hardMultiple ChoiceObjective-mapped
PDE Practice Question: In the Vertex AI Pipeline component YAML exhibit,…
Exhibit
Refer to the exhibit.
```
# Vertex AI Pipeline component YAML
name: model-evaluation
inputs:
model_path:
type: String
test_data_path:
type: String
threshold_accuracy:
type: Float
default: 0.85
outputs:
evaluation_metrics:
type: Metrics
implementation:
container:
image: gcr.io/my-project/eval:latest
args: [
--model_path, {inputValue: model_path},
--test_data_path, {inputValue: test_data_path},
--threshold_accuracy, {inputValue: threshold_accuracy},
--output_path, {outputPath: evaluation_metrics}
]
```In the Vertex AI Pipeline component YAML exhibit, the component is designed to evaluate a model and produce metrics. If the threshold_accuracy is set to 0.85, what is the expected behavior of this component?
⚠ Common exam trap
Google Cloud often tests the misconception that setting a threshold in a component's YAML automatically enforces that threshold (e.g., causing failure or deployment), when in reality the YAML only defines the interface and the component's code must explicitly implement such logic.
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
✓
It will output the evaluation metrics, and the pipeline can use them for conditional decisions
In Vertex AI Pipelines, a component's YAML definition specifies inputs, outputs, and implementation. Setting `threshold_accuracy` to 0.85 defines a parameter that the component can use internally, but by itself it does not trigger deployment or cause failure. The component's expected behavior is to output evaluation metrics, and the pipeline can then use those metrics in conditional logic (e.g., via `Condition` or `if/else` tasks) to decide subsequent steps, such as model deployment or retraining.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
It will output the evaluation metrics, and the pipeline can use them for conditional decisions
Why this is correct
The component outputs metrics for downstream use.
- ✗
It will deploy the model if the accuracy meets the threshold
Why it's wrong here
Deployment is not part of this component.
- ✗
It will ignore the threshold_accuracy input if not provided
Why it's wrong here
It has a default value, so it will use 0.85.
- ✗
It will fail if the model accuracy is below 0.85
Why it's wrong here
No failure condition is defined in the YAML.
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