The correct option is Write a Python function that streams a very large XML file and extracts requested fields without loading the entire file into memory and use an iterator based approach.

This option directs Copilot to use a streaming parser and iterator based processing so the service can handle multi gigabyte XML inputs within a 512 MB Cloud Run container. By processing elements incrementally and discarding them once the needed fields are extracted, the code avoids materializing the entire document and therefore conserves memory while still meeting the requirement to return only selected fields.

Generate Python code that parses XML and uses very little memory while extracting values is too vague and gives no explicit constraint to avoid loading the whole file. Without clear instructions to stream or iterate over the document, Copilot may still produce a solution that builds an in memory tree.

Generate Python code to parse an XML document and extract specific fields efficiently emphasizes efficiency but does not specify memory conservation or streaming. Efficiency could be interpreted as speed rather than memory usage, which risks a solution that reads the full XML into memory.

Write Python that loads the XML into memory then parallelizes extraction with Apache Beam on Dataflow for speed explicitly instructs loading the full document, which violates the memory constraint of the Cloud Run container. It also introduces external pipeline complexity that is unnecessary for the stated goal of conserving memory within the service.

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