In the context of performance, what is the purpose of parallel processing groups within a custom transformer?

• A. To distribute processing load and increase performance.
• B. To merge multiple inputs into one.
• C. To enforce feature filtering.
• D. To guarantee output order.

Answer: (A)

Parallel processing groups inside a custom transformer are used to divide the transformer’s work across multiple worker threads, enabling concurrent processing of features. This distributes the processing load, so multiple features can be handled at the same time, which boosts throughput and can noticeably improve performance on large datasets or when the inner operations are CPU-intensive or involve waiting on I/O.

They don’t serve to merge inputs, enforce filtering, or guarantee a specific output order. In parallel execution, outputs can arrive in different orders unless you add steps to enforce ordering. Also, real-world gains depend on the workload and hardware, and some steps may not benefit from parallelism if they aren’t thread-safe or already near maximum throughput.