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Google Generative-AI-Leader Exam Syllabus Topics:

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

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Google Cloud Certified - Generative AI Leader Exam Sample Questions (Q88-Q93):

NEW QUESTION # 88
A large enterprise company is experiencing challenges managing their model. They have many versions of their model, including the code, data, and parameters used to train the models. They need to find a solution to manage versions, track changes, and stay organized throughout their lifecycle. What Gemini Enterprise Agent Platform tool should the company use?

Answer: D

Explanation:
Model Registry provides a centralized repository for organizing and governing machine learning models and their versions throughout the model lifecycle. It allows teams to register models, retain version history, associate metadata, track lineage and deployment status, and consistently identify which model artifact is approved or operating in an environment. These capabilities directly address the company's need to manage numerous versions and remain organized. Model Monitoring observes deployed model behavior, including performance changes and data drift, but is not the primary version-management repository. Pipelines automate repeatable machine learning workflows such as training, evaluation, and deployment. Feature Store manages reusable machine learning features rather than complete model versions and their lifecycle metadata. Consequently, Model Registry is the appropriate tool for tracking, organizing, and controlling the company's evolving model assets.


NEW QUESTION # 89
An organization has successfully trained a ML model and is now in the model deployment stage of the ML lifecycle. They need to ensure security throughout the ML lifecycle. What is a key security practice that they should implement at this stage?

Answer: C

Explanation:
During the model deployment and serving stage, securing endpoints against unauthorized access, data exfiltration, model theft, and adversarial inference attacks is critical. Implementing strict identity and access controls (IAM), API authentication, private endpoints (VPC Service Controls), and network traffic monitoring ensures only authorized users and services can interact with the deployed model endpoint.


NEW QUESTION # 90
A company has a machine learning project that involves diverse data types like streaming data and structured databases. How does Google Cloud support data gathering for this project?

Answer: B

Explanation:
Google Cloud offers a comprehensive suite of services for data ingestion and storage. Pub/Sub is for streaming data, Cloud Storage for various file types (including unstructured), and Cloud SQL for relational structured databases. These are fundamental for gathering diverse data. Gemini is a model, BigQuery is for analysis, and Vertex AI is for ML platform, not primary data collection tools themselves.


NEW QUESTION # 91
A company is developing a generative AI application to analyze customer feedback collected through online surveys. Stakeholders are concerned about potential privacy risks associated with this data, as the feedback contains personally identifiable information (PII). They need to mitigate these risks before using the data to train the AI model. What action should the company prioritize?

Answer: B

Explanation:
The problem is the existence of Personally Identifiable Information (PII) within the customer feedback data, which introduces privacy risks for the development and training of the generative AI model. The goal is to mitigate these risks before using the data to train the AI model.
According to Google's Responsible AI and data handling best practices, when sensitive data like PII is present in a dataset intended for model training, the most critical step to prioritize is data minimization and privacy protection at the source. This is often achieved through anonymization or de-identification.
Applying data anonymization techniques (D) directly addresses the risk by removing or obscuring the sensitive data elements. This prevents the PII from being embedded into the model's parameters during training, thereby eliminating the risk of data leakage or privacy violations in the AI application's outputs. This is a crucial early step in the ML lifecycle for datasets containing sensitive information.
Option C, implementing access controls, is a necessary security measure but is a reactive control that protects the raw data; it does not remove the PII risk from the derived model itself. Option A is a long-term change to data collection but doesn't solve the problem for the existing data. Option B relates to bias and accuracy, not specifically PII risk mitigation.
(Reference: Google Cloud's Secure AI Framework (SAIF) and Responsible AI principles emphasize protecting sensitive data at all stages of the ML lifecycle, with de-identification being the primary method before training.)


NEW QUESTION # 92
A company is defining their generative AI strategy. They want to follow Google-recommended practices to increase their chances of success. Which strategy should they use?

Answer: D

Explanation:
Google Cloud often recommends a "top-down" approach for generative AI strategy. This means starting with clear business objectives and leadership alignment on how generative AI can solve critical business problems, rather than simply experimenting from the bottom up without a clear strategic direction.


NEW QUESTION # 93
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