Gen AI Leader Tests Picking the Least Custom Google Offering — Not Building Models
A non-technical, business-scenario exam for leaders who sponsor gen AI. Stop thinking like an engineer: name the user, the data and the constraint, then choose the prebuilt Google product or technique that fits.
Check Your Readiness →Most candidates understand Google Cloud Certified Generative AI Leader concepts — and still fail. This exam tests how you apply knowledge under pressure.
Four sections of plain-language briefs: an organisation, a goal and one constraint. The guide uses April 2026 names (Agent Platform, formerly Vertex AI; Agent Search, formerly Vertex AI Search; Agent Studio; Gemini Enterprise, which absorbed Agentspace). The winning option is usually the simplest lever that meets every stated requirement.
Gemini through Agent Platform, because it is the flagship
Gemma is Google's family of lightweight open-weight models that can be downloaded, self-hosted at the edge and tuned freely. Gemini is a managed, closed model; self-hosting and open weights are the clue words.
Deploy conversational agents from the Customer Engagement Suite
Agent assist supports the human employee during the call. Conversational agents talk to customers directly for self-service, and conversational insights analyses past interactions. A human on the line points to agent assist.
Use chain-of-thought prompting so the model reasons it through
ReAct interleaves reasoning with tool actions and observations, and function calling lets the model emit a structured request that the application executes. Chain-of-thought improves reasoning but cannot fetch data or act.
Fine-tune the model on hundreds of approved summaries
Try few-shot prompting first: include examples showing the exact layout. It is the cheapest, fastest fix for format consistency; fine-tuning is reserved for behaviour that prompting and grounding cannot correct.
Select the most capable model and run a company-wide pilot
Google's recommended path starts with the business problem, stakeholders and success KPIs, then weighs data readiness, skills and budget to pick a focused quick-win use case before any model or product choice.
Standard website or document search is Agent Search out of the box; contact-centre needs map to the Customer Engagement Suite. The exam treats a bespoke build as overreach unless the workflow is genuinely unique to the business.
Workspace Gemini helps inside Gmail, Docs, Sheets, Slides and Meet. When answers must come from SharePoint, Jira, a CRM and Drive together, with agents doing routine tasks, the product is Gemini Enterprise.
Fine-tuning changes behaviour, style or domain format and costs more. Stale prices, policies or post-cutoff events need grounding: RAG over first-party content via Agent Search, or Google Search grounding for public world information.
Sampling settings control randomness, not truth. Lower temperature and top-p for focused, repeatable output; set output token limits for length and cost. Correctness comes from grounding and better prompts, never from turning randomness up.
Pseudonymisation swaps identifiers for tokens that can be re-linked with a key, so the data is still personal and needs protection. Only anonymisation is irreversible. The exam pairs this with transparency, fairness and named accountability.
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