Ingest and transform data Questions
Practice questions for Ingest and transform data topic in Microsoft Certified: Fabric Data Engineer Associate. 74 questions covering this domain.
A company wants Fabric to reflect Azure Databricks catalog metadata without physically copying source data into OneLake. Which mirroring approach shou...
A platform team wants to write its own application's change data into a mirrored database by using a landing zone URL in OneLake. Which mirroring appr...
What does DeltaFlow add when you use supported CDC connectors in Fabric eventstreams?
Which Fabric item is designed for T-SQL-first development and supports multi-table transactions?
A team creates a shortcut under a subdirectory inside the Tables area of a lakehouse and expects it to be recognized as a table. Why will this design ...
A data movement solution must copy data between two on-premises data sources that use different gateways. What is the documented approach?
A single Copy activity needs to move data from one on-premises source to one on-premises sink. What gateway limitation applies?
A shortcut is created inside a KQL database. Which KQL function should engineers use to query it?
Which workload is explicitly listed as a good fit for an eventhouse?
Analysts want to query data through the lakehouse SQL analytics endpoint. Which data is directly queryable there?
An eventstream already applies transformations, and the team needs the transformed stream to feed multiple downstream targets. Which eventstream const...
A team needs a physical Delta-formatted replica of an Azure SQL Database in OneLake for near real-time analytics and Direct Lake reporting, without bu...
A pipeline uses workspace staging for a long-running copy job. What should the engineer remember?
An engineer routes streaming data from an eventstream to a lakehouse destination. How is the data stored?
What happens if you delete a OneLake shortcut object?
In a lakehouse, where can you create shortcuts at any level of the hierarchy without table-discovery rules?
A data engineering team primarily uses Apache Spark and needs one place for structured and unstructured data. Which Fabric item is the better fit?
Which dataflow Gen2 capability publishes intermediate transformations to a hidden lakehouse to enable reuse and parallelism?
Which Fabric warehouse syntax is supported for relational constraints?
A team needs to incrementally copy data from a relational source to a lakehouse using Fabric data pipelines. Which pattern is documented?
Sign in to see all 74 questions
Create a free account to browse all questions — completely free during our launch phase.