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Google Generative-AI-Leader Exam Syllabus Topics:

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

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Real Generative-AI-Leader Latest Practice & Generative-AI-Leader Free Questions - Generative-AI-Leader Tesking Vce

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Google Cloud Certified - Generative AI Leader Exam Sample Questions (Q87-Q92):

NEW QUESTION # 87
A user asks a generative AI model about the scientific accuracy of a popular science fiction movie. The model confidently states that humans can indeed travel faster than light, referencing specific but entirely fictional theories and providing made-up explanations of how this is achieved according to the movie's "established science." The model presents this information as factual, without indicating that it originates from a fictional work. What type of model limitation is this?

Answer: B


NEW QUESTION # 88
The office of the CISO wants to use generative AI (gen AI) to help automate tasks like summarizing case information, researching threats, and taking actions like creating detection rules. What agent should they use?

Answer: D

Explanation:
Given the tasks involve researching threats and creating detection rules, the most appropriate and specialized agent would be a Security agent. This type of agent would be pre-configured or easily adaptable to understand security-specific contexts, data, and actions within a CISO's domain.
________________________________________


NEW QUESTION # 89
An organization is deploying generative AI applications on Google Cloud. They want to understand the fundamental security measures integrated into Google ' s global network and hardware. What term specifically describes these inherent protective measures?

Answer: D

Explanation:
Secure-by-design infrastructure means that security protections are incorporated into Google Cloud's hardware, network, and software architecture from their initial design rather than being added only after deployment. Google's infrastructure applies layered controls such as custom hardware security, encryption, identity-based access, network protections, continuous monitoring, and a hardened global architecture. These measures protect workloads and data at multiple levels and provide a secure foundation for generative AI applications. Network protection describes only one part of this broader architecture. Operations management concerns administration and reliability processes, while security oversight refers generally to governance and supervision rather than inherent technical controls. Because the organization is asking for the term covering security measures fundamentally integrated into Google's global network and hardware, secure-by-design infrastructure is the precise and comprehensive answer.


NEW QUESTION # 90
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's adoption frameworks advise an executive-sponsored, top-down strategy when initiating generative AI programs. A top-down approach ensures executive alignment with tangible business objectives, formalizes risk and governance controls, allocates appropriate cloud infrastructure and data resources, and prioritizes use cases delivering high business value rather than disconnected experimentation.


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: D

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
......

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