Question

Your team runs a Vertex AI Pipeline daily. A preprocessing component reads a static reference table from Cloud Storage and produces a transformed dataset artifact. You enabled execution caching to avoid recomputing unchanged steps. This week, the reference file in Cloud Storage was updated, but the pipeline continued reusing the cached output from the preprocessing component and never reprocessed the new data. What is the most likely cause?

Accepted Answer

Vertex AI Pipelines caching keys are computed from component inputs (parameters and input artifact metadata) and the component specification, not from the contents of files read internally via a hardcoded Cloud Storage path.

Answer

Execution caching in Vertex AI Pipelines expires only after 24 hours, so the same-day run reused the result before the cache became invalid.

Answer

Caching automatically detects any change to files in the pipeline's project bucket, but only if the bucket has object versioning enabled.

Answer

The pipeline was compiled with caching set at the pipeline level, which ignores per-component input changes entirely.