hardMultiple Select
PMLE Practice Question: A financial institution uses a machine learning…
A financial institution uses a machine learning model to approve loans. They must monitor for fairness and bias. Which THREE Google Cloud tools or features can help them achieve this? (Choose 3.)
⚠ Common exam trap
Google Cloud often tests the distinction between data security tools (like DLP) and ML fairness tools, so candidates mistakenly select Cloud DLP thinking it addresses bias because it handles sensitive attributes, but DLP does not analyze model predictions or fairness 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
✓
What-If Tool
The What-If Tool (A) is a visualization toolkit integrated with Vertex AI that lets analysts probe a trained model with counterfactual and hypothetical inputs to inspect how predictions change across sensitive attributes like gender or ethnicity, directly supporting fairness and bias analysis. Vertex AI Model Monitoring (B) continuously tracks deployed models for training-serving skew and prediction drift, and can be configured with fairness-related metrics so the institution detects when a model's behavior degrades or becomes biased in production. Explainable AI (E) provides feature attributions (e.g., Sampled Shapley and integrated gradients) through Vertex AI, showing which features drive each loan decision so reviewers can confirm that protected attributes are not improperly influencing approvals. Cloud Data Loss Prevention (C) is for discovering and redacting sensitive data such as PII, not for evaluating model fairness, and Cloud Healthcare API (D) is a healthcare-specific data interoperability service unrelated to loan-model bias 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.
- ✓
What-If Tool
Why this is correct
The What-If Tool lets you probe a trained model with counterfactual examples, swapping protected attributes such as gender or ethnicity to reveal whether predictions shift unfairly. This directly satisfies the stem's fairness and bias monitoring requirement by exposing disparate impact across loan approval decisions.
- ✓
Vertex AI Model Monitoring
Why this is correct
Vertex AI Model Monitoring detects training-serving skew and prediction drift, including distribution shifts across sensitive attributes such as age or gender. This satisfies the fairness-monitoring constraint by surfacing when loan approval inputs diverge from baseline, flagging potential bias before it affects lending decisions.
- ✗
Cloud Data Loss Prevention
Why it's wrong here
Cloud Data Loss Prevention classifies and redacts sensitive data such as card numbers and personal identifiers, so it cannot measure disparate impact across protected groups. It is tempting because bias monitoring often begins with discovering sensitive attributes in training data, and DLP would be the right choice for locating or de-identifying that data before training.
- ✗
Cloud Healthcare API
Why it's wrong here
The Cloud Healthcare API is specific to healthcare data and not designed for general bias monitoring.
- ✓
Explainable AI
Why this is correct
Explainable AI provides feature attributions for each prediction, exposing whether protected attributes such as gender or ethnicity disproportionately drive loan decisions. This satisfies the fairness and bias monitoring requirement by revealing per-feature contribution patterns that aggregate accuracy metrics cannot surface.
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JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This PMLE 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 PMLE exam.