In a Fabric notebook, which syntax should you use to access the Research division data for Productline1?

In a Fabric notebook, which syntax should you use to access the Research division data for Productline1?

• A. spark.sql("SELECT * FROM Lakehouse1.ResearchProduct ")
• B. SELECT * FROM Lakehouse1.ResearchProduct
• C. spark.read.table("Lakehouse1.ResearchProduct")
• D. spark.sql("SELECT * FROM Lakehouse1.ResearchProduct ")

Answer: (A)

In a Fabric notebook, querying Lakehouse data most reliably uses Spark SQL through the Spark session. Using a SQL string to select from the fully qualified table Lakehouse1.ResearchProduct runs a SQL query and returns the results as a DataFrame, which is the straightforward way to access and inspect the data in one step. The trailing space inside the query doesn’t affect its execution—Spark SQL treats it as whitespace. This approach is clear and directly expresses the intention to retrieve all rows from that specific table. An alternative method would be to load the table with the DataFrame API (spark.read.table), which also gives you a DataFrame but is not executing a SQL SELECT query.

In a Fabric notebook, querying Lakehouse data most reliably uses Spark SQL through the Spark session. Using a SQL string to select from the fully qualified table Lakehouse1.ResearchProduct runs a SQL query and returns the results as a DataFrame, which is the straightforward way to access and inspect the data in one step. The trailing space inside the query doesn’t affect its execution—Spark SQL treats it as whitespace. This approach is clear and directly expresses the intention to retrieve all rows from that specific table. An alternative method would be to load the table with the DataFrame API (spark.read.table), which also gives you a DataFrame but is not executing a SQL SELECT query.