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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
Exam Price:$165 USD
Certificate Validity Period:1 year
Available Languages:Korean, German, Spanish, Chinese (Simplified), English, Japanese, Portuguese (Brazil), French
Related Certifications:Microsoft Certified: Power BI Data Analyst Associate
Microsoft Certified: Azure Data Engineer Associate
Exam Duration:100 minutes
Real Exam Qty:50–60
Passing Score:700 (scaled score 100–1000)
Exam Format:Multiple choice, Scenario-based, Multiple select, Case studies, Interactive items
Recommended Training:Microsoft Fabric Documentation
Microsoft Learn: Implementing Data Engineering Solutions Using Microsoft Fabric
Exam Registration:Pearson VUE Registration
Microsoft Official Exam 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
  • 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 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
  • 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.

Microsoft Implementing Data Engineering Solutions Using Microsoft Fabric Sample Questions (Q74-Q79):

NEW QUESTION # 74
You have a Fabric notebook named Notebook1 that has been executing successfully for the last week.
During the last run, Notebook1executed nine jobs.
You need to view the jobs in a timeline chart.
What should you use?

Answer: A

Explanation:
The run series from the details of the application run is the most detailed and relevant feature for visualizing job execution in a timeline format, making it the correct choice for this scenario. It provides an intuitive way to analyze job execution patterns and improve the efficiency of the notebook.


NEW QUESTION # 75
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?

Answer: A

Explanation:
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.


NEW QUESTION # 76
You have a Fabric workspace that contains 10 data pipelines named Pipeline1 through Pipeline10. The data pipelines load, transform, and publish data to a lakehouse named Lakehouse1.
Each data pipeline takes 30 minutes to run. The data pipelines must run in numerical sequence based on their names. The end-to-end process must start every eight hours.
You need to configure orchestration to ensure that all the data pipelines run in the correct order. The solution must minimize the number of schedules and administrative effort.
What should you do?

Answer: B


NEW QUESTION # 77
You are building a Fabric notebook named MasterNotebookl in a workspace. MasterNotebookl contains the following code.

You need to ensure that the notebooks are executed in the following sequence:
1. Notebook_03
2. Notebook.Ol
3. Notebook_02
Which two actions should you perform? Each correct answer presents part of the solution. NOTE: Each correct selection is worth one point.

Answer: A,E


NEW QUESTION # 78
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: B

Explanation:
This code does not meet the goal because this is an SQL-like query and cannot be executed in KQL, which is required for the database.
Correct code should look like:


NEW QUESTION # 79
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