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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: Fabric Analytics Engineer Associate
Exam Price:Varies by region (approx. $165 USD)
Certificate Validity Period:1 year (renewable)
Available Languages:Simplified Chinese, French, English, Spanish, Portuguese (Brazil), German, Japanese, Korean
Exam Format:Multiple choice, Case studies, Multiple response, Scenario-based questions
Exam Duration:100-120
Passing Score:700/1000
Real Exam Qty:40-60
Recommended Training:Microsoft Learn DP-700 Learning Path
Microsoft Fabric Documentation
Exam Registration:Pearson VUE Microsoft Exams
Microsoft Learn Certification Exam Page
Sample Questions:Microsoft DP-700 Sample Questions
Exam Way:Online proctored via Pearson VUE or in-person at authorized testing centers
Pre Condition:No formal prerequisites, but recommended experience with Azure data services and basic data engineering concepts
Official Syllabus URL:https://learn.microsoft.com/credentials/certifications/exams/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
  • 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.
Topic 3
  • 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.

Microsoft Implementing Data Engineering Solutions Using Microsoft Fabric Sample Questions (Q30-Q35):

NEW QUESTION # 30
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.

Answer:

Explanation:

Explanation:


NEW QUESTION # 31
HOTSPOT
You have a Fabric workspace that contains a warehouse named DW1. DW1 contains the following tables and columns.

You need to create an output that presents the summarized values of all the order quantities by year and product. The results must include a summary of the order quantities at the year level for all the products.
How should you complete the code? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation:


NEW QUESTION # 32
You have three users named User1, User2, and User3.
You have the Fabric workspaces shown in the following table.

You have a security group named Group1 that contains User1 and User3.
The Fabric admin creates the domains shown in the following table.

User1 creates a new workspace named Workspace3.
You add Group1 to the default domain of Domain1.
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation:


NEW QUESTION # 33
You have a Fabric workspace named Workspace1 that contains a warehouse named Warehouse2. A team of data analysts has Viewer role access to Workspace1. You create a table by running the following statement.

You need to ensure that the team can view only the first two characters and the last four characters of the Creditcard attribute.
How should you complete the statement? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation:


NEW QUESTION # 34
You have a Fabric workspace that contains a lakehouse named Lakehouse1. Data is ingested into Lakehouse1 as one flat table. The table contains the following columns.

You plan to load the data into a dimensional model and implement a star schema. From the original flat table, you create two tables named FactSales and DimProduct. You will track changes in DimProduct.
You need to prepare the data.
Which three columns should you include in the DimProduct table? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.

Answer: A,D,E

Explanation:
In a star schema, the DimProduct table serves as a dimension table that contains descriptive attributes about products. It will provide context for the FactSales table, which contains transactional data. The following columns should be included in the DimProduct table:
ProductName: The ProductName is an important descriptive attribute of the product, which is needed for analysis and reporting in a dimensional model.
ProductColor: ProductColor is another descriptive attribute of the product. In a star schema, it makes sense to include attributes like color in the dimension table to help categorize products in the analysis.
ProductID: ProductID is the primary key for the DimProduct table, which will be used to join the FactSales table to the product dimension. It's essential for uniquely identifying each product in the model.


NEW QUESTION # 35
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