You have a Fabric tenant that contains a machine learning model registered in a Fabric workspace. You need to use the model to generate predictions by using the PREDICT function in a Fabric notebook.

The correct answer is C. Spark SQL D. PySpark. In Microsoft Fabric notebooks, ML model scoring via the PREDICT function is supported through SynapseML and MLflow integrations in two languages: Spark SQL (C), which allows SQL-style syntax such as SELECT PREDICT(model, *) FROM table, and PySpark (D), which allows Python DataFra

Submitted by joshua94· Apr 18, 2026Prepare and serve data

Question

You have a Fabric tenant that contains a machine learning model registered in a Fabric workspace. You need to use the model to generate predictions by using the PREDICT function in a Fabric notebook. Which two languages can you use to perform model scoring? Each correct answer presents a complete solution. NOTE: Each correct answer is worth one point.

Options

AT-SQL
BDAX
CSpark SQL
DPySpark

How the community answered

(37 responses)

A
19% (7)
B
11% (4)
C
70% (26)

Explanation

In Microsoft Fabric notebooks, ML model scoring via the PREDICT function is supported through SynapseML and MLflow integrations in two languages: Spark SQL (C), which allows SQL-style syntax such as SELECT PREDICT(model, *) FROM table, and PySpark (D), which allows Python DataFrame-based invocation of the model. Both run on the Spark engine and can reference models registered in the Fabric workspace. T-SQL (A) is used in Fabric warehouses and does not support the notebook-based PREDICT function. DAX (B) is a query/calculation language for semantic models and has no ML prediction capability.

Topics

#Fabric Notebooks#Machine Learning#Model Scoring#PREDICT function

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