hardMultiple ChoiceObjective-mapped
PDE Practice Question: Refer to the exhibit
Network Topology
Refer to the exhibit. A data scientist notices that the evaluation component rarely passes the threshold, causing the pipeline to fail often. What should they do to improve efficiency?
⚠ Common exam trap
Google Cloud often tests the misconception that simply adjusting thresholds or removing components is the solution, when the correct approach is to add conditional logic to gate resource-intensive steps based on upstream quality metrics.
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
✓
Add a conditional component that only runs evaluation if training metrics are above a certain level
Adding a conditional component that only runs evaluation when training metrics exceed a certain threshold prevents unnecessary evaluation runs on poorly performing models. This reduces pipeline failures by ensuring that evaluation, which may be resource-intensive or prone to failure with low-quality inputs, is only triggered when the model has demonstrated sufficient training performance. This approach optimizes resource usage and pipeline reliability without sacrificing the evaluation step entirely.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Reduce the training dataset size
Why it's wrong here
Reducing data may worsen model performance, not solve the threshold issue.
- ✓
Add a conditional component that only runs evaluation if training metrics are above a certain level
Why this is correct
Conditional execution saves cost and time by skipping evaluation on underperforming models.
- ✗
Remove the evaluation component
Why it's wrong here
Removing evaluation eliminates quality checks, which is not advisable.
- ✗
Increase the threshold value
Why it's wrong here
Raising threshold would accept worse models, reducing overall quality.
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