Which languages can be used to perform model scoring with the PREDICT function?

Which languages can be used to perform model scoring with the PREDICT function?

• A. T-SQL and DAX
• B. PySpark and DAX
• C. Spark SQL and T-SQL
• D. PySpark and Spark SQL

Answer: (D)

The PREDICT function is designed to leverage machine learning models for scoring data, and in the context of this question, it can be utilized specifically with PySpark and Spark SQL. PySpark is the Python API for Spark, allowing for distributed processing using Python. It seamlessly integrates with Spark's capabilities for handling large data sets, which is essential for tasks like model scoring. The PREDICT function can be applied within PySpark to read models and score new data efficiently. Similarly, Spark SQL is the SQL interface for Apache Spark, enabling users to perform data manipulation using SQL queries. While engaging with machine learning models, Spark SQL can also make use of the PREDICT function to score data, allowing access through SQL-like syntax, which is highly beneficial for users familiar with SQL. The combination of these two languages signifies the flexibility in addressing model scoring needs within a Spark ecosystem, making it suitable for researchers and data scientists who utilize these languages for greater scalability and efficiency in handling data workloads.

The PREDICT function is designed to leverage machine learning models for scoring data, and in the context of this question, it can be utilized specifically with PySpark and Spark SQL.

PySpark is the Python API for Spark, allowing for distributed processing using Python. It seamlessly integrates with Spark's capabilities for handling large data sets, which is essential for tasks like model scoring. The PREDICT function can be applied within PySpark to read models and score new data efficiently.

Similarly, Spark SQL is the SQL interface for Apache Spark, enabling users to perform data manipulation using SQL queries. While engaging with machine learning models, Spark SQL can also make use of the PREDICT function to score data, allowing access through SQL-like syntax, which is highly beneficial for users familiar with SQL.

The combination of these two languages signifies the flexibility in addressing model scoring needs within a Spark ecosystem, making it suitable for researchers and data scientists who utilize these languages for greater scalability and efficiency in handling data workloads.