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PDE Practice Question: Refer to the exhibit

Network Topology
outputdata-pathargs: [inputmodel-pathmodel-outputthresholdRefer to the exhibit.```yamlpipeline:name: training-pipelinecomponents:- component: data-ingestionimage: gcr.io/my-project/data-ingestion:latest- component: trainingimage: gcr.io/my-project/training:latestdepends-on: data-ingestion- component: evaluationimage: gcr.io/my-project/evaluation:latestdepends-on: training```

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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Written by Johnson Ajibi, MSc IT Security

Senior Network & Security Engineer · founder of Courseiva

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