Part I: Effective Study Methods Aligned with Exam Content

This exam covers four major modules:

1. Fundamentals of Generative AI

2. Google Cloud’s GenAI Offerings

3. Techniques to Improve Model Output

4. Business Strategies & Responsible AI

The following study methods are specifically designed for each of these domains to help you master the content and perform well.

1. Goal-Oriented Modular Learning (Built on Question-Answer Logic)

Module	Guiding Question	Sample Task
Fundamentals	What is a foundation model and how is it trained?	Draw a Transformer diagram + explain pretraining vs. fine-tuning
Cloud Tools	What are the differences between Vertex AI Studio and Codey?	Create a comparison table: Vertex AI vs GenAI Studio vs Model Garden
Output Quality	How do temperature and prompt structure affect results?	Write 3 prompts (Zero-shot, Few-shot, CoT) and compare results
Strategy	How should an organization move from pilot to scale?	Draw the workflow from prototype → pilot → deploy → govern

Why this works: You study with a purpose and learn exactly what the exam will ask — mostly scenario-based and application-style questions.

2. Three-Step Prompt Practice Strategy

Many questions test your understanding of prompt styles, structure, and effectiveness.

Practice Method:

• Step 1: Write a simple prompt (e.g., “Summarize this article”)

• Step 2: Rewrite with role, format, and detail (e.g., “Act as a legal analyst. Summarize in bullet points.”)

• Step 3: Compare the outputs using Gemini, PaLM, or ChatGPT, and note the quality difference

This trains your ability to optimize and debug prompts, which is directly assessed on the exam.

3. Product Recognition Framework (“GenAI Tool Triangle”)

Many questions ask, “Which Google tool should be used in this scenario?”

Use the following keyword-based memory method:

Tool	Function Keywords	Memory Aid
Vertex AI Studio	Prompt testing, light customization	Think “AI lab for developers”
Model Garden	Discover and deploy models	Think “model supermarket”
Codey	Generate, explain, or convert code	Like GitHub Copilot
Imagen	Text-to-image generation	Keyword: “Generate images”
BigQuery + GenAI	Use LLMs in SQL	Think “SQL + LLM together”
Workspace + Gemini	AI in Docs, Sheets, Gmail	Like a personal office assistant

Use keyword matching + use-case mental mapping for rapid product identification.

4. Three-Round Spaced Review (Day 1 – Day 3 – Day 6)

Based on the Ebbinghaus forgetting curve:

• First exposure (Day 1): Learn and practice

• Second review (Day 3): Use flashcards and concept recall

• Third reinforcement (Day 6): Rebuild diagrams and do practice questions

Each round must include an output activity like teaching, summarizing, or drawing to increase retention.

Part II: Exam Strategies (For Real-World Performance)

1. Recognize Question Types and Adjust Reading Style

Type	Description	Strategy
Concept questions	Definitions, comparisons	Use keyword difference cards
Tool selection	Match a scenario to a product	Use “function → tool” reverse mapping
Business scenarios	Evaluate actions for goals	Identify the key verb and business value
Responsible AI	Choose the right ethical principle	Refer to Fairness, Transparency, Privacy, Accountability

2. Spot Keywords to Eliminate Wrong Choices

Common distractor traits:

• Extreme terms like “Always,” “Never,” “Only”

• Vague but appealing phrases like “might help in some cases”

• Looks correct but misrepresents the product’s actual capability (e.g., saying Codey generates images)

3. If You’re Unsure, Use Elimination Strategy

Instead of guessing, follow this 3-step logic:

1. Eliminate obviously incorrect answers (outside product capabilities)

2. Among remaining options, ask: which best fits the scenario?

3. When unsure, prefer practical, actionable choices over vague theory

4. Manage Time and Use Marking Efficiently

• Around 45–60 questions, 90 minutes → ~1.5 minutes per question

• Recommended approach:

  • Finish high-confidence questions in the first 45 mins

  • Use the “Mark” feature for uncertain items

  • Save the last 15 minutes for review of marked questions

5. Night Before the Exam – Just Do These 3 Things

1. Review the four core checklists: Prompt types, Tools, RAG, Responsible AI principles

2. Do 15 mixed practice questions and identify weak areas

3. Use the “Feynman technique”: explain each domain in your own words

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