- 137 Actual Exam Questions
- Compatible with all Devices
- Printable Format
- No Download Limits
- 90 Days Free Updates
Get All Implementing Data Engineering Solutions Using Microsoft Fabric Exam Questions with Validated Answers
| Vendor: | Microsoft |
|---|---|
| Exam Code: | DP-700 |
| Exam Name: | Implementing Data Engineering Solutions Using Microsoft Fabric |
| Exam Questions: | 137 |
| Last Updated: | August 25, 2026 |
| Related Certifications: | Fabric Data Engineer Associate |
| Exam Tags: | Data engineering, Data management Intermediate Level Microsoft Data Analysts and Engineers |
Looking for a hassle-free way to pass the Microsoft Implementing Data Engineering Solutions Using Microsoft Fabric exam? DumpsProvider provides the most reliable Dumps Questions and Answers, designed by Microsoft certified experts to help you succeed in record time. Available in both PDF and Online Practice Test formats, our study materials cover every major exam topic, making it possible for you to pass potentially within just one day!
DumpsProvider is a leading provider of high-quality exam dumps, trusted by professionals worldwide. Our Microsoft DP-700 exam questions give you the knowledge and confidence needed to succeed on the first attempt.
Train with our Microsoft DP-700 exam practice tests, which simulate the actual exam environment. This real-test experience helps you get familiar with the format and timing of the exam, ensuring you're 100% prepared for exam day.
Your success is our commitment! That's why DumpsProvider offers a 100% money-back guarantee. If you don’t pass the Microsoft DP-700 exam, we’ll refund your payment within 24 hours no questions asked.
Don’t waste time with unreliable exam prep resources. Get started with DumpsProvider’s Microsoft DP-700 exam dumps today and achieve your certification effortlessly!
You have a Fabric workspace that contains a semantic model named Modell. You need to monitor the refresh history of Model 1 and visualize the refresh history in a chart. What should you use?
You have a Fabric workspace named Workspace1 that is connected to a GitHub repository named repo1. Workspace1 contains the items shown in the following table.

You modify Semantic model 1, Semanticmodel2, and Report2.
You need to commit the changes to repo1.
What is the minimum number of commits you should perform?
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 Fabric eventstream that loads data into a table named Bike_Location in a KQL database. The table contains the following columns:
BikepointID
Street
Neighbourhood
No_Bikes
No_Empty_Docks
Timestamp
You need to apply transformation and filter logic to prepare the data for consumption. The solution must return data for a neighbourhood named Sands End when No_Bikes is at least 15. The results must be ordered by No_Bikes in ascending order.
Solution: You use the following code segment:

Does this meet the goal?
This code does not meet the goal because it uses order by, which is not valid in KQL. The correct term in KQL is sort by.
Correct code should look like:

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 efficiently ingest large data files (500 GB each) into Lakehouse1 with high throughput and trigger the process when a new file is added, a Data pipeline is the most suitable solution. Data pipelines in Fabric are ideal for orchestrating data movement and can be configured to automatically trigger based on file arrivals or other events. This solution meets both requirements: ingesting the data without transformations (since you just need to copy the data) and triggering the process when new files are added.
You need to ensure that usage of the data in the Amazon S3 bucket meets the technical requirements.
What should you do?
To ensure that the usage of the data in the Amazon S3 bucket meets the technical requirements, we must address two key points:
Minimize egress costs associated with cross-cloud data access: Using a shortcut ensures that Fabric does not replicate the data from the S3 bucket into the lakehouse but rather provides direct access to the data in its original location. This minimizes cross-cloud data transfer and avoids additional egress costs.
Prevent saving a copy of the raw data in the lakehouses: Disabling caching ensures that the raw data is not copied or persisted in the Fabric workspace. The data is accessed on-demand directly from the Amazon S3 bucket.
Security & Privacy
Satisfied Customers
Committed Service
Money Back Guranteed