You have source data in a folder on a local computer.
You need to create a solution that will use Fabric to populate a data store. The solution must meet the following requirements:
Support the use of dataflows to load and append data to the data store.
Ensure that Delta tables are V-Order optimized and compacted automatically.
Which type of data store should you use?
A lakehouse is the correct choice for satisfying the requirements of supporting dataflows to load and append data to the data store, as well as ensuring that Delta tables are V-Order optimized and compacted automatically. Lakehouses are designed to handle both structured and unstructured data, enabling the creation and management of Delta tables that can be optimized for performance and automatically compacted. This makes a lakehouse an optimal solution for comprehensive data analytics and storage needs, aligning with the specified requirements.
A - The only logical answer here. B is an Azure SQL database and is an Azure product but doesn't have V-Order. C is a just a generic warehouse and once again, doesn't necessarily contain any V-Order feature. D is a database that uses KQL and is irrelevant of the question.
https://learn.microsoft.com/en-us/fabric/data-warehouse/ingest-data#best-practices The Note says "Regardless of how you ingest data into warehouses, the parquet files produced by the data ingestion task will be optimized using V-Order write optimization... Unlike in Fabric Data Engineering, V-Order is a global setting in Synapse Data Warehouse that cannot be disabled." Therefore the answer is warehouse
The answer is correct - C Warehouse The key to this question is "Ensure that Delta tables are V-Order optimized". V-Order optimization isn't guaranteed in Lakehouse, and there are times when you need to run OPTIMIZE to ensure the tables are V-Order Optimized. This link here shows the answer: https://learn.microsoft.com/en-us/fabric/data-warehouse/ingest-data#best-practices The Note says "Regardless of how you ingest data into warehouses, the parquet files produced by the data ingestion task will be optimized using V-Order write optimization... Unlike in Fabric Data Engineering, V-Order is a global setting in Synapse Data Warehouse that cannot be disabled."
Delta table... = Lakehouse
To meet the requirements of supporting dataflows to load and append data to the data store while ensuring that Delta tables are V-Order optimized and compacted automatically, you should use a lakehouse in Fabric as your solution.
I have recently taken the exam and this question was asked.Its a multiple choice question.we need to select 2 options.so as per the comments both lakehouse and warehouse are support delta tables and v-order optimization.Its A,C
Lakehouses can automatically handle the optimization and compaction of Delta tables, including V-Order optimization, which arranges data in an optimal order to improve query performance. Why Not the Other Options? Azure SQL Database (Option B):Azure SQL Database is a relational database service that does not natively support Delta tables or V-Order optimization. It is more suited for traditional OLTP workloads. Warehouse (Option C):While warehouses are excellent for structured data and support dataflows, they may not provide the same level of native support for Delta tables and automatic V-Order optimization as a lakehouse. KQL Database (Option D):KQL (Kusto Query Language) databases are optimized for log and telemetry data, primarily used with Azure Data Explorer. They are not designed to support Delta tables or the specific optimizations required for large-scale transactional data processing.
lakehouse doesn't automaticly store data in delta tables while warehouse does.
The whole new platform is focused on the lakehouse and optimization for it, so answer A.
Both A & C are correct
C - Warehouse as for warehouse the optimized V-order is automatically enabled but for Lakehouse is setting that you need to enable or disable.
IMHO, "A" because: The Lakehouse and the Delta Lake table format are central to Microsoft Fabric, assuring that tables are optimized for analytics is a key requirement. ... in https://learn.microsoft.com/en-us/fabric/data-engineering/delta-optimization-and-v-order?tabs=sparksql