A manufacturing company has a large on-premises SQL Server database (about 2 TB) that they need to ingest into Fabric for analytics. The network connection is reliable but not high-bandwidth. They need to perform this ingestion once, and then refresh daily with incremental updates. What is the most appropriate ingestion method?

Use a Shortcut to the on-premises SQL Server database, allowing Fabric to query it directly.

Use a Dataflow (Gen2) with a SQL Server connector to copy the entire table daily.

Use a Data Pipeline with a Copy activity to perform an initial full load, then use incremental loads based on a watermark column.

Use a Notebook with Spark to read the entire table and write to a Lakehouse, then schedule it daily.

A company needs to ingest a high-volume stream of clickstream data and also perform complex transformations before loading into a lakehouse. The transformations are stateful and require custom logic. The team wants to minimize latency and operational overhead. What should they use?

Use an Eventhouse to ingest the stream and perform transformations in KQL.

Use a Spark Structured Streaming job in a notebook to transform and write to a lakehouse.

Use an eventstream with custom transformation logic and route the output to a lakehouse.

Use a Dataflow Gen2 to ingest and transform the data in batches.

Contoso's marketing team needs to analyze data from a third-party SaaS application that exposes a REST API with OAuth 2.0 authentication. The data volume is moderate (a few thousand records per day), and the team wants to refresh the dataset hourly in Fabric. What should you use to ingest this data?

Use a Notebook with Python and the requests library to call the API and write the results to a Lakehouse table, then schedule the notebook to run hourly.

Create a shortcut in OneLake to the REST API endpoint, pointing directly to the SaaS application's data.

Create a Dataflow (Gen2) with a Power Query connector for the REST API, using OAuth 2.0 credentials stored in the connection.

Use a Dataflow (Gen2) with a Power Query connector for the REST API, and configure a scheduled refresh to run hourly.

What is the primary benefit of integrating OneLake with an Eventhouse in Microsoft Fabric?

It provides a backup of the Eventhouse data

It allows Eventhouse data to be stored in a format that can be queried by other Fabric engines

It automatically creates semantic models for the data

It eliminates the need for data transformation

A data analyst needs to find a dataset that was shared with them, but they are not sure if it is in a lakehouse, a warehouse, or an external ADLS location. They have access to the OneLake catalog. What should they do?

Check each workspace manually for the dataset.

Use the Real-Time hub to search for the dataset.

Search the OneLake catalog for the dataset by name or metadata.

Ask the data owner to provide the direct link.

Microsoft Certified:Fabric Analytics Engineer Associate

Domain 2Objective 1

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Questions 1–5

1application · medium
A manufacturing company has a large on-premises SQL Server database (about 2 TB) that they need to ingest into Fabric for analytics. The network connection is reliable but not high-bandwidth. They need to perform this ingestion once, and then refresh daily with incremental updates. What is the most appropriate ingestion method?
Select an answer first
2expert · hard
A company needs to ingest a high-volume stream of clickstream data and also perform complex transformations before loading into a lakehouse. The transformations are stateful and require custom logic. The team wants to minimize latency and operational overhead. What should they use?
Select an answer first
3application · medium
Contoso's marketing team needs to analyze data from a third-party SaaS application that exposes a REST API with OAuth 2.0 authentication. The data volume is moderate (a few thousand records per day), and the team wants to refresh the dataset hourly in Fabric. What should you use to ingest this data?
Select an answer first
4foundation · easy
What is the primary benefit of integrating OneLake with an Eventhouse in Microsoft Fabric?
Select an answer first
5expert · hard
A data analyst needs to find a dataset that was shared with them, but they are not sure if it is in a lakehouse, a warehouse, or an external ADLS location. They have access to the OneLake catalog. What should they do?
Select an answer first

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Questions 1–5

A company needs to ingest a high-volume stream of clickstream data and also perform complex transformations before loading into a lakehouse. The transformations are stateful and require custom logic. The team wants to minimize latency and operational overhead. What should they use?

What is the primary benefit of integrating OneLake with an Eventhouse in Microsoft Fabric?

A data analyst needs to find a dataset that was shared with them, but they are not sure if it is in a lakehouse, a warehouse, or an external ADLS location. They have access to the OneLake catalog. What should they do?

Keep studying — Prepare data