PMLE Automating and Orchestrating ML Pipelines Practice Question

An organization runs a Vertex AI pipeline that includes a model evaluation step. Team members want to reuse previously computed evaluation metrics when re-running the pipeline with unchanged code and hyperparameters. Which feature should they enable?

⚠ Common exam trap

The trap is overcomplicating the solution by suggesting manual storage or importer components; candidates often forget that Vertex AI Pipelines caching is enabled by default and automatically reuses outputs when inputs and code are unchanged.

Answer choices

Why each option matters

Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.

Correct answer & explanation

✓

Enable pipeline caching (default behavior)

Vertex AI Pipelines has caching enabled by default. When a pipeline is re-run with unchanged code, hyperparameters, and inputs, the evaluation step will reuse the cached output from the previous run, saving time and cost. Team members do not need to manually store outputs or disable caching.

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

✗
Manually store outputs in Cloud Storage and check for existence
Why it's wrong here
Manual Cloud Storage checks bypass Vertex AI Pipelines' built-in component caching, so unchanged evaluation steps recompute metrics instead of reusing them. This approach is tempting when orchestrating external systems that lack native caching, but here the pipeline itself already supports cache reuse keyed on code and parameters.
✓
Enable pipeline caching (default behavior)
Why this is correct
Pipeline caching reuses outputs from previously executed components when the pipeline definition, code, and inputs are unchanged, so the evaluation step's metrics are not recomputed. Enabling it satisfies the stem's requirement to reuse prior evaluation metrics across identical re-runs.
✗
Use the importer component to fetch previous results
Why it's wrong here
The importer component brings external artefacts into a pipeline's metadata store; it does not retrieve prior execution metrics for reuse. Vertex AI Pipelines caches step outputs when the pipeline definition, inputs and parameters are unchanged, so enabling caching reuses the earlier evaluation metrics directly.
✗
Disable caching for the evaluation component
Why it's wrong here
Disabling caching forces the evaluation component to rerun every execution, discarding previously computed metrics rather than reusing them. Disabling caching is tempting when debugging stale artefacts or forcing fresh data reads, but the stem explicitly requires reuse when code and hyperparameters are unchanged.

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PMLE Automating and Orchestrating ML Pipelines Practice Question

Why each option matters

Option-by-option breakdown

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