Chapter 3 of 6 · Google Cloud Digital Leader
Innovating with Google Cloud Artificial Intelligence
AI, ML and gen AI terms, agentic AI, data quality, responsible AI and Google Cloud's AI offerings.
- 4topics
- 17 minfast track
- 1diagrams and exhibits
- 18%of the exam
Topics in this chapter
Take them in order, or jump to the one you need.
- 1AI, Machine Learning, Generative AI and AgentsCloud Digital Leader definitions of AI, ML, generative AI, data analytics and BI, how agentic AI is reshaping work, and the…4 min
- 2Data Quality and Responsible AIWhy AI needs high-quality data, the six data quality dimensions, explainable and responsible AI, and the key benefits of Google…4 min
- 3Choosing Google Cloud AI SolutionsHow to choose a Google Cloud AI solution: speed, effort, differentiation and skills, Gemini Enterprise Agent Platform, Gemini…Diagram5 min
- 4Custom Models, BigQuery ML and AI HypercomputerBuild custom models with your own data using Agent Studio and AutoML on Agent Platform, run ML with SQL in BigQuery ML, and the…4 min
About this chapter
What this chapter covers
This section is about how artificial intelligence creates business value, and it is worth about 18 percent of the exam. It covers fundamental AI and ML concepts (definitions, agentic AI, the problems ML solves, data quality and responsible AI) and how Google Cloud's AI offerings create value (choosing an approach, Gemini Enterprise Agent Platform, pre-trained APIs, custom models, AI Hypercomputer and BigQuery ML).
You are not asked to build or tune a model. You are asked to recognize the right kind of AI solution for a business situation and the tradeoffs between options.
How the topics fit together
- AI, machine learning, generative AI and agents covers definitions, agentic AI and the business problems ML solves.
- Data quality and responsible AI covers data quality dimensions, explainability, responsible AI and the benefits of Google Cloud's AI.
- Choosing Google Cloud AI solutions covers strategic considerations, Gemini Enterprise Agent Platform, Gemini and pre-trained APIs.
- Custom models, BigQuery ML and AI Hypercomputer covers building models with your own data and the infrastructure behind it.
The fastest path to value is usually a pre-built API or agent. Building a custom model makes sense when the problem is unique to the business and you have the data and skills.
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 examples and tables.
- 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 Digital Leader question bank has 512 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.