A batch process needs to store structured tabular data that will be queried with T-SQL and support transactional updates. Which data store is most appropriate?

Azure Synapse Data Warehouse (dedicated)

A company receives nightly CSV exports from a legacy on-premises system. The data is used by analysts who query it with T-SQL in Fabric, and the data must be available in OneLake for downstream notebooks. The ingestion must be automated and the source files must be moved to an archive location after a successful load. What should you do?

Create a Dataflow Gen2 to load the CSVs into a Lakehouse table, and then manually delete the source files after the refresh completes.

Use a notebook with PySpark to read the CSVs and write to the Lakehouse, then schedule the notebook to run nightly.

Create a Data Pipeline with a Copy activity to load the CSVs into a Lakehouse table, then add a Delete activity to remove the source files.

Create a OneLake shortcut from the on-premises folder to the Lakehouse, so the CSVs are read directly without copying.

Which of the following is a common strategy for handling missing data in a batch dataset?

Duplicating rows with missing values

Imputing missing values with a default or calculated value

Converting missing values to zero

Ignoring missing values entirely

What is the primary purpose of a data pipeline in Microsoft Fabric for batch data ingestion?

To orchestrate and automate the movement of data from sources to destinations

To visualize data in dashboards

To store data in a relational database

A company uses Azure SQL Database as the source for a Fabric warehouse. They want to replicate the data into OneLake continuously with minimal latency and without writing custom code. The replicated data must be available for querying in the warehouse. What should you configure?

A mirroring configuration from Azure SQL Database to Fabric

A Dataflow Gen2 with a scheduled refresh

A notebook that runs every 30 minutes to copy data

A Data Pipeline with a Copy activity scheduled to run every hour

Microsoft Certified:Fabric Data Engineer Associate

Domain 2Objective 2

Ingest and Transform Batch Data DP-700 Practice Questions (Page 9)

Part of the Ingest and transform data 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 4–8 from this objective — we provide 48 practice questions to prepare you well beyond it. (estimate)

48questions here

10free pages

13concepts

30–35%of the exam

Questions 41–45

41foundation · easy
A batch process needs to store structured tabular data that will be queried with T-SQL and support transactional updates. Which data store is most appropriate?
Select an answer first
42application · medium
A company receives nightly CSV exports from a legacy on-premises system. The data is used by analysts who query it with T-SQL in Fabric, and the data must be available in OneLake for downstream notebooks. The ingestion must be automated and the source files must be moved to an archive location after a successful load. What should you do?
Select an answer first
43foundation · easy
Which of the following is a common strategy for handling missing data in a batch dataset?
Select an answer first
44foundation · easy
What is the primary purpose of a data pipeline in Microsoft Fabric for batch data ingestion?
Select an answer first
45application · medium
A company uses Azure SQL Database as the source for a Fabric warehouse. They want to replicate the data into OneLake continuously with minimal latency and without writing custom code. The replicated data must be available for querying in the warehouse. What should you configure?
Select an answer first

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