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| Vendor: | Microsoft |
|---|---|
| Exam Code: | DP-700 |
| Exam Name: | Implementing Data Engineering Solutions Using Microsoft Fabric |
| Exam Questions: | 129 |
| Last Updated: | August 6, 2026 |
| Related Certifications: | Fabric Data Engineer Associate |
| Exam Tags: | Data engineering, Data management Intermediate Level Microsoft Data Analysts and Engineers |
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You have a Fabric deployment pipeline that uses three workspaces named Dev, Test, and Prod.
You need to deploy an eventhouse as part of the deployment process.
What should you use to add the eventhouse to the deployment process?
A deployment pipeline in Fabric is designed to automate the process of deploying assets (such as reports, datasets, eventhouses, and other objects) between environments like Dev, Test, and Prod. Since you need to deploy an eventhouse as part of the deployment process, a deployment pipeline is the appropriate tool to move this asset through the different stages of your environment.
You have a Fabric workspace that contains a lakehouse named Lakehouse1.
In an external data source, you have data files that are 500GB each. A new file is added every day.
You need to ingest the data into Lakehouse1 without applying any transformations. The solution must meet the following requirements
Trigger the process when a new file is added.
Provide the highest throughput.
Which type of item should you use to ingest the data?
To ingest large files (500 GB each) from an external data source into Lakehouse1 with high throughput and to trigger the process when a new file is added, an Eventstream is the best solution.
An Eventstream in Fabric is designed for handling real-time data streams and can efficiently ingest large files as soon as they are added to an external source. It is optimized for high throughput and can be configured to trigger upon detecting new files, allowing for fast and continuous ingestion of data with minimal delay.
You are developing a data engineering solution in Fabric by using Apache Spark.
You need to monitor the performance of Spark workloads the solution must meet the following requirements
* Provide comprehensive information about the performance of the data engineering workloads.
* Identify stages and tasks that run slowly.
* Minimize administrative effort
What should you use?
You have a Fabric workspace that contains a data pipeline named Pipeline! as shown in the exhibit.

You need to recommend a solution for handling old files. The solution must meet the technical requirements. What should you include in the recommendation?
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