hardMultiple Choice
PDE Practice Question: A financial services company must ensure that…
A financial services company must ensure that predictions from a deployed model do not become biased against protected groups. They have a monitoring system in place. Which metric should they track?
⚠ Common exam trap
Candidates often confuse operational metrics (latency, accuracy) with fairness metrics, assuming high accuracy guarantees fairness, but the Google Professional Data Engineer exam tests that bias can exist even with high accuracy if the model performs differently across demographic segments.
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
✓
Prediction distribution across demographic segments
Tracking prediction distribution across demographic segments (option B) directly monitors for bias by comparing the model's output rates for different protected groups. If the distribution diverges significantly, it indicates potential disparate impact, which is the core concern for fairness in deployed models. This aligns with monitoring for algorithmic fairness, not just operational performance.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Prediction latency
Why it's wrong here
Prediction latency measures serving performance, revealing nothing about differential outcomes across protected groups. It is tempting because latency monitoring is standard for deployed endpoints, but bias detection requires comparing prediction distributions or error rates between protected and non-protected cohorts, which latency cannot express.
- ✓
Prediction distribution across demographic segments
Why this is correct
Tracking prediction distribution across demographic segments reveals whether outcomes diverge between protected groups, exposing disparate impact. Comparing approval or score rates per segment detects bias that aggregate accuracy metrics would hide, satisfying the fairness monitoring requirement.
- ✗
Per-query input feature distribution
Why it's wrong here
Per-query input feature distribution tracks drift in incoming data, not disparate outcomes across protected groups. It is tempting because distribution shift can indirectly affect fairness, but detecting bias requires outcome-based metrics such as disparate impact or bias drift computed on predictions grouped by protected attribute.
- ✗
Model accuracy over time
Why it's wrong here
Aggregate accuracy over time can stay stable while errors concentrate on a protected group, masking bias. It is tempting because accuracy is the default model-health metric, but fairness requires disaggregated outcome metrics such as disparate impact or bias drift measured per protected attribute.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This PDE practice question is part of Courseiva's free Google Cloud certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the PDE exam.