Microsoft Fabric Data Engineer Associate DP-700 Practice Question
You are designing a batch ingestion solution in Microsoft Fabric for an IoT manufacturer. The sensors generate about 150 GB of semi-structured JSON files each day, and the company expects the data volume to reach multiple petabytes within a few years. Data engineers will use PySpark notebooks to clean and denormalize the data before it is loaded into a dimensional model for Power BI. Management requires the most cost-effective storage that still provides ACID transactions and supports schema evolution. Which Fabric data store should you recommend as the landing zone for the incoming files?
Create a Lakehouse and land the files in its unmanaged Delta tables.
Provision a Fabric Warehouse and load the files directly into relational tables.
Ingest the files into a Real-Time Analytics KQL database.
Use Mirroring to replicate the data from an external Azure SQL database into Fabric.
A Fabric lakehouse stores data in the open Delta/Parquet format on OneLake, giving petabyte-scale, low-cost object storage while still supporting ACID transactions and schema enforcement/evolution through the Delta Lake protocol. It integrates natively with Spark notebooks for transformation and can later feed a dimensional model or semantic model. A Fabric warehouse is optimized for structured, relational data and is typically more expensive for raw petabyte-scale storage. A KQL database targets high-volume streaming and log analytics but is not optimized for large-scale batch storage or Spark transformations. Mirroring replicates an existing relational source; it cannot serve as the initial landing zone for raw IoT files.
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What is the Delta Lake protocol and why is it critical for this solution?
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Why is a Fabric Lakehouse more cost-effective compared to other options for large-scale storage?
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What is the role of PySpark notebooks in this solution, and why are they suitable for this use case?
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Microsoft Fabric Data Engineer Associate DP-700
Ingest and transform data
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