Exam DP-700 topic 1 question 2 discussion

KQL is avaialble in eventhouse only https://learn.microsoft.com/en-us/fabric/fundamentals/decision-guide-data-store

Eventhouse: Read operations: KQL, Spark and T-SQL Write operations: KQL, Spark https://learn.microsoft.com/en-us/fabric/get-started/decision-guide-data-store

I think eventhouse should be correct answer because it can support KQL, Spark and T-SQL. And can support semi-structured data event not good like lakehouse. The problem if we keep semi-structured data in lakehouse, we still need eventhouse to execute KQL on them. However, we just can build shortcut in KQL database to point delta tables in lakehouse(by OneLake), we cannot point to semi-structured files.

A Lakehouse in Microsoft Fabric is the best choice for storing semi-structured data while allowing access using T-SQL, KQL, and Apache Spark

Check this link and choose an answer, it must be B. https://learn.microsoft.com/en-us/fabric/fundamentals/decision-guide-data-store

Based on documentation read on eventhouse allows SQL,T-SQL and Spark. https://learn.microsoft.com/en-us/fabric/fundamentals/decision-guide-data-store

https://learn.microsoft.com/en-us/fabric/fundamentals/decision-guide-data-store

By default you cannot query a lakehouse with kql but if you integrate it with Real time analytics you are able to query the lakehouse with kql

Eventhouse: Read operations: KQL, Spark and T-SQL Write operations: KQL, Spark

Eventhouse: Read operations: KQL, Spark and T-SQL Write operations: KQL, Spark

Out of the available options, only eventhouse can be read using KQL

lakehouse permit to read Spark data We are not in KQL

Like 38578c4 link says: Eventhouse support read with: KQL, T-SQL, Spark & write with: KQL, Spark, connector ecosystem

A Lakehouse in Microsoft Fabric is the best choice for storing semi-structured data while allowing access using T-SQL, KQL, and Apache Spark B. Eventhouse - Designed for real-time event processing. - Does not support T-SQL or Apache Spark writes. C. Datamart - Built for self-service BI, supports T-SQL, but not Spark-based writing. - Not optimized for semi-structured data. D. Warehouse - Optimized for structured relational data (fact & dimension tables). - Does not support KQL or Spark-based writes.

Lakehouse: data will only be written bij using Spark. You can use shortcuts to access the data from een Eventhouse for KQL queries. If real-time processing or KQL were requitements, Eventhouse would be more appropiate.

You are deal with semi-structured data and we would like to use only spark.

A lakehouse in Microsoft Fabric is a versatile storage option designed for semi-structured and unstructured data, combining the flexibility of a data lake with the structure of a data warehouse. It supports Apache Spark, T-SQL, and KQL queries, making it ideal for the requirements. Why the other options are not suitable: B. Eventhouse: Eventhouse is not a valid Microsoft Fabric storage option. C. Datamart: A datamart is designed for structured data and primarily supports T-SQL, but not Spark or KQL. D. Warehouse: A warehouse is optimized for structured data and supports T-SQL, but it is not designed for semi-structured data or Spark. A lakehouse is the best choice for storing data in a format that allows Spark to write it while enabling T-SQL and KQL for querying.