You are designing a partition strategy for a fact table in an Azure Synapse Analytics dedicated SQL pool. The table has the following specifications:

• Contain sales data for 20,000 products.

• Use hash distribution on a column named ProduclID,

• Contain 2.4 billion records for the years 20l9 and 2020.

Which number of partition ranges provides optimal compression and performance of the clustered columnstore index?

You have an Azure Databricks workspace and an Azure Data Lake Storage Gen2 account named storage!

New files are uploaded daily to storage1.

• Incrementally process new files as they are upkorage1 as a structured streaming source. The solution must meet the following requirements:

• Minimize implementation and maintenance effort.

• Minimize the cost of processing millions of files.

• Support schema inference and schema drift.

Which should you include in the recommendation?

Note: This question it part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution.

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You have an Azure Data Lake Storage account that contains a staging zone.

You need to design a daily process to ingest incremental data *rom the staging zone, transform the data by executing an R script and then insert the transformed data into a data warehouse in Azure Synapse Analytics.

Solution: You use an Azure Data Factory schedule trigger to execute a pipeline that executes a mapping data flow, and then inserts the data into the data warehouse.

Does this meet the goal?