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Google Generative-AI-Leader Exam Syllabus Topics:

Topic
Details

Topic 1

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 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

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 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.

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Google Cloud Certified - Generative AI Leader Exam Sample Questions (Q17-Q22):

NEW QUESTION # 17
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?

A. Unsupervised learning
B. Deep learning
C. Supervised learning
D. Reinforcement learning

Answer: A

Explanation:
Since the team has not yet assigned any labels or categories to the sensor readings and wants to identify "anomalies, malfunctions, or natural groupings" based on the data alone, this is a classic unsupervised learning problem. Unsupervised learning techniques like clustering or anomaly detection are used to find hidden patterns or structures in unlabeled data.

NEW QUESTION # 18
A social media platform uses a generative AI model to automatically generate summaries of user-submitted posts to provide quick overviews for other users. While the summaries are generally accurate for factual posts, the model occasionally misinterprets sarcasm, satire, or nuanced opinions, leading to summaries that misrepresent the original intent and potentially cause misunderstandings or offense among users. What should the platform do to overcome this limitation of the AI-generated summaries?

A. Decrease the output length of the summaries to make them more concise.
B. Incorporate a human-in-the-loop (HITL) review process to refine the summaries.
C. Implement stricter safety settings to filter out potentially misinterpreted content altogether.
D. Increase the temperature parameter of the model to encourage more varied and less literal interpretations.

Answer: B

Explanation:
When AI struggles with nuances like sarcasm or satire, human oversight is often the most effective solution.
A human-in-the-loop (HITL) process allows human reviewers to check, correct, and refine AI-generated content before it is published, ensuring accuracy and appropriateness, especially for sensitive or complex language.
________________________________________

NEW QUESTION # 19
An organization wants to use generative AI to create a marketing campaign. They need to ensure that the AI model generates text that is appropriate for the target audience. What should the organization do?

A. Use prompt chaining.
B. Use few-shot prompting.
C. Use role prompting.
D. Adjust the temperature parameter.

Answer: C

Explanation:
Role prompting is a technique where you instruct the generative AI model to "act as" a specific persona or character. By assigning the model a role (e.g., "Act as a marketing expert writing for a young, tech-savvy audience"), you can guide its tone, style, and content to be appropriate for the target audience of the marketing campaign.

NEW QUESTION # 20
A pharmaceutical company's research and development department spends significant time manually reviewing new scientific papers to identify potential drug targets. They need a solution that can answer questions about these documents and provide summarized insights to researchers without requiring extensive coding expertise. What should the organization do?

A. Use Vertex AI AutoML to train a model that classifies papers into predefined research areas.
B. Use Vertex AI Search to index the papers and enable keyword-based searches.
C. Use Vertex AI Agent Builder to create a custom AI agent.
D. Use Gemini for Google Workspace to facilitate collaborative document review.

Answer: C

Explanation:
The requirement is to answer questions about the documents and provide summarized insights without requiring extensive coding expertise. Vertex AI Agent Builder is designed precisely for creating custom AI agents, often with low-code or no-code capabilities, that can interact with and process large volumes of information like scientific papers. While Vertex AI Search could index papers for keyword searches, it doesn't directly answer questions or provide summarized insights in the same way a generative AI agent built with Agent Builder could. Gemini for Google Workspace is for collaborative work, not specifically for building custom AI agents for document analysis. Vertex AI AutoML is for training classification models, which is different from answering questions and summarizing.

NEW QUESTION # 21
A software developer needs a highly efficient, open-source large language model that can be fine-tuned on a local machine for rapid prototyping of a chatbot application. They require a model that offers strong performance in natural language understanding and generation, while being lightweight enough to run on limited hardware. Which Google-developed family of models should they use?

A. Gemini
B. Veo
C. Imagen
D. Gemma

Answer: D

Explanation:
Gemma is Google's family of lightweight, state-of-the-art open models, built from the same research and technology used to create the Gemini3 models. They are designed for developers to build innovative AI applications on their local machines or in the cloud, offering a balance of performance and efficiency suitable for limited hardware and rapid prototyping. Veo is for video generation, Gemini is typically larger and more general-purpose, and Imagen is for image generation.

NEW QUESTION # 22
......

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