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This signal belongs to the adjacent case “A script needs to start an existing job and collect its run ID,” whose remedy is Call the supported Jobs run-now API or CLI command. The observed result for the present requirement is No token appears in logs and authenticated requests still succeed.

The collected job count matches the complete paginated inventory would demonstrate the separate action Follow pagination tokens until all results have been retrieved, which addresses “A script lists many jobs and silently misses results beyond one API page.” It does not verify the requirement “A script logs access tokens during API debugging” following Move authentication to managed profiles and sanitize request/response logging.

Run details include task states and failure messages would demonstrate the separate action Use supported Jobs get-run API or CLI command, which addresses “An operator needs to inspect details of a failed run automatically.” It does not verify the requirement “A script logs access tokens during API debugging” following Move authentication to managed profiles and sanitize request/response logging.

Refactor import-time code into explicitly called initialization belongs to the separate case “A package import creates unexpected side effects each time a worker loads it,” where Repeated worker imports no longer trigger unintended data writes would be expected. That differs from the condition in this stem: “A wheel was built for a different Python version than the job runtime.”

Select supported compatible compute or replace the incompatible dependency is appropriate when “A task fails only on serverless compute because an unsupported native extension is required”; it would be verified by The task runs successfully in its documented supported environment. It does not directly satisfy “A wheel was built for a different Python version than the job runtime,” whose relevant acceptance evidence is A fresh job imports the wheel without ABI errors.

Declare it as a task or bundle environment dependency belongs to the separate case “An external library is available to the notebook author but not to scheduled tasks,” where Scheduled job task loads the required module successfully would be expected. That differs from the condition in this stem: “A wheel was built for a different Python version than the job runtime.”

This signal belongs to the adjacent case “A task fails only on serverless compute because an unsupported native extension is required,” whose remedy is Select supported compatible compute or replace the incompatible dependency. The observed result for the present requirement is A fresh job imports the wheel without ABI errors.

This signal belongs to the adjacent case “A transitive dependency upgrade breaks a stable transformation,” whose remedy is Lock a compatible dependency graph and test before redeploying. The observed result for the present requirement is A fresh job imports the wheel without ABI errors.

That observation is a valid indicator for “A package import creates unexpected side effects each time a worker loads it” when using Refactor import-time code into explicitly called initialization. The scenario instead tests “A wheel was built for a different Python version than the job runtime”; the relevant result is A fresh job imports the wheel without ABI errors.

The alternative Lock a compatible dependency graph and test before redeploying has a distinct purpose—A transitive dependency upgrade breaks a stable transformation. It would not establish the required outcome (An external library is available to the notebook author but not to scheduled tasks); the necessary verification concerns Scheduled job task loads the required module successfully.

Build a compatible distribution or update runtime within supported constraints is appropriate when “A wheel was built for a different Python version than the job runtime”; it would be verified by A fresh job imports the wheel without ABI errors. It does not directly satisfy “An external library is available to the notebook author but not to scheduled tasks,” whose relevant acceptance evidence is Scheduled job task loads the required module successfully.

Refactor import-time code into explicitly called initialization belongs to the separate case “A package import creates unexpected side effects each time a worker loads it,” where Repeated worker imports no longer trigger unintended data writes would be expected. That differs from the condition in this stem: “An external library is available to the notebook author but not to scheduled tasks.”

A fresh job imports the wheel without ABI errors would demonstrate the separate action Build a compatible distribution or update runtime within supported constraints, which addresses “A wheel was built for a different Python version than the job runtime.” It does not verify the requirement “An external library is available to the notebook author but not to scheduled tasks” following Declare it as a task or bundle environment dependency.

The known-good test case passes under the pinned versions would demonstrate the separate action Lock a compatible dependency graph and test before redeploying, which addresses “A transitive dependency upgrade breaks a stable transformation.” It does not verify the requirement “An external library is available to the notebook author but not to scheduled tasks” following Declare it as a task or bundle environment dependency.

That observation is a valid indicator for “A task fails only on serverless compute because an unsupported native extension is required” when using Select supported compatible compute or replace the incompatible dependency. The scenario instead tests “An external library is available to the notebook author but not to scheduled tasks”; the relevant result is Scheduled job task loads the required module successfully.

This action solves a different issue: “An external library is available to the notebook author but not to scheduled tasks.” Its normal signal is Scheduled job task loads the required module successfully. Here the requested behavior is “A transitive dependency upgrade breaks a stable transformation,” so that signal is not sufficient.

