Unit 1 roadmap — Fundamentals of gen AI

Under each topic, Google's own considerations are quoted from Google's Generative AI Leader exam guide, retrieved 2026-10-06; the numbered objectives beneath them are this hive's learning objectives, written over those considerations. Each topic closes with its own summary of what the exam actually tests, taken from its lecture.

Topic 2 — Understand AI concepts and learning approaches

GAIL-U1.T2 · 4 objectives · lecture deck of 13 slides

The exam guide's considerations, quoted. Defining core gen AI concepts (e.g., artificial intelligence, natural language processing, machine learning, generative AI, foundation models, multimodal foundation models, diffusion models, prompt tuning, prompt engineering, large language models). Describing the machine learning approaches (e.g., supervised, unsupervised, reinforcement).

1. Distinguish artificial intelligence, machine learning, natural language processing and generative AI.
2. Distinguish foundation, large language, multimodal and diffusion models.
3. Distinguish prompt engineering from learned prompt tuning and model tuning.
4. Choose supervised, unsupervised or reinforcement learning for a stated learning problem.

What this topic actually tests. Which question does the label answer? field, method, material or output. What does the description prove? scale, language, modalities or method — not fit. What changed? the prompt, a learned prefix, or the parameters. What signal? labels, patterns or rewards.

Topic 3 — Follow the machine learning lifecycle

GAIL-U1.T3 · 2 objectives · lecture deck of 10 slides

The exam guide's considerations, quoted. Identifying the stages of the machine learning lifecycle; data ingestion, data preparation, model training, model deployment, and model management; and the Google Cloud tools for each stage.

1. Sequence ingestion, preparation, training, deployment and ongoing management.
2. Match lifecycle needs to relevant Google Cloud tools without confusing Model Garden and Model Registry.

What this topic actually tests. Order: ingest, prepare, train and evaluate, deploy, manage — and loop back on drift. Proof: each stage proves only its own step. Tools: Pub/Sub and Dataflow move and shape data; AutoML or custom training learn; Model Garden finds models; Model Registry manages yours; Model Monitoring watches.

Topic 1 — Choose a foundation model for a business use case

GAIL-U1.T1 · 3 objectives · lecture deck of 11 slides

The exam guide's considerations, quoted. Identifying how to choose the appropriate foundation model for a business use case (e.g., modality, context window, security, availability and reliability, cost, performance, fine-tuning, and customization).

1. Screen model candidates against required inputs, context and deployment constraints.
2. Choose workload-specific evaluation evidence before selecting a model.
3. Compare eligible candidates using measured quality, latency and workload cost.

What this topic actually tests. Can it do the job at all? modality, context, features, data controls, availability. Does it do our job well? evidence from our own prompts, scored broadly and read by people. Is it the cheapest that clears the bar? priced on our workload, with quality and latency as thresholds.

Topic 4 — Match generative AI to business work

GAIL-U1.T4 · 2 objectives · lecture deck of 10 slides

The exam guide's considerations, quoted. Identifying business use cases where gen AI can create, summarize, discover, and automate (e.g., text generation, image generation, code generation, video generation, data analysis, and personalized user experience). Describing how various data types are used in gen AI and the business implications.

1. Match create, summarize, discover and automate use cases to business outcomes and review needs.
2. Choose input and output data types for text, image, code, video, analysis and personalized experiences.

What this topic actually tests. Outcome: a measurable goal, decided before the technology. Job: create, summarize, discover or automate, with a review step. Data: the input and output types of each task, and whether a traditional prediction should feed a generative step.

Topic 5 — Judge whether data is fit for the task

GAIL-U1.T5 · 3 objectives · lecture deck of 10 slides

The exam guide's considerations, quoted. Explaining the characteristics and importance of data quality and data accessibility in AI (e.g., completeness, consistency, relevance, availability, cost, format). Identifying the differences between structured and unstructured data, and identifying real-world examples of each type. Identifying the differences between labeled and unlabeled data.

1. Evaluate completeness, consistency, relevance, availability, cost and format before using data.
2. Distinguish structured and unstructured data using business examples.
3. Distinguish labeled and unlabeled examples and their learning uses.

What this topic actually tests. Is it fit for this purpose? complete, consistent, recent, reachable, valid, affordable to label. Where does its meaning live? in columns, or inside files. Does it carry the answer? labeled to learn from, unlabeled to predict on.

Topic 6 — Locate decisions in the generative AI landscape

GAIL-U1.T6 · 5 objectives · lecture deck of 14 slides

The exam guide's considerations, quoted. Infrastructure Models Platforms Agents Applications

1. Explain the infrastructure layer and its compute, storage and networking responsibilities.
2. Distinguish reusable models from the applications that use them.
3. Explain how platforms support development, evaluation, deployment and operations.
4. Explain how agents combine model decisions with tools and bounded workflows.
5. Identify the user-facing application layer and its business workflow.

What this topic actually tests. Infrastructure runs everything. Models are reused across applications. Platforms build, deploy and govern. Agents act through tools within permissions. Applications change a workflow for real people.

Topic 7 — Choose among Google model families

GAIL-U1.T7 · 4 objectives · lecture deck of 12 slides

The exam guide's considerations, quoted. Gemini Gemma Imagen Veo

1. Recognize Gemini use cases and verify model-specific input and output capabilities.
2. Recognize Gemma open-weight use cases and the resulting deployment and license responsibilities.
3. Recognize Imagen image-generation and supported editing use cases.
4. Recognize Veo video-generation use cases and verify version-specific capabilities.

What this topic actually tests. Gemini for most text, code and multimodal work. Gemma when you must run or tune the model yourself — and run it. Imagen for its specialized image strengths, with Gemini as Google's starting point. Veo for video, within each version's documented limits.