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Coding & Algorithms questions require understanding of core concepts and practice. PracHub provides solutions with explanations to help you master coding & algorithms interviews.

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This is a medium difficulty Coding & Algorithms question, commonly asked during Technical Screen rounds at Anthropic.

What role is this question designed for?

This question is commonly asked for Software Engineer candidates at Anthropic during technical interviews.

Implement a Parallel Image Processor | Anthropic Interview Question

Q: Implement a Parallel Image Processor

This question evaluates understanding of parallel and process-based concurrency, batch image-processing pipelines, transform correctness, and per-job fault isolation within the Coding & Algorithms domain.

You are building a batch image-processing utility.

Implement a function that receives a list of jobs. Each job contains:

input_path : the source image file
output_path : where to write the processed image
operations : an ordered list of image transformations

Support these operations:

grayscale — convert the image to grayscale.
scale(f) — multiply both width and height by a positive scaling factor f .
resize(w, h) — resize the image to exactly w x h pixels.

For each job, load the image, apply the operations in order, and save the final result to output_path.

Follow-up:

First, implement a correct solution for a small number of images.
Then improve the implementation for a very large batch. Assume image transformations are CPU-intensive and that jobs are independent. Use process-based parallelism to increase throughput, while still returning one result record per input job and handling per-file failures without stopping the entire batch.

Explain any important design choices, such as worker-count selection, memory considerations, and how you would preserve correctness while improving performance.