PMLE Monitoring ML Solutions Practice Question
A team is using Vertex AI Explainability with a deployed model. They need to generate explanations for image classification predictions. Which explanation method should they configure in the ExplanationSpec?
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
✓
XRAI
XRAI (eXplanation with Ranked Area Integrals) is specifically designed for image models to highlight regions that contribute to the prediction.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
XRAI
Why this is correct
XRAI is the method designed for image models in Vertex AI Explainability.
- ✗
SHAP with KernelExplainer
Why it's wrong here
KernelExplainer is not supported natively in Vertex AI Explainability.
- ✗
Sampled Shapley
Why it's wrong here
Sampled Shapley is for tabular data, not images.
- ✗
Integrated Gradients
Why it's wrong here
Integrated Gradients works for images but XRAI is optimized and recommended.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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