Question 1

Which of the following are key strategies to improve collaboration between analytics engineers and architects in Microsoft Fabric?

Options :

A : Sharing performance metrics and analytics dashboards regularly

B : Limiting access to data models to only one team

C : Avoiding documentation of system changes and updates

D : Using a collaborative platform for code reviews and feedback

Answer: D

Question 2

What is a typical use case for PySpark in Microsoft Fabric data transformation?

Options :

A : Running SQL queries for data analysis

B : Automatically cleaning data without user intervention

C : Transforming large-scale datasets that exceed the memory of a single machine

D : Analyzing structured data only

Answer: C

Question 3

Which tool in Microsoft Fabric is commonly used for orchestrating and automating data workflows?

Options :

A : Power BI

B : Azure Data Factory (ADF)

C : Azure Purview

D : Azure Synapse Analytics

Answer: B

Question 4

Which of the following is an advantage of using stream processing in data ingestion within Microsoft Fabric?

Options :

A : Stream processing is a low-latency solution for data processing.

B : Stream processing allows for real-time data ingestion and analysis.

C : Stream processing is best for large-scale batch operations.

D : Stream processing can store data without needing to process it.

Answer: A,B

Question 5

You have a Fabric workspace that contains a data pipeline named Pipeline! as shown in the exhibit.

(Click the Exhibit tab.) What will occur the next time Pipelinel tuns?

Options :

A : Both activities will run simultaneously.

B : Both activities will be skipped.

C : Execute procedurel will run and Copy_kdi will be skipped.

D : Copy.kdi will run and Execute procedurel will be skipped.

E : Execute procedure1 will run first, and then Copy_kdi will run.

F : Copy.kdi will run first, and then Execute procedurel will run.

Answer: A