How can parameters be used in a notebook for orchestration?

To schedule the notebook to run at a specific time

To define the notebook's visual appearance

To store the notebook's output data permanently

To pass values into the notebook when it is run by a pipeline

A data engineer has a notebook that processes daily sales data. The notebook currently has hardcoded date values. The engineer wants to reuse the same notebook for different date ranges without editing the code each time. What is the best approach?

Create a new notebook for each date range.

Use notebook parameters and pass values from a pipeline activity.

Use a Dataflow Gen 2 to pass parameters to the notebook.

Hardcode the dates in the notebook and change them manually.

A data engineer has a Fabric notebook that processes sales data. The notebook currently hardcodes the year and month in the file path. The engineer wants to reuse the same notebook for multiple reporting periods by passing the year and month from a pipeline. What should the engineer do?

Use a Dataflow Gen 2 to pass the year and month to the notebook

Hardcode the year and month in the pipeline activity and run the notebook unchanged

Modify the notebook to read the year and month from notebook parameters and update the pipeline to pass values

Create a separate notebook for each year and month combination

A data engineering team needs to perform a complex transformation that requires custom Python libraries and iterative logic. The source data is in a lakehouse, and the output should be written back to the same lakehouse. The team wants to minimize the number of artifacts. What should they use?

A Fabric notebook with Python code

A pipeline with a Notebook activity that calls a Dataflow Gen 2

A pipeline with a Copy activity and a Dataflow Gen 2

A Dataflow Gen 2 with custom Python script

What is the purpose of dynamic expressions in a pipeline?

To replace the need for pipeline parameters

To provide a visual interface for building expressions

To compute values at runtime based on context, such as the current time or pipeline parameters

To define the pipeline's static configuration

Microsoft Certified:Fabric Data Engineer Associate

Domain 1Objective 4

Orchestrate Processes DP-700 Practice Questions (Page 6)

Part of the Implement and manage an analytics solution domain, which accounts for 30–35% of the DP-700 exam. Microsoft does not publish an official question count, but from its 100-minute exam (~40–65 total, ~12–23 in this domain), expect 3–6 from this objective — we provide 39 practice questions to prepare you well beyond it. (estimate)

39questions here

8free pages

9concepts

30–35%of the exam

Questions 26–30

26foundation · easy
How can parameters be used in a notebook for orchestration?
Select an answer first
27application · medium
A data engineer has a notebook that processes daily sales data. The notebook currently has hardcoded date values. The engineer wants to reuse the same notebook for different date ranges without editing the code each time. What is the best approach?
Select an answer first
28application · medium
A data engineer has a Fabric notebook that processes sales data. The notebook currently hardcodes the year and month in the file path. The engineer wants to reuse the same notebook for multiple reporting periods by passing the year and month from a pipeline. What should the engineer do?
Select an answer first
29application · medium
A data engineering team needs to perform a complex transformation that requires custom Python libraries and iterative logic. The source data is in a lakehouse, and the output should be written back to the same lakehouse. The team wants to minimize the number of artifacts. What should they use?
Select an answer first
30foundation · easy
What is the purpose of dynamic expressions in a pipeline?
Select an answer first

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