Select supported compatible compute or replace the incompatible dependency belongs to the separate case “A task fails only on serverless compute because an unsupported native extension is required,” where The task runs successfully in its documented supported environment would be expected. That differs from the condition in this stem: “A transitive dependency upgrade breaks a stable transformation.”

The alternative Refactor import-time code into explicitly called initialization has a distinct purpose—A package import creates unexpected side effects each time a worker loads it. It would not establish the required outcome (A transitive dependency upgrade breaks a stable transformation); the necessary verification concerns The known-good test case passes under the pinned versions.

This signal belongs to the adjacent case “A wheel was built for a different Python version than the job runtime,” whose remedy is Build a compatible distribution or update runtime within supported constraints. The observed result for the present requirement is The known-good test case passes under the pinned versions.

That observation is a valid indicator for “A task fails only on serverless compute because an unsupported native extension is required” when using Select supported compatible compute or replace the incompatible dependency. The scenario instead tests “A transitive dependency upgrade breaks a stable transformation”; the relevant result is The known-good test case passes under the pinned versions.

Repeated worker imports no longer trigger unintended data writes pertains to a different control (Refactor import-time code into explicitly called initialization) and a different need (A package import creates unexpected side effects each time a worker loads it). It cannot establish that Lock a compatible dependency graph and test before redeploying met the stated goal.

This action solves a different issue: “An external library is available to the notebook author but not to scheduled tasks.” Its normal signal is Scheduled job task loads the required module successfully. Here the requested behavior is “A package import creates unexpected side effects each time a worker loads it,” so that signal is not sufficient.

The alternative Build a compatible distribution or update runtime within supported constraints has a distinct purpose—A wheel was built for a different Python version than the job runtime. It would not establish the required outcome (A package import creates unexpected side effects each time a worker loads it); the necessary verification concerns Repeated worker imports no longer trigger unintended data writes.

Lock a compatible dependency graph and test before redeploying is appropriate when “A transitive dependency upgrade breaks a stable transformation”; it would be verified by The known-good test case passes under the pinned versions. It does not directly satisfy “A package import creates unexpected side effects each time a worker loads it,” whose relevant acceptance evidence is Repeated worker imports no longer trigger unintended data writes.

Scheduled job task loads the required module successfully pertains to a different control (Declare it as a task or bundle environment dependency) and a different need (An external library is available to the notebook author but not to scheduled tasks). It cannot establish that Refactor import-time code into explicitly called initialization met the stated goal.

This signal belongs to the adjacent case “A task fails only on serverless compute because an unsupported native extension is required,” whose remedy is Select supported compatible compute or replace the incompatible dependency. The observed result for the present requirement is Repeated worker imports no longer trigger unintended data writes.

A fresh job imports the wheel without ABI errors pertains to a different control (Build a compatible distribution or update runtime within supported constraints) and a different need (A wheel was built for a different Python version than the job runtime). It cannot establish that Refactor import-time code into explicitly called initialization met the stated goal.

Refactor import-time code into explicitly called initialization is appropriate when “A package import creates unexpected side effects each time a worker loads it”; it would be verified by Repeated worker imports no longer trigger unintended data writes. It does not directly satisfy “A task fails only on serverless compute because an unsupported native extension is required,” whose relevant acceptance evidence is The task runs successfully in its documented supported environment.

Lock a compatible dependency graph and test before redeploying belongs to the separate case “A transitive dependency upgrade breaks a stable transformation,” where The known-good test case passes under the pinned versions would be expected. That differs from the condition in this stem: “A task fails only on serverless compute because an unsupported native extension is required.”

Build a compatible distribution or update runtime within supported constraints is appropriate when “A wheel was built for a different Python version than the job runtime”; it would be verified by A fresh job imports the wheel without ABI errors. It does not directly satisfy “A task fails only on serverless compute because an unsupported native extension is required,” whose relevant acceptance evidence is The task runs successfully in its documented supported environment.

Repeated worker imports no longer trigger unintended data writes would demonstrate the separate action Refactor import-time code into explicitly called initialization, which addresses “A package import creates unexpected side effects each time a worker loads it.” It does not verify the requirement “A task fails only on serverless compute because an unsupported native extension is required” following Select supported compatible compute or replace the incompatible dependency.

That observation is a valid indicator for “A wheel was built for a different Python version than the job runtime” when using Build a compatible distribution or update runtime within supported constraints. The scenario instead tests “A task fails only on serverless compute because an unsupported native extension is required”; the relevant result is The task runs successfully in its documented supported environment.

This signal belongs to the adjacent case “An external library is available to the notebook author but not to scheduled tasks,” whose remedy is Declare it as a task or bundle environment dependency. The observed result for the present requirement is The task runs successfully in its documented supported environment.