PMLE Monitoring ML Solutions Practice Question
A company has deployed a model for image classification and wants to monitor for feature drift using XRAI attributions. However, they notice that the XRAI attribution maps are too large and are causing high latency in the monitoring pipeline. What is the most effective way to reduce the overhead of explainability monitoring for image models?
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
✓
Reduce the sampling rate for the explainability feature
Vertex AI Explainable AI supports XRAI for image models, but generating XRAI attributions can be computationally expensive. Sampling a subset of predictions reduces the number of explanations generated, lowering latency and cost.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Disable XRAI and use integrated gradients instead
Why it's wrong here
Integrated gradients may also be expensive; switching doesn't guarantee lower latency.
- ✓
Reduce the sampling rate for the explainability feature
Why this is correct
Sampling rate controls the fraction of predictions for which explanations are generated, directly reducing overhead.
- ✗
Use a smaller image size for the model
Why it's wrong here
While smaller images reduce computation, it requires retraining and may affect accuracy.
- ✗
Increase the number of replicas on the endpoint
Why it's wrong here
Adding replicas improves serving capacity but does not reduce the per-prediction overhead of explainability.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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