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Microsoft DP-700 Exam Syllabus Topics:

Section	Objectives
Design and implement data ingestion solutions	- Ingest batch and streaming data into Microsoft Fabric 1. Ingest streaming data using Real-Time Analytics 2. Use Pipelines for data movement 3. Use Dataflows Gen2 for ingestion - Integrate external data sources 1. Connect to on-premises and SaaS sources 2. Connect to Azure data services
Develop data transformation solutions	- Transform data using Spark in Fabric 1. PySpark data processing 2. Notebooks in Microsoft Fabric - Data modeling and lakehouse transformation 1. Medallion architecture (Bronze/Silver/Gold) 2. Delta Lake optimization
Secure and govern data in Microsoft Fabric	- Implement data security 1. Workspace and item-level permissions 2. Row-level security and column-level security - Data governance and compliance 1. Data lineage and cataloging 2. Microsoft Purview integration
Manage and monitor data solutions	- Optimize performance and reliability 1. Capacity and workload management in Fabric 2. Spark job optimization - Monitor pipelines and workloads 1. Configure alerts and logging 2. Monitor data pipeline runs

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Microsoft Implementing Data Engineering Solutions Using Microsoft Fabric Sample Questions (Q73-Q78):

NEW QUESTION # 73
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 the join type to kind=outer.
Does this meet the goal?

A. No
B. Yes

Answer: A

Explanation:
An outer join will include unmatched rows from both tables, increasing the dataset size and processing time.
It does not improve query performance.

NEW QUESTION # 74
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 # 75
HOTSPOT
You are processing streaming data from an external data provider.
You have the following code segment.

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:

Litware from New York will be displayed at the top of the result set - Yes The data is sorted first by Location in descending order and then by UnitsSold in descending order. Since " New York " is alphabetically the last Location, it will appear first in the result set. Within " New York " , Litware has the highest UnitsSold (1000), so it will be displayed at the top.
Fabrikam in Seattle will have value = 2 in the Rank column - No
The row_rank_dense function assigns dense ranks based on UnitsSold within each location. In " Seattle " :
Contoso has UnitsSold = 300 # Rank 1
Litware has UnitsSold = 100 # Rank 2
Fabrikam also has UnitsSold = 100, so it shares the same rank (2) as Litware.
Litware in San Francisco will have the same value in the Rank column as Litware in New York - No The rank is calculated separately for each location. In " San Francisco " :
Both Relecloud and Litware have UnitsSold = 500, so they share the same rank (1).
In " New York " , Litware has the highest UnitsSold = 1000 # Rank 1.
Since ranks are calculated independently for each location, Litware in San Francisco does not share the same rank as Litware in New York.

NEW QUESTION # 76
You have an Azure key vault named KeyVaultl that contains secrets.
You have a Fabric workspace named Workspace-!. Workspace! contains a notebook named Notebookl that performs the following tasks:
* Loads stage data to the target tables in a lakehouse
* Triggers the refresh of a semantic model
You plan to add functionality to Notebookl that will use the Fabric API to monitor the semantic model refreshes. You need to retrieve the registered application ID and secret from KeyVaultl to generate the authentication token.
Solution: You use the following code segment:
Use notebookutils.credentials.getSecret and specify the key vault URL and key vault secret. Does this meet the goal?

A. Yes
B. No

Answer: A

NEW QUESTION # 77
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 # 78
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