Unit 4 roadmap — Business strategies for a successful gen AI solution

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 1 — Plan, integrate and measure a gen AI solution

GAIL-U4.T1 · 4 objectives · lecture deck of 13 slides

The exam guide's considerations, quoted. Recognizing the different types of gen AI solutions (e.g., text generation, image generation, code generation, personalized user needs). Identifying the key factors that influence gen AI needs (e.g., business requirements, technical constraints). Describing how to choose the right gen AI solution for a specific business need. Identifying the steps to integrate gen AI into an organization. Identifying techniques to measure the impact of gen AI initiatives.

1. Recognize text, image, code and personalization solution types and the business need each serves.
2. Weigh business requirements and technical constraints when choosing a gen AI solution.
3. Sequence the steps to integrate gen AI into an organization.
4. Choose metrics that measure the impact of a gen AI initiative.

What this topic actually tests. What output does the business need? text, image, code or personalization. Is gen AI the right tool? or traditional, pre-trained, combined — or none. What must change around it? users, process, people, a safe release. How will we know? ROI measures agreed in advance.

Topic 2 — Secure AI systems

GAIL-U4.T2 · 3 objectives · lecture deck of 10 slides

The exam guide's considerations, quoted. Explaining security throughout the ML lifecycle. Identifying the purpose and benefits of Google’s Secure AI Framework (SAIF). Recognizing Google Cloud security tools and their purpose (e.g., secure-by-design infrastructure, Identity and Access Management (IAM), Security Command Center, and workload monitoring tools).

1. Explain security risks and controls at each stage of the machine learning lifecycle.
2. Describe the purpose and benefits of Google's Secure AI Framework (SAIF).
3. Match IAM, Security Command Center, secure-by-design infrastructure and workload monitoring to what each protects.

What this topic actually tests. Which stage? data, training, deployment or serving. Which risk? poisoning, exfiltration, prompt attacks, query floods. Which framework? SAIF's self-assessment names the gaps. Which tool? IAM for access, Security Command Center for risk, Model Armor for prompts, Cloud Monitoring for health.

Topic 3 — Apply responsible AI

GAIL-U4.T3 · 4 objectives · lecture deck of 12 slides

The exam guide's considerations, quoted. Explaining the importance of responsible AI and transparency. Describing privacy considerations (e.g., privacy risks, data anonymization and pseudonymization). Describing the implications of data quality, bias, and fairness. Describing the importance of accountability and explainability in AI systems.

1. Explain why responsible AI and transparency matter to a business.
2. Distinguish privacy risks, data anonymization and pseudonymization.
3. Explain how data quality, bias and fairness affect AI outcomes.
4. Explain accountability and explainability in AI systems.

What this topic actually tests. Have we tested our own risks? safeguards are a floor. Can people be found in our data? minimize, de-identify, check re-identification. Is it fair, and to whom? audit data, test slices. Who explains and owns each decision? attributions, provenance, named roles.