PMLE Monitoring ML Solutions Practice Question
An ML engineer wants to monitor a deployed model for fairness across different age groups and genders. Which TWO Vertex AI services should they use together to achieve this? (Choose two.)
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
✓
BigQuery
Vertex AI Model Evaluation provides sliced evaluation when ground truth is available in BigQuery. Vertex AI Explainable AI can help understand feature importance but is not required for fairness monitoring.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Vertex AI Feature Store
Why it's wrong here
Feature Store is not used for fairness evaluation.
- ✗
Vertex AI Explainable AI
Why it's wrong here
Explainable AI is not required for fairness evaluation; it provides feature attributions.
- ✓
BigQuery
Why this is correct
BigQuery stores the ground truth labels and can be used as the source for sliced evaluation.
- ✗
Cloud Monitoring
Why it's wrong here
Cloud Monitoring can display metrics but does not perform fairness evaluation.
- ✓
Vertex AI Model Evaluation
Why this is correct
Model Evaluation with sliced evaluation can compute metrics per subgroup.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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