Model Scoring Languages in Fabric Notebook
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.
Community Votes
100% of anonymous learners picked answer CD. Votes are pick records left by other test-takers — they are not the verified answer.
Community Insight
This question tests knowledge of supported runtime environments within Fabric Notebooks for machine learning tasks, specifically distinguishing between T-SQL/DAX and Spark-based languages.
In Microsoft Fabric, you can use PySpark and Spark SQL to generate predictions using the PREDICT function. These languages support the necessary DataFrame and Transformer APIs for model scoring.
Candidates often select T-SQL because it supports a PREDICT clause in Azure SQL Database, but this is not available or relevant for the batch scoring context described in Fabric Notebooks using MLFlow/Spark pools.
Community Discussion (16 comments)
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Expert Analysis
Why the Answer Is Correct
The correct answers are C (Spark SQL) and D (PySpark). In Microsoft Fabric, machine learning workloads are executed within the OneLake/Spark engine. To perform batch scoring using thePREDICT function, you must interact with the data via Spark DataFrames. This can be done using PySpark (Python API), Spark SQL (SQL syntax on DataFrames), or through SynapseML Transformers. Both PySpark and Spark SQL provide the necessary interface to load datasets, apply ML models (often via MLFlow), and generate predictions.Why the Other Options Are Wrong
T-SQL (Option A) is used for relational database operations in Azure SQL or Synapse Dedicated SQL Pools. While T-SQL has aPREDICT statement for server-side scoring, it operates on tables/views within the SQL engine, not within a Fabric Notebook's Spark execution context. DAX (Option B) is a formula language used primarily in Power BI and Analysis Services for data analysis and visualization, not for executing machine learning inference code in notebooks.Community Comment Notes
Community consensus strongly favors CD, citing official Microsoft documentation that lists PySpark, Spark SQL, and SparkR as valid options for notebook-based scoring. Users reference tutorials showing how to load test datasets into Spark DataFrames and create MLFlowTransformers. Some users initially considered T-SQL due to its existence in other Microsoft products but confirmed it is incorrect for this specific notebook scenario.Official Reference
Exam Strategy
Always identify the execution environment first. If the task involves 'Notebooks' and 'Machine Learning' in Fabric, think Spark (PySpark/Spark SQL). If it involves 'Dashboards' or 'Reports', think DAX. If it involves 'Relational Tables' and 'SQL Queries', think T-SQL.
Frequently Asked Questions
Why can't I use T-SQL for model scoring in Fabric Notebooks?
T-SQL runs in the SQL Pool engine, whereas Fabric Notebooks run in the Spark pool. The PREDICT function in this context refers to Spark-based ML inference, not SQL Server's predictive analytics.
Is R supported for model scoring in Fabric Notebooks?
Yes, SparkR is listed as a supported language alongside PySpark and Spark SQL. However, PySpark is generally preferred due to better ecosystem integration with libraries like MLflow and scikit-learn.
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