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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
Related Certifications:	Microsoft Certified: Azure Data Engineer Associate Microsoft Certified: Power BI Data Analyst Associate
Available Languages:	Chinese (Simplified), English, Japanese, Portuguese (Brazil), French, Korean, German, Spanish
Exam Format:	Scenario-based, Interactive items, Multiple select, Multiple choice, Case studies
Exam Price:	$165 USD
Passing Score:	700 (scaled score 100–1000)
Real Exam Qty:	50–60
Certificate Validity Period:	1 year
Exam Duration:	100 minutes
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 Prüfungsplan:

Thema	Einzelheiten
Thema 1	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.
Thema 2	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.
Thema 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 DP-700 Prüfungsfragen mit Lösungen (Q107-Q112):

107. Frage
You have a Fabric tenant that is linked to a Microsoft Entra tenant. The Microsoft [intra tenant contains a user named User1 and two groups named Group! and Groups.
The Fabric tenant contains the workspaces shown in the following table.

You need to meet the following access requirements:
* User1 must have read access to the following:
* The operations department reports
* The HR department warehouse and reports
* The legal department warehouse and reports
* User1 must be able to create new items in Workspace
* Group1 must have access to the finance department warehouse and reports.
* Group1 must be able to add new users to the finance department workspace.
* Group2 must have access to only the HR warehouse and the legal warehouse.
The solution must follow the principle of least privilege.
What should you do? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Antwort:

Begründung:

Explanation:

108. Frage
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?

A. a datamart
B. an eventhouse
C. a warehouse
D. a lakehouse

Antwort: D

Begründung:
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.

109. Frage
You have a Fabric warehouse named DW1 that contains a Type 2 slowly changing dimension (SCD) dimension table named DimCustomer. DimCustomer contains 100 columns and 20 million rows. The columns are of various data types, including int, varchar, date, and varbinary.
You need to identify incoming changes to the table and update the records when there is a change. The solution must minimize resource consumption.
What should you use to identify changes to attributes?

A. a hash function to compare the attributes in the source table.
B. a direct attributes comparison for the attributes in the source table.
C. a hash function to compare the attributes in the DimCustomer table.
D. a direct attributes comparison across the attributes in the DimCustomer table.

Antwort: A

110. Frage
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 change project to extend.
Does this meet the goal?

A. Yes
B. No

Antwort: B

Begründung:
Using extend retains all columns in the table, potentially increasing the size of the output unnecessarily.
project is more efficient because it selects only the required columns.

111. Frage
You have a Fabric workspace named Workspace! that contains an Azure Data Factory pipeline named Pipeline1 You need to monitor the execution and activity status of Pipeline1 and automatically email an alert if the pipeline fails. The solution must minimize development and administrative effort.
What should you use to monitor Pipeline1, and what should you use to email the alert? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Antwort:

Begründung:

Explanation:

112. Frage
......

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