hardMultiple Choice
PMLE Practice Question: A large enterprise has multiple ML models…
A large enterprise has multiple ML models deployed in production across different regions. They want to implement a centralized monitoring dashboard that tracks key performance indicators such as prediction accuracy, latency, and error rates for all models, with the ability to drill down into individual model versions. Which approach best meets these requirements?
⚠ Common exam trap
Google Cloud often tests the distinction between logging (Cloud Logging) and monitoring (Cloud Monitoring), where candidates mistakenly think log-based dashboards are sufficient for real-time KPI tracking, ignoring the need for structured, low-latency custom 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
✓
Use Cloud Monitoring with custom metrics reported by each model deployment, and create a unified dashboard with filterable resources
Cloud Monitoring with custom metrics allows each model deployment to report key performance indicators (e.g., prediction accuracy, latency, error rates) as metric time series. These custom metrics can be aggregated into a single unified dashboard, and the dashboard can be configured with filterable resources (e.g., region, model version) to enable drill-down into individual model versions. This approach provides centralized, real-time monitoring without relying on log-based or batch analytics.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use Vertex AI Experiments to log metrics and compare across runs
Why it's wrong here
Vertex AI Experiments tracks training runs and their metrics for comparison, not live production inference telemetry across regions or per-version drill-down. Vertex AI Model Monitoring covers deployed endpoints. Experiments is correct when comparing hyperparameters and training runs during model development.
- ✗
Use Cloud Logging to search logs from each model and create a dashboard
Why it's wrong here
Cloud Logging searches raw log entries and builds log-based dashboards, but it does not compute model performance metrics such as prediction accuracy or skew, nor organise them by deployed model version. Vertex AI Model Monitoring does. Cloud Logging suits debugging and audit trails of application events.
- ✗
Use BigQuery to store prediction logs and then visualize in Looker
Why it's wrong here
BigQuery plus Looker stores and visualises logs but builds no model-aware monitoring; accuracy, latency and per-version drill-down must be assembled by hand. Vertex AI Model Monitoring provides these natively. This stack suits bespoke analytics on prediction data where custom SQL and dashboards are wanted.
- ✓
Use Cloud Monitoring with custom metrics reported by each model deployment, and create a unified dashboard with filterable resources
Why this is correct
Custom metrics let each deployment report accuracy, latency and error rates into Cloud Monitoring, where a single dashboard with filterable resource labels aggregates all models and supports drilling into individual versions. This satisfies the centralised, cross-region visibility requirement.
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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.