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Google Generative-AI-Leader Exam Syllabus Topics:

Section	Weight	Objectives
Techniques to improve gen AI model output	20%	- Describe prompt engineering techniques and their purpose. 1. One-shot 2. Chain of thought 3. Zero-shot 4. Few-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
Business strategies for a successful gen AI solution	15%	- 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. Building a business case 2. Evaluating AI solutions 3. Choosing the right model - Describe change management best practices and their importance. 1. Enabling AI adoption 2. Creating a culture of innovation
Fundamentals of generative AI	30%	- Identify the core layers of the gen AI landscape and the business implications. 1. Agents 2. Models 3. Infrastructure 4. Platforms 5. Applications - Describe core generative AI (gen AI) concepts and use cases. 1. 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 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. Describing the machine learning approaches (e.g., supervised, unsupervised, reinforcement) 4. Identifying how to choose the appropriate foundation model for a business use case (e.g., modality, context window, security, availability and reliability, cost) - 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
Google Cloud's generative AI offerings	35%	- Identify the use cases and strengths of Google's foundation models. 1. Imagen 2. Gemini 3. Gemma 4. Veo - Describe Google Cloud's gen AI product and service portfolio. 1. Model Garden 2. Vertex AI 3. Vertex AI Studio 4. Google Workspace 5. Gemini for Google Cloud

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Google Cloud Certified - Generative AI Leader Exam Sample Questions (Q23-Q28):

NEW QUESTION # 23
What is the definition of prompt engineering?

A. Providing an AI model with a direct instruction or question without any additional context or examples.
B. The art and science of designing instructions to guide AI models to generate the desired responses.
C. A branch of artificial intelligence that uses machine learning to process and interpret text and data
D. The ability to connect model output to verifiable sources.

Answer: B

Explanation:
Prompt engineering is the systematic practice of designing and refining instructions, context, examples, constraints, and output requirements so that a generative AI model produces a desired response. It can include assigning a role, specifying the task, supplying relevant background, defining a response format, and using zero-shot, one-shot, or few-shot examples. Option A more broadly describes natural language processing rather than prompt engineering. Option C describes grounding, which connects generated output to trusted and verifiable information. Option D describes a zero-shot prompt, only one possible prompting technique, and therefore is too narrow to serve as the general definition. Effective prompt engineering normally involves iterative testing and refinement to improve the relevance, consistency, accuracy, and usefulness of model responses. Thus, option B provides the complete definition.

NEW QUESTION # 24
A development team is configuring a generative AI model for a customer-facing application and wants to ensure the generated content is appropriate and harmless. What is the primary function of the safety settings parameter in a generative AI model?

A. To limit the maximum text length that the model generates by ensuring concise responses.
B. To determine the number of tokens the model can process at once by influencing the complexity and length of inputs and outputs.
C. To control the creativity and randomness of the model's output by adjusting the diversity of word choices.
D. To filter out potentially harmful or inappropriate content from the model's output based on the desired level of filtering.

Answer: D

Explanation:
Safety settings in generative AI models are specifically designed to prevent the generation of content that could be harmful, offensive, or inappropriate. This includes filtering for categories like hate speech, sexually explicit content, self-harm, and violence, based on predefined thresholds. Options A, B, and D refer to other parameters like max_output_tokens or temperature, which control output length, input/output processing, and creativity, respectively, not safety.

NEW QUESTION # 25
A sales manager wants to responsibly use generative AI (gen AI) to increase efficiency with their existing tasks. They want to allow the sales team to focus on building customer relationships and closing deals. How should the sales team use gen AI?

A. To analyze customer interactions on social media and automatically generate sales pitches tailored to their public profiles.
B. To draft emails and provide real-time insights about customer needs.
C. To replace the sales team's CRM system with a more intuitive and user-friendly interface.
D. To automate creative content like blog posts and social media updates to attract new leads.

Answer: B

Explanation:
The strategic goal is to boost sales efficiency by shifting the team's focus to high-value activities (relationships and closing deals) by automating repetitive administrative tasks.
Option C directly addresses this goal by leveraging Gen AI's core capabilities for text generation and summarization/analysis:
Drafting emails automates a major time sink for sales reps (a common, repetitive task).
Providing real-time insights automates the labor-intensive research and manual data analysis required to understand customer needs, giving the rep instant, actionable context.
Options A and D are less direct solutions for improving sales efficiency: Option A is an expensive, high-risk platform replacement, not an efficiency use case. Option D describes marketing tasks, which, while related, are not the primary, day-to-day tasks that sales reps perform to clear their schedules for relationship building. Therefore, Gen AI's most effective role in sales is as a productivity assistant for drafting and quick research.
(Reference: Google Cloud documentation on sales enablement use cases emphasizes that Gen AI's role is to automate administrative and time-consuming tasks like drafting outreach messages and synthesizing customer information to enhance seller productivity, allowing them to focus on revenue-generating activities.)

NEW QUESTION # 26
A team is using a generative AI model to automatically generate short summaries of customer feedback. They need to ensure that these summaries are concise and easy to digest. What model setting should they adjust?

A. Safety settings
B. Temperature
C. Output length
D. Top-p (nucleus sampling)

Answer: C

Explanation:
The objective is to make the generated summaries concise--that is, to control their length. In the configuration of a generative AI model, particularly a large language model (LLM), the parameter used to directly control the maximum size of the response is the Output Length parameter (often referred to as max_output_tokens or max_tokens). By setting a low limit on this parameter, the team can ensure that the model is forced to terminate its response once that limit is reached, resulting in a shorter, more concise summary that is "easy to digest," as requested.

NEW QUESTION # 27
A security team needs a centralized platform to gain a comprehensive overview of their organization's security health across their entire Google Cloud environment, including potential threats to their generative AI deployments. Which Google Cloud security offering is specifically for this purpose?

A. Secure-by-design infrastructure
B. Workload monitoring tools
C. Security Command Center
D. Identity and Access Management

Answer: C

Explanation:
Security Command Center is Google Cloud's comprehensive security management and data risk platform. It provides centralized visibility into security posture, identifies vulnerabilities, detects threats, and helps manage compliance across the entire Google Cloud environment, including services and deployments like generative AI.

NEW QUESTION # 28
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