The answer is that the data_processing step does not define any outputs. This is the most likely cause because Vertex AI Pipelines require explicit output declarations for any artifact a component produces; without them, the pipeline has no way to pass the train_data artifact downstream to the training step. When debugging missing input artifact in Vertex AI pipeline errors, remember that a component’s output must be both defined in its component spec and then connected via a channel to the consuming step. On the Google Professional Machine Learning Engineer exam, this scenario tests your understanding of the pipeline’s directed acyclic graph (DAG) structure and how artifacts flow between components—a common trap is assuming outputs are automatically inherited from the component’s code. A quick memory tip: “No output spec, no artifact path”—if a step lacks an outputs: block, it cannot feed any data forward, so always check that each producer explicitly lists its artifacts.
PMLE Automating and orchestrating ML pipelines Practice Question
This PMLE practice question tests your understanding of automating and orchestrating ml pipelines. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
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?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue: "most likely"
Why it matters: Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
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.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
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
Error is at training step, not evaluation.
✗
The training step uses the wrong image tag
Why it's wrong here
Image tag is correct; error is about missing input.
✗
The container command for data_processing is incorrect
Why it's wrong here
No evidence of command error.
✓
The data_processing step does not define any outputs
Why this is correct
The pipeline must define an output from data_processing to feed into training.
Clue confirmation
The clue word "most likely" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
✗
The pipeline is missing a deployment step
Why it's wrong here
Not related to the missing input.
Common exam traps
Common exam trap: answer the scenario, not the keyword
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.
Trap categories for this question
Command / output trap
No evidence of command error.
Detailed technical explanation
How to think about this question
In Vertex AI Pipelines, each component's outputs are defined via the `Output[Artifact]` type in the component's YAML or Python function decorator. If a component does not declare any outputs, it cannot produce artifacts for downstream consumption. The pipeline graph is validated at compile time, and if a required input (e.g., `train_data`) is not connected to any upstream output, the pipeline fails with a clear 'Missing required input' error. This is analogous to a DAG where edges must be explicitly defined between nodes.
KKey Concepts to Remember
Read the scenario before looking for a memorised answer.
Find the constraint that changes the correct option.
Eliminate answers that are true in general but not in this case.
TExam Day Tips
→Watch for words such as best, first, most likely and least administrative effort.
→Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
What to study next
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Automating and orchestrating ML pipelines — This question tests Automating and orchestrating ML pipelines — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: 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.
What should I do if I get this PMLE question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Are there clue words in this question I should notice?
Yes — watch for: "most likely". Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Question Discussion
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