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Google Generative-AI-Leader Exam Syllabus Topics:

SectionWeightObjectives
Techniques to improve gen AI model output20%- Describe prompt engineering techniques and their purpose.
  • 1. Chain of thought
  • 2. Zero-shot
  • 3. Few-shot
  • 4. One-shot
- Describe the process of fine-tuning gen AI models.
  • 1. Reinforcement learning from human feedback (RLHF)
  • 2. Supervised tuning
- Describe how grounding can be used to improve model output.
  • 1. Grounding with enterprise data
  • 2. Grounding with Google Search
Fundamentals of generative AI30%- Identify the core layers of the gen AI landscape and the business implications.
  • 1. Platforms
  • 2. Applications
  • 3. Infrastructure
  • 4. Agents
  • 5. Models
- Describe core generative AI (gen AI) concepts and use cases.
  • 1. Defining core gen AI concepts (e.g., artificial intelligence, natural language processing, machine learning, generative AI, foundation models, multimodal foundation models, diffusion models, prompt tuning, prompt engineering, large language models)
  • 2. Describing the machine learning approaches (e.g., supervised, unsupervised, reinforcement)
  • 3. Identifying how to choose the appropriate foundation model for a business use case (e.g., modality, context window, security, availability and reliability, cost)
  • 4. Identifying the stages of the machine learning lifecycle (e.g., data ingestion, data preparation, model training, model deployment, model management) and the Google Cloud tools for each stage
- Describe how various data types are used in gen AI and the business implications.
  • 1. Identifying the differences between structured and unstructured data, and identifying real world examples of each type
  • 2. Identifying the differences between labeled and unlabeled data
  • 3. Explaining the characteristics and importance of data quality and data accessibility in AI (e.g., completeness, consistency, relevance, availability, cost, format)
Business strategies for a successful gen AI solution15%- Describe Google's approach to responsible AI and its importance.
  • 1. Google's AI principles
  • 2. Responsible AI best practices
- Describe change management best practices and their importance.
  • 1. Enabling AI adoption
  • 2. Creating a culture of innovation
- Describe best practices for a successful gen AI project.
  • 1. Choosing the right model
  • 2. Building a business case
  • 3. Evaluating AI solutions
Google Cloud's generative AI offerings35%- Describe Google Cloud's gen AI product and service portfolio.
  • 1. Gemini for Google Cloud
  • 2. Model Garden
  • 3. Google Workspace
  • 4. Vertex AI Studio
  • 5. Vertex AI
- Identify the use cases and strengths of Google's foundation models.
  • 1. Gemma
  • 2. Veo
  • 3. Gemini
  • 4. Imagen

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Google Cloud Certified - Generative AI Leader Exam Sample Questions (Q103-Q108):

NEW QUESTION # 103
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 data. What cost-effective technique should they use to improve their output?

Answer: C

Explanation:
Retrieval-augmented generation connects the agent to an external knowledge source containing the current internal articles. When a customer submits a question, the system retrieves relevant passages and supplies them to the model as grounding context before the response is generated. This allows the agent to answer questions about the new software release without retraining the foundation model. It is cost-effective because the knowledge index can be refreshed whenever documentation changes, keeping responses current while reducing hallucinations. Role-based and basic prompting can influence behavior or response style but cannot supply facts that the model has never received. Fine-tuning may modify model behavior, but it is more expensive, slower to update, and generally less suitable for frequently changing factual documentation. RAG therefore provides the most practical and maintainable solution.


NEW QUESTION # 104
A creative agency named Northshore Images plans to fine tune an image generation model using Vertex AI, and it needs a single repository to hold about 32 TB of source pictures. The data science group requires extremely durable and elastically scalable storage for unstructured objects that will feed their Vertex AI training runs. Which Google Cloud service best fits storing large collections of object data such as image files?

Answer: B

Explanation:
The correct option is Cloud Storage because it is a highly durable and elastically scalable object store for unstructured data such as image files and it integrates seamlessly with Vertex AI training. It can comfortably hold a single repository of about 32 TB.
This service offers bucket level durability and availability with regional or multi regional placement for resilience. It provides simple object access using gs paths that Vertex AI training jobs can read directly. It also supports lifecycle management, versioning and granular access control which are all valuable when managing large image datasets for machine learning.


NEW QUESTION # 105
What is a primary benefit of using a multi-agent system?

Answer: A

Explanation:
Multi-agent systems are designed to tackle complex problems by breaking them down into sub-tasks, where each agent specializes in a specific function. These agents then coordinate and collaborate to achieve a larger, more intricate goal that a single, monolithic AI model might struggle with.
________________________________________


NEW QUESTION # 106
An organization wants granular control over who can use and see their generative AI models and related resources on Google Cloud. Which Google Cloud security offering is specifically for this purpose?

Answer: C

Explanation:
Identity and Access Management (IAM) is the fundamental Google Cloud service that allows you to define who has what access to which resources. It provides granular control over permissions for users, groups, and service accounts, including access to generative AI models and related data.
________________________________________


NEW QUESTION # 107
A research company needs to analyze several lengthy PDF documents containing financial reports and identify key performance indicators (KPIs) and their trends over the past year. They want a Google Cloud prebuilt generative AI tool that can process these documents and provide summarized insights directly from the source material with citations. What should the analyst do?

Answer: A

Explanation:
The requirements are for a prebuilt tool that is designed for:
Analyzing uploaded private documents (lengthy PDFs).
Providing summarized insights (extracting KPIs and trends). Offering citations (grounding the answers to the source material). NotebookLM (C) is the Google tool explicitly designed for this use case. It is a generative AI powered notebook/research assistant that allows users to upload source documents (including PDFs), then ask questions and generate summaries or insights that are grounded in and cited back to the source documents. This makes it an ideal prebuilt solution for an analyst who needs to process complex, lengthy financial reports and verify the data with citations.


NEW QUESTION # 108
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