Unit 3 roadmap — Innovating with Google Cloud Artificial Intelligence

Every objective below is quoted from Google's Cloud Digital Leader exam guide, retrieved 2026-09-22. Under each topic is that topic's own summary of what the exam actually tests, taken from its lecture.

Topic 1 — AI and ML Fundamentals

CDL-U3.T1 · 6 objectives · lecture deck of 14 slides

1. Define artificial intelligence (AI) and machine learning (ML).
2. Differentiate the capabilities of AI and ML from data analytics and business intelligence.
3. Discuss the types of problems that ML can solve.
4. Explain the business value ML creates, including: ability to work with large datasets; scaling business decisions; and unlocking unstructured data.
5. Explain why high-quality, accurate data is essential for successful ML models.
6. Discuss the importance of explainable and responsible AI

What this topic actually tests. Learned or programmed? only learning from data is ML. Past or next? BI explains what happened; ML predicts what might. Labeled or not? it picks supervised or unsupervised. Good data in, and an explanation out? without both, accuracy is not trust.

Topic 2 — Google Cloud’s AI and ML solutions

CDL-U3.T2 · 2 objectives · lecture deck of 8 slides

1. Explain which decisions and tradeoffs organizations need to consider when selecting Google Cloud AI/ML solutions and products, including: speed; effort; differentiation; required expertise.
2. Discuss which Google Cloud AI and ML solutions and products might apply given different business use cases, including: pre-trained APIs; AutoML; build custom models.

What this topic actually tests. General task? pre-trained API. Your data, a standard objective, no coders? AutoML — or BigQuery ML if it is already in BigQuery. Your own objective or metric, and data scientists on hand? custom training. Speed and ease run one way; differentiation runs the other.

Topic 3 — Building and using Google Cloud AI and ML solutions

CDL-U3.T3 · 5 objectives · lecture deck of 12 slides

1. Discuss how BigQuery ML lets users create and execute machine learning models in BigQuery by using standard SQL queries.
2. Select which Google Cloud pre-trained API best applies to different business use cases, including: Natural Language API, Vision API, Cloud Translation API, Speech-to-Text API, and Text-to-Speech API.
3. Explain how an organization can create business value by using their own data to train custom ML models with AutoML.
4. Discuss how building custom models by using Google Cloud’s Vertex AI can create opportunities for business differentiation.
5. Recognize TensorFlow as an end-to-end open source set of tools for building and training machine learning models and that Cloud Tensor Processing Unit (TPU) is Google’s proprietary hardware optimized for TensorFlow and ML performance.

What this topic actually tests. Does the business need its own model? No → a pre-trained API, chosen by input. Is the data in BigQuery and the team fluent in SQL? → BigQuery ML. Own data, no coders? → AutoML. Is the model the differentiator? → custom training. And TensorFlow is software; the TPU is hardware.