain millions of rows.

You have the following KQL queryset.

You need to reduce how long it takes to run the KQL queryset.

Solution: You add the make_list() function to the output columns.

Does this meet the goal?



Answer : B

Adding an aggregation like make_list() would require additional processing and memory, which could make the query slower.


Page:    1 / 14   
Total 67 questions