Which two languages can be used to perform model scoring with the PREDICT function in a Fabric notebook?

Which two languages can be used to perform model scoring with the PREDICT function in a Fabric notebook?

• A. Spark SQL and PySpark
• B. Python and R
• C. SQL and Java
• D. C# and F#

Answer: (A)

Model scoring with PREDICT in a Fabric notebook is accessed through Spark’s two native interfaces: Spark SQL and PySpark. Spark SQL lets you apply a trained model by writing SQL-like queries, which is great for straightforward, table-based scoring within your data flows. PySpark lets you use Python to run the same PREDICT function in a programmatic, code-driven way, enabling more complex processing and integration with Python libraries. Both run inside the same Spark session, so you can mix declarative SQL and Python code in the same notebook session. The other language pairings don’t align with how PREDICT is exposed in this environment, which is why Spark SQL and PySpark are the correct combination.

Model scoring with PREDICT in a Fabric notebook is accessed through Spark’s two native interfaces: Spark SQL and PySpark. Spark SQL lets you apply a trained model by writing SQL-like queries, which is great for straightforward, table-based scoring within your data flows. PySpark lets you use Python to run the same PREDICT function in a programmatic, code-driven way, enabling more complex processing and integration with Python libraries. Both run inside the same Spark session, so you can mix declarative SQL and Python code in the same notebook session. The other language pairings don’t align with how PREDICT is exposed in this environment, which is why Spark SQL and PySpark are the correct combination.