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Microsoft DP-700 Exam Overview:
| Certification Vendor: | Microsoft |
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| Exam Name: | Implementing Data Engineering Solutions Using Microsoft Fabric |
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| Exam Number: | DP-700 |
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| Exam Format: | Drag-and-drop, Case studies, Multiple choice, Scenario-based |
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| Passing Score: | 700 / 1000 |
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| Real Exam Qty: | 50 - 60 |
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| Available Languages: | Spanish, English, Simplified Chinese, Portuguese (Brazil), German, Japanese, French |
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| Certificate Validity Period: | 12 Months (Renewable annually) |
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| Related Certifications: | Microsoft Certified: Fabric Data Engineer Associate |
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| Exam Duration: | 100 minutes |
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| Exam Price: | $165 USD |
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| Sample Questions: | Microsoft DP-700 Sample Questions |
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| Exam Way: | Online proctored or Test center |
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| Pre Condition: | No specific prior certification required. |
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| Official Syllabus URL: | https://learn.microsoft.com/en-us/credentials/certifications/exams/dp-700/ |
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| Topic | Details |
|---|
| 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.
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| 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.
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| 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.
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Microsoft Implementing Data Engineering Solutions Using Microsoft Fabric Sample Questions (Q142-Q147):
NEW QUESTION # 142
You need to ensure that usage of the data in the Amazon S3 bucket meets the technical requirements.
What should you do?
- A. Create a shortcut and ensure that caching is disabled for the workspace.
- B. Create a workspace identity and enable high concurrency for the notebooks.
- C. Create a workspace identity and use the identity in a data pipeline.
- D. Create a shortcut and ensure that caching is enabled for the workspace.
Answer: A
Explanation:
To ensure that the usage of the data in the Amazon S3 bucket meets the technical requirements, we must address two key points:
Minimize egress costs associated with cross-cloud data access: Using a shortcut ensures that Fabric does not replicate the data from the S3 bucket into the lakehouse but rather provides direct access to the data in its original location. This minimizes cross-cloud data transfer and avoids additional egress costs.
Prevent saving a copy of the raw data in the lakehouses: Disabling caching ensures that the raw data is not copied or persisted in the Fabric workspace. The data is accessed on-demand directly from the Amazon S3 bucket.
NEW QUESTION # 143
Your company has three newly created data engineering teams named Team1, Team2, and Team3 that plan to use Fabric. The teams have the following personas:
* Team1 consists of members who currently use Microsoft Power BI. The team wants to transform data by using by a low-code approach.
* Team2 consists of members that have a background in Python programming. The team wants to use PySpark code to transform data.
* Team3 consists of members who currently use Azure Data Factory. The team wants to move data between source and sink environments by using the least amount of effort.
You need to recommend tools for the teams based on their current personas.
What should you recommend for each team? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:
Explanation:

Explanation:

NEW QUESTION # 144
You have a Fabric workspace named Workspace1 that contains a notebook named Notebook1.
In Workspace1, you create a new notebook named Notebook2.
You need to ensure that you can attach Notebook2 to the same Apache Spark session as Notebook1.
What should you do?
- A. Enable high concurrency for notebooks.
- B. Increase the number of executors.
- C. Change the runtime version.
- D. Enable dynamic allocation for the Spark pool.
Answer: A
Explanation:
To ensure that Notebook2 can attach to the same Apache Spark session as Notebook1, you need to enable high concurrency for notebooks. High concurrency allows multiple notebooks to share a Spark session, enabling them to run within the same Spark context and thus share resources like cached data, session state, and compute capabilities. This is particularly useful when you need notebooks to run in sequence or together while leveraging shared resources.
NEW QUESTION # 145
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 # 146
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:
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 # 147
......
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