PMLE Practice Question: The exhibit shows part of a Vertex AI Pipeline…

Exhibit

Refer to the exhibit.

```
# vertex_ai_pipeline.yaml
components:
  - name: data_processing
    container:
      image: us-central1-docker.pkg.dev/my-project/my-repo/data_processor:v1
      command: ["python", "process.py"]
  - name: training
    container:
      image: us-central1-docker.pkg.dev/my-project/my-repo/trainer:v2
      command: ["python", "train.py"]
    inputs:
      - name: train_data
        type: Dataset
    outputs:
      - name: model
        type: Model
  - name: evaluation
    container:
      image: us-central1-docker.pkg.dev/my-project/my-repo/evaluator:v1
    inputs:
      - name: model
        type: Model
      - name: test_data
        type: Dataset
    outputs:
      - name: metrics
        type: Metrics
```

The exhibit shows part of a Vertex AI Pipeline definition. The pipeline fails at the training step with an error: 'Missing required input: train_data'. What is the most likely cause?

⚠ Common exam trap

Google Cloud often tests the distinction between runtime errors (e.g., container image issues) and graph validation errors (e.g., missing input/output connections), leading candidates to confuse a missing output definition with a container or command misconfiguration.

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

✓

The data_processing step does not define any outputs

The error 'Missing required input: train_data' indicates that the training step expects an input artifact named 'train_data', but no upstream step provides it. In Vertex AI Pipelines, a component's output must be explicitly defined and connected to the downstream component's input. Since the data_processing step does not define any outputs, it cannot produce the 'train_data' artifact, causing the training step to fail.

Answer analysis

Option-by-option breakdown

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

✗
The evaluation step expects a metric output but training does not produce it
Why it's wrong here
A metric-output mismatch would surface as an evaluation-step error about missing metrics, not a training-step error naming train_data. Output wiring matters when chaining evaluation to training, but here the training component never received its declared input, so the fault lies upstream.
✗
The training step uses the wrong image tag
Why it's wrong here
A wrong image tag causes an image-pull or entrypoint failure within the training step, not a missing-input message. Image tags matter when the component code itself must change version; here the training component started and reported that train_data was never supplied by its upstream producer.
✗
The container command for data_processing is incorrect
Why it's wrong here
An incorrect container command would typically produce a non-zero exit or command-not-found inside the data_processing step, not a missing-input error at training. Command arguments are the right thing to inspect when a step runs but misbehaves, not when an input is absent.
✓
The data_processing step does not define any outputs
Why this is correct
Vertex AI Pipelines passes data between steps through declared component outputs. If the data_processing component declares no output artifact, no train_data reference exists for the training step, so compilation or runtime reports the missing required input.
✗
The pipeline is missing a deployment step
Why it's wrong here
A missing deployment step would leave the model unserved after training, but the failure occurs at the training step itself, before any deployment runs. Deployment steps are added when the pipeline must register or serve a model, which is unrelated to resolving an unpopulated input artefact.

Go deeper

PMLE Practice Question: The exhibit shows part of a Vertex AI Pipeline…

Exhibit

Why each option matters

Option-by-option breakdown

Related to this question