PMLE Practice Question: Drag and drop the steps to set up data lineage…

Drag and drop the steps to set up data lineage tracking for ML pipelines using Vertex AI Experiments in the correct order.

Drag or tap steps into the slots.

Steps

Order

1Step 1

2Step 2

3Step 3

4Step 4

5Step 5

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

✓

Initialize the Vertex AI SDK, then create an experiment, then log metrics and parameters, then record artifacts, then review lineage.

Start with SDK setup, then create an experiment, log metrics, record artifacts, and review lineage.

Answer analysis

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

✓
Initialize the Vertex AI SDK, then create an experiment, then log metrics and parameters, then record artifacts, then review lineage.
Why this is correct
This is the correct order because you must set up the SDK first to access Vertex AI, then create an experiment to organize runs, log metrics during training, record artifacts to track data/model lineage, and finally review the lineage in the UI.
✗
Create an experiment, then initialize the SDK, then log metrics, then review lineage, then record artifacts.
Why it's wrong here
This is incorrect because you cannot create an experiment without initializing the SDK first, and you should record artifacts before reviewing lineage, as lineage relies on artifact metadata.
✗
Initialize the SDK, then log metrics and parameters, then create an experiment, then record artifacts, then review lineage.
Why it's wrong here
This is incorrect because you must create an experiment before logging metrics; otherwise, the metrics are not associated with a specific experiment.
✗
Initialize the SDK, then create an experiment, then record artifacts, then log metrics, then review lineage.
Why it's wrong here
This is incorrect because you should log metrics during training, and recording artifacts typically happens after logging metrics to capture the exact model version and dataset used.

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