Chapter 6 of 6 · Google Cloud Professional Machine Learning Engineer
Monitoring AI solutions
AI security, responsible AI, explainability, skew and drift.
- 2topics
- 11 minfast track
- 1diagrams and exhibits
- 13%of the exam
Topics in this chapter
Take them in order, or jump to the one you need.
- 1AI Security, Responsible AI and ExplainabilityProtect AI systems from data exfiltration and malicious prompts with regex, safety filters and Model Armor, monitor for bias and…5 min
- 2Model Monitoring, Drift and Evaluating Gen AIConfigure Model Monitoring for skew, data drift and attribution drift, set up continuous evaluation, and monitor, test and…Diagram6 min
About this chapter
What this chapter covers
Monitoring AI solutions is about 13% of the Google Cloud Professional Machine Learning Engineer exam. It covers keeping models safe, fair and accurate after they go live on Gemini Enterprise Agent Platform (the platform formerly called Vertex AI): protecting data and models from misuse, checking for bias and explaining predictions, and spotting when production data or quality drifts away from what the model learned.
It covers identifying risks (secure AI systems that resist data exfiltration, malicious prompting and leaks of sensitive data to LLMs, using regex, safety filters and Model Armor; responsible AI practices such as monitoring for bias; model explainability on Agent Platform) and monitoring, testing and troubleshooting (Model Monitoring on Gemini Enterprise Agent Platform and continuous evaluation; training-serving skew, data drift, concept drift and feature attribution drift; monitoring, testing and evaluating gen AI solutions).
The exam has 50 to 60 multiple choice and multiple select questions in 2 hours. Questions here usually describe a symptom in production, such as falling accuracy, a leaked secret or a biased outcome, and ask "What should you do?".
How the topics fit together
Guardrails protect inputs and outputs, explanations and bias checks show whether decisions are sound, and monitoring shows when the model no longer matches reality. Each finding feeds a fix: a filter, a data change or a retrain.
Name the drift precisely. Training data differs from serving data: training-serving skew. Serving data changes over time: data drift. The input to label relationship changes: concept drift. The reasons for predictions change: feature attribution drift.
How to use these pages
Each topic page has the same shape, so you can skim for what you need.
- What it is: the idea in plain words.
- How it works: the mechanism, with tables and code examples.
- What to remember for the exam: the facts to memorise and how questions are phrased.
- Common mistakes: the classic traps.
- Check yourself: three questions to test your understanding.
Ready for more practice?
The full Google Cloud Professional Machine Learning Engineer question bank has 749 questions, with timed practice exams and an explanation for every answer.
This study guide is independent and is not affiliated with, endorsed by, or sponsored by Google Cloud. Vendor names, certification names and exam codes are trademarks of their respective owners and are used for identification only.