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Microsoft DP-700 Exam Overview:

Certification Vendor:Microsoft
Exam Name:Implementing Data Engineering Solutions Using Microsoft Fabric
Exam Number:DP-700
Real Exam Qty:50–60
Passing Score:700 (scaled score 100–1000)
Exam Price:$165 USD
Related Certifications:Microsoft Certified: Power BI Data Analyst Associate
Microsoft Certified: Azure Data Engineer Associate
Certificate Validity Period:1 year
Exam Format:Multiple select, Multiple choice, Scenario-based, Case studies, Interactive items
Exam Duration:100 minutes
Available Languages:Japanese, Korean, Chinese (Simplified), German, English, Spanish, French, Portuguese (Brazil)
Recommended Training:Microsoft Learn: Implementing Data Engineering Solutions Using Microsoft Fabric
Microsoft Fabric Documentation
Exam Registration:Microsoft Official Exam Registration
Pearson VUE Registration
Sample Questions:Microsoft DP-700 Sample Questions
Exam Way:Online proctored or onsite testing center
Pre Condition:No mandatory prerequisites; recommended experience with data integration, transformation, SQL, PySpark, KQL, and Microsoft Fabric
Official Syllabus URL:https://learn.microsoft.com/en-us/credentials/certifications/resources/study-guides/dp-700

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Microsoft DP-700 Exam Syllabus Topics:

TopicDetails
Topic 1
  • Monitor and optimize an analytics solution: This section of the exam measures the skills of Data Analysts in monitoring various components of analytics solutions in Microsoft Fabric. It focuses on tracking data ingestion, transformation processes, and semantic model refreshes while configuring alerts for error resolution. One skill to be measured is identifying performance bottlenecks in analytics workflows.
Topic 2
  • Ingest and transform data: This section of the exam measures the skills of Data Engineers that cover designing and implementing data loading patterns. It emphasizes preparing data for loading into dimensional models, handling batch and streaming data ingestion, and transforming data using various methods. A skill to be measured is applying appropriate transformation techniques to ensure data quality.
Topic 3
  • Implement and manage an analytics solution: This section of the exam measures the skills of Microsoft Data Analysts regarding configuring various workspace settings in Microsoft Fabric. It focuses on setting up Microsoft Fabric workspaces, including Spark and domain workspace configurations, as well as implementing lifecycle management and version control. One skill to be measured is creating deployment pipelines for analytics solutions.

Microsoft Implementing Data Engineering Solutions Using Microsoft Fabric Sample Questions (Q27-Q32):

NEW QUESTION # 27
You have Fabric workspace that contains eventhouse named Eventhouse1.
In Eventhouse1, you plan to create a table named EventStreamData in a KQL database. The table will contain data based on the following sample.

Answer:

Explanation:

Explanation:


NEW QUESTION # 28
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?

Answer: C

Explanation:
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.


NEW QUESTION # 29
You have a Fabric workspace that contains an eventstream named EventStream1.
You discover that an EventStream1 transformation fails.
You need to find the following error information:
The error details, including the occurrence time
The total number of errors
What should you use? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:


NEW QUESTION # 30
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?

Answer: A

Explanation:
Filter Condition: It correctly filters rows where Neighbourhood is "Sands End" and No_Bikes is greater than or equal to 15.
Sorting: The sorting is explicitly done by No_Bikes in ascending order using sort by No_Bikes asc.
Projection: It projects the required columns (BikepointID, Street, Neighbourhood, No_Bikes, No_Empty_Docks, Timestamp), which minimizes the data returned for consumption.


NEW QUESTION # 31
You have two Fabric notebooks named Load_Salesperson and Load_Orders that read data from Parquet files in a lakehouse. Load_Salesperson writes to a Delta table named dim_salesperson. Load.Orders writes to a Delta table named fact_orders and is dependent on the successful execution of Load_Salesperson.
You need to implement a pattern to dynamically execute Load_Salesperson and Load_Orders in the appropriate order by using a notebook.
How should you complete the code? To answer, drag the appropriate values the correct targets. Each value may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation:


NEW QUESTION # 32
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