Google Generative-AI-Leader Exam Syllabus Topics:

Topic	Details
Topic 1	Google Cloud’s Generative AI Offerings: This section of the exam measures the skills of Cloud Architects and highlights Google Cloud’s strengths in generative AI. It emphasizes Google’s AI-first approach, enterprise-ready platform, and open ecosystem. Candidates will learn about Google’s AI infrastructure, including TPUs, GPUs, and data centers, and how the platform provides secure, scalable, and privacy-conscious solutions. The section also explores prebuilt AI tools such as Gemini, Workspace integrations, and Agentspace, while demonstrating how these offerings enhance customer experience and empower developers to build with Vertex AI, RAG capabilities, and agent tooling.
Topic 2	Fundamentals of Generative AI: This section of the exam measures the skills of AI Engineers and focuses on the foundational concepts of generative AI. It covers the basics of artificial intelligence, natural language processing, machine learning approaches, and the role of foundation models. Candidates are expected to understand the machine learning lifecycle, data quality, and the use of structured and unstructured data. The section also evaluates knowledge of business use cases such as text, image, code, and video generation, along with the ability to identify when and how to select the right model for specific organizational needs.
Topic 3	Techniques to Improve Generative AI Model Output: This section of the exam measures the skills of AI Engineers and focuses on improving model reliability and performance. It introduces best practices to address common foundation model limitations such as bias, hallucinations, and data dependency, using methods like retrieval-augmented generation, prompt engineering, and human-in-the-loop systems. Candidates are also tested on different prompting techniques, grounding approaches, and the ability to configure model settings such as temperature and token count to optimize results.
Topic 4	Business Strategies for a Successful Generative AI Solution: This section of the exam measures the skills of Cloud Architects and evaluates the ability to design, implement, and manage enterprise-level generative AI solutions. It covers the decision-making process for selecting the right solution, integrating AI into an organization, and measuring business impact. A strong emphasis is placed on secure AI practices, highlighting Google’s Secure AI Framework and cloud security tools, as well as the importance of responsible AI, including fairness, transparency, privacy, and accountability.

Reference: https://cloud.google.com/developers/generative-ai-leader

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Google Cloud Certified - Generative AI Leader Sample Questions:

Question #1

A customer support team uses a generative AI agent to answer product questions. The AI cannot answer questions about a new software version with features and troubleshooting documented in internal articles that weren't part of its training dat a. What cost-effective technique should they use to improve their output?

A. Basic prompting with provided examples
B. Retrieval augmented generation (RAG)
C. Role-based prompting
D. Fine-tuning the foundation model with the new documentation

Reveal Solution Discussion 0

Correct Answer: B 🗳️

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Question #2

A financial institution uses generative AI (gen AI) to approve and reject loan applications, but gives no reasons for rejection. Customers are starting to file complaints. The company needs to implement a solution to reduce the complaints. What should the company do?

A. Develop fairness assessments for the gen AI model.
B. Implement explainable gen AI policies.
C. Fine-tune the gen AI model.
D. Collect a larger and more diverse dataset for the gen AI model.

Reveal Solution Discussion 0

Correct Answer: B 🗳️

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Question #3

A support team is building a generative AI model to classify customer support tickets into different categories (e.g., Billing Issue, Technical Support, Feature Request). They have a dataset of past tickets with their correct categories and need to train the AI model for this task. What prompting technique should they use?

A. Few-shot prompting
B. Role prompting
C. Zero-shot prompting
D. One-shot prompting

Reveal Solution Discussion 0

Correct Answer: A 🗳️

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Question #4

A research team has collected a large dataset of sensor readings from various industrial machines. This dataset includes measurements like temperature, pressure, vibration levels, and electrical current, recorded at regular intervals. The team has not yet assigned any labels or categories to these readings and wants to identify potential anomalies, malfunctions, or natural groupings of machine behavior based on the sensor data alone. What type of machine learning should they use?