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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
Certificate Validity Period:1 year (renewable)
Passing Score:700/1000
Real Exam Qty:40-60
Available Languages:Japanese, Spanish, German, French, English, Portuguese (Brazil), Korean, Simplified Chinese
Exam Format:Case studies, Scenario-based questions, Multiple choice, Multiple response
Exam Duration:100-120
Exam Price:Varies by region (approx. $165 USD)
Related Certifications:Microsoft Certified: Fabric Analytics Engineer Associate
Microsoft Certified: Azure Data Engineer Associate
Recommended Training:Microsoft Fabric Documentation
Microsoft Learn DP-700 Learning Path
Exam Registration:Microsoft Learn Certification Exam Page
Pearson VUE Microsoft Exams
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
  • 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 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.
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 (Q59-Q64):

NEW QUESTION # 59
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 move the filter to line 02.
Does this meet the goal?

Answer: A

Explanation:
Moving the filter to line 02: Filtering the Stream table before performing the join operation reduces the number of rows that need to be processed during the join. This is an effective optimization technique for queries involving large datasets.


NEW QUESTION # 60
HOTSPOT
You have a Fabric workspace named Workspace1_DEV that contains the following items:
10 reports
Four notebooks
Three lakehouses
Two data pipelines
Two Dataflow Gen1 dataflows
Three Dataflow Gen2 dataflows
Five semantic models that each has a scheduled refresh policy
You create a deployment pipeline named Pipeline1 to move items from Workspace1_DEV to a new workspace named Workspace1_TEST.
You deploy all the items from Workspace1_DEV to Workspace1_TEST.
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:


NEW QUESTION # 61
You have a Fabric workspace named Workspace1 that contains the items shown in the following table.

For Model1, the Keep your Direct Lake data up to date option is disabled.
You need to configure the execution of the items to meet the following requirements:
Notebook1 must execute every weekday at 8:00 AM.
Notebook2 must execute when a file is saved to an Azure Blob Storage container.
Model1 must refresh when Notebook1 has executed successfully.
How should you orchestrate each item? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation:


NEW QUESTION # 62
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?

Answer: A


NEW QUESTION # 63
You have a Fabric workspace that contains two lakehouses named Lakehouse1 and Lakehouse2. Lakehouse1 contains staging data in a Delta table named Orderlines. Lakehouse2 contains a Type 2 slowly changing dimension (SCD) dimension table named Dim_Customer.
You need to build a query that will combine data from Orderlines and Dim_Customer to create a new fact table named Fact_Orders. The new table must meet the following requirements:
Enable the analysis of customer orders based on historical attributes.
Enable the analysis of customer orders based on the current attributes.
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:


NEW QUESTION # 64
